Study and Measurement of Scatter Effect on Wireless
Transcription
Study and Measurement of Scatter Effect on Wireless
EXAMENSARBETE I TRÅDLÖSA SYSTEM 120 HP, AVANCERAD NIVÅ STOCKHOLM, SVERIGE 2015 Study and Measurement of Scatter Effect on Wireless Channel using Realtime LTE Co-Operative Relays UMER ZEESHAN ASHRAF KTH KUNGLIGA TEKNISKA HÖGSKOLAN SKOLAN FÖR INFORMATIONS- OCH KOMMUNIKATIONSTEKNIK Abstract Relays are an important application of the fixed wireless access channel (FWA), with fast growing needs for higher throughput cooperative schemes are standardized to enhance throughput and cooperative relays are a realization of the standard. We study the effects of moving scatters on the FWA channel with simulation and real-time LTE measurements with testbed and characterize the FWA MIMO channel based on its performance and small scale movements in the environment, compare the LOS and NLOS effects of channel in relation to scatter movements and antenna height. The measurement and simulation results show that there is significant effect of scatters on the characteristics of wireless channel. The doppler spectrum bandwidth increases in relation to the both speed and number of moving scatters. On performance metric MIMO capacity is studied and shown that capacity increases with antenna height in NLOS condition and presence of scatters improve the channel capacity in LOS condition. i Dedication All the praise and thanks be for the lord Almighty. I would like to dedicate this work to the excellent teachers who delivered the knowledge. Thanks to my loving wife without whom this would not have been possible. Thanks to my parents for their eternal love and prayers, my sister and friends for supporting me. ii Acknowledgements I would like to thanks to prof. Ben for accepting my work, for the continuous feedback and suggestions, Michael for being a mentor and a great person, my guide and Thesis advisor, without him this would not have been possible. Vodafone chair for giving me the opportunity to do a research study, it was a very important step in my life. iii Table of Contents List of Acronyms v List of Tables vi List of Figures vii 1 Introduction 1.1 The Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 2 Wireless channel 2.1 Background . . . . . . . . . . . . . . . . . . . . . 2.1.1 Multi-path Fading Channels . . . . . . . . 2.1.2 Time variance of the channel . . . . . . . . 2.2 State of the art . . . . . . . . . . . . . . . . . . . 2.2.1 Ricean K-Factor . . . . . . . . . . . . . . 2.2.2 K-Factor Estimation . . . . . . . . . . . . 2.2.3 Doppler Spectrum model for FWA channel . . . . . . . 3 3 3 5 7 7 8 8 . . . . . . . 10 10 12 15 16 16 17 18 4 Simulation 4.1 OFDM transceiver chain . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Doppler spectrum fixed wireless channel . . . . . . . . . . . . . . . . 20 20 24 5 Measurements and Field trials 5.1 Test-bed equipment . . . . . . 5.2 Indoor measurements . . . . . 5.3 Outdoor measurements . . . . 5.3.1 Evaluation chain . . . 28 28 28 35 36 3 Methodology 3.1 Channel model . . . . . . . . . . . . . . . . . . 3.1.1 OFDM channel estimation . . . . . . . . 3.2 Performance . . . . . . . . . . . . . . . . . . . . 3.2.1 Signal and noise power estimation . . . . 3.2.2 Subcarrier power variance . . . . . . . . 3.2.3 Capacity . . . . . . . . . . . . . . . . . . 3.3 Effect of Imperfect CSI on channel performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv 6 Results 6.1 Indoor measurements . . 6.1.1 Sub-carrier power 6.2 Outdoor measurements . 6.2.1 Sub-carrier power 6.3 Capacity . . . . . . . . . . . . . . 40 40 41 41 41 41 7 Conclusions 7.1 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 46 46 . . . . . variance . . . . . variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A SORBAS test equipment A.1 AVAILABLE SORBAS TEST UE OPTIONS . . . . . . . . . A.2 DUPLEX MODES . . . . . . . . . . . . . . . . . . . . . . . . A.3 RADIO FREQUENCY . . . . . . . . . . . . . . . . . . . . . . A.4 PHYSICAL LAYER FEATURES . . . . . . . . . . . . . . . . A.4.1 Downlink Characteristics . . . . . . . . . . . . . . . . . A.4.2 Uplink Characteristics . . . . . . . . . . . . . . . . . . A.4.3 PhysicalSignals and Channels . . . . . . . . . . . . . . A.5 LAYER 2 FEATURES . . . . . . . . . . . . . . . . . . . . . . A.5.1 The UE MAC and RLC implementation according to Release 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3GPP . . . . 1 1 1 1 1 1 1 2 2 2 v List of Acronyms LTE Long term evolution COMP Co-ordinated multipoint MIMO Multiple Input Multiple Output CSI Channel state information CoR Cooperative relay UE User equipment AWGN Additive white Gaussian noise NLOS Non line-of-sight PDF Probability density function WSS Wide sense stationary ACF Autocorrelation function CTF Channel transfer function CIR Channel impulse response BER Bit error rate SNR Signal to noise ratio KPI key parameter indications RSRP Reference signal received power RSRQ Reference signal received quality vi List of Tables 2.1 Coherence time Tc [sec] . . . . . . . . . . . . . . . . . . . . . . . . . . 5 4.1 4.2 OFDM Simulation parameters . . . . . . . . . . . . . . . . . . . . . . Scatter simulation parameters . . . . . . . . . . . . . . . . . . . . . . 20 25 5.1 5.2 Indoor Measurement parameters . . . . . . . . . . . . . . . . . . . . . Outdoor Measurement parameters . . . . . . . . . . . . . . . . . . . . 32 36 vii List of Figures 1.1 Cooperative relay scenario . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1 2.2 2.3 2.4 Multipath fading . . . . . . . . . . . Jakes Spectrum and its assumption. . K-factor measurement results. . . . . Doppler spectrum for FWA channel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 6 8 9 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 OFDM transmitter . . . . . . . . . . OFDM receiver . . . . . . . . . . . . Reference symbols grid for 1 antenna Parallel narrowband subchannels . . OFDM channel estimation structure CTF and CIR . . . . . . . . . . . . . Channel Capacity . . . . . . . . . . . Antipodal signaling constellation . . Performance with Imperfect CSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 11 12 13 13 15 17 19 19 4.1 4.2 4.3 4.4 4.5 4.6 4.7 OFDM baseband transmitter . . Baseband signal through channel OFDM baseband receiver . . . . . ML detector . . . . . . . . . . . . Symbol error rate curves . . . . . Scatter simulation model . . . . . Doppler spectrum analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 21 22 22 23 26 27 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 SORBAS test equipment . . . . . . . . . . Indoor measurement environment . . . . . Lab dimensions and movement description Indoor Static environment . . . . . . . . . Indoor 1 person moving environment . . . Indoor 2 persons moving environment . . . Indoor 4 persons moving environment . . . Test-bed map . . . . . . . . . . . . . . . . Measurement location and Van . . . . . . Measurement location and Van . . . . . . Post dump analysis chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 30 31 33 33 34 34 35 37 38 39 6.1 6.2 6.3 Indoor environment SC power variance . . . . . . . . . . . . . . . . . Outdoor environment SC power variance . . . . . . . . . . . . . . . . Measured MIMO capacity . . . . . . . . . . . . . . . . . . . . . . . . 43 44 45 . . . . . . . . . . . . . . 1 Chapter 1 Introduction Cellular networks are widely used means of communication and hold importance in business and daily life matters. The use of mobile phones has been increased exponentially in the last decade because of advances in wireless networks and affordable packages for the end user. A recent survey reveals the number of mobile phone users worldwide has reached to 5.6 billion[1]. The demand for higher rates in cellular networks is an ever increasing need. There has been an exponential rise in the cellular networks traffic mainly because of the multimedia, streaming applications and high speed internet access. We have reached up to 1 Gbps of peak data rate in recent Long term evolution(LTE) advanced standards and still the need grows bigger. A Global mobile traffic forecast done by Cisco predicts an exponential increase from 2011 to 2016[2]. New technologies are emerging with efficient bandwidth utilization, scheduling and multiple access schemes. The main challenge in this ever increasing demand for higher traffic requirements is the very nature of wireless channel. Wireless channel changes to a significant extent with small changes in position and random for a mobile user. Another challenge is the limited bandwidth, which is very expensive and must be used efficiently and reused as frequently as possible. The wireless networks are becoming denser and denser and the systems are becoming interference limited. Coordinated multipoint(COMP) is a technology which offers solution to the interference problem with cooperation between neighbouring base stations. There is a back-haul link on which channel state information (CSI) and scheduling information is shared between the base stations. With help of a pre-coder which uses channel information to effectively neutralize the channel attenuation in advance and joint processing (JP) which enables the multiplexed signals to be processed jointly and as a result the system becomes a multi input multi output(MIMO) system with transmitter antennas spatially distributed. This offers a likelihood that MIMO channels would be uncorrelated and independent with a good overall MIMO capacity. An important application of COMP is that it can be used for data rate enhancement at cell edge because the user at the ell edge usually faces interference and has a bad channel condition. The CSI feedback and JP poses a delay in case of a mobile user is a problem because the channel information becomes outdated and the pre-coder can not function. Another challenge is optimum clustering and user grouping of mobile users. The scenario in which receiver is fixed and a useful application is a relay case, the situation is much simpler. Figure 1.1 describes the scenario. In case of a relay we face a fixed wireless access(FWA) channel which offers an interesting opportunity for our use case as it is expected not to vary too much with time. The only causes of variation in the CSI would be the movement in environment. The focus of this thesis is to study these variations in environment and their effect on channel. Another prospect is the issue 2 regarding deployment of the relays. The height and location are important parameters in this regard. An analysis of the Cooperative relay(CoR) channel based on height is performed in an outdoor scenario along the road with cars passing by as moving scatterers. Figure 1.1: Cooperative relay scenario 1.1 The Problem Statement For this thesis work we study three main issues regarding cooperative relays 1. How the scatterers in environment effect channel stability of FWA channel 2. How the channel behaves with deterministic movements in environment 3. What is the performance of the MIMO CoR channel with increasing height at different locations 3 Chapter 2 Wireless channel 2.1 2.1.1 Background Multi-path Fading Channels The physical medium through which the information signal travels, from transmitter to the receiver is called communication channel. For instance, telephone line in wired system and environment between the transmitter and the receiver in wireless system is called as communication channel. Wireless channels are highly unreliable in nature, both in time and frequency domain. The distortion experienced by the received signal after propagating through wireless channel is called as fading. In multi-path propagation, the transmitted signal can take several paths to reach the receiver. The signals reaching through different paths experience different delays. The superposition of these signals at the receiver can be constructive or destructive depending upon the delays introduced by each path. This causes variation in received signal strength, frequency and phase at the receiver and thus give rise to multi-path fading. Figure 1.2 describes a multipath fading wireless channel. The multi-path fading can be further divided into two types, i.e. large scale fading and small scale fading. Figure 2.1: Multipath fading Large scale fading represents the variation in the mean of received signal power over the large distance as compare to the wavelength of the signal. It occurs when receiver is shadowed by large terrain obstacles such as hills or buildings. Large scale fading is often expressed in terms of mean path loss and log-normal distributed variation about the mean for a given distance between transmitter and the receiver [3]. Log-normal distribution means that the logarithm of signal power follows norm distribution. Small scale fading includes the variation in received signal power over small distances, comparable or less than wavelength of the signal. This occurs due to scatters present in the communication channel. These scatters are created due to reflection, 4 diffraction and scattering of radio waves from different objects and surfaces located between the transmitter and the receiver. The received signal is the summation of all these scattered signals. This causes fluctuations in the received signal strength. Let us assume that the bandpass signal s(t)is transmitted during an interval 0 ≤ t ≤ T . It can be expressed in terms of its baseband waveform s l (t) as follows <{sl (t)ej2πfc t } (2.1) Where <{.} denotes the real part of {.} and fc is the carrier frequency. While travelling through multi-path fading environments s(t) will be modified by a complex factor α(t) that takes care of both large and small scale fading. Assuming that there are i multiple scattering paths, each having time variant propagation delay τi (t) and a time variant multiplicative factor αi , the received bandpass signal can be written as follows according to [4] X y(t) = αi s[t − τi (t)] + n(t) (2.2) i " y(t) = < # X |αi (t)|ejθi (t) sl (t − τi (t)) ej2πfc t ! + n(t) (2.3) i Where |αi (t)| is the time varying amplitude and θi (t) is the time varying phase of the complex gain factor αi , and n(t) at any instant t denotes additive white Gaussian noise (AWGN) at the receiver with zero mean and known variance. Main source of AWGN is thermal noise causing excitation in electrons or because of interference collected by the antenna. As n(t) is the sum of large number of statistically independent random variables hence according to the central limit theorem [3], it can be modelled as Gaussian random variable. Assuming perfect carrier recovery and timing synchronization, the baseband received signal can be modelled as X yl (t) = |αi (t)|ejθi (t) sl (t − τi (t)) + n(t) (2.4) i Since y l (t) is the response of an equivalent baseband channel to an equivalent baseband signal s l (t), the expression for time variant baseband channel impulse response h(τ ;t) can be obtained as follows X h(τ ; t) = αi (t)δ(t − τi (t)) (2.5) i Where αi (t) is the complex attenuation factor of path i. In most urban scenarios the scattering environment is rich and there are many scattering paths and the case in which none is more dominant than the other i.e Non line-of-sight (NLOS) scenario, then according to central limit theorem h(τ ; t) can be modelled as zero mean complex valued Gaussian random process and the received envelope |h(τ ; t)| at any 5 instant t has Rayleigh probability density function (pdf). Therefore the channel is modelled as Rayleigh fading channel in this case. In the presence of fixed scatterers or one dominant path i.e line-of-sight(LOS) scenario, in addition to moving scatterers the h(τ ; t) cannot be modelled as having zero mean and hence envelop envelope |h(τ ; t)| can be modelled as random process with Ricean distributed pdf, hence the fading caused in LOS scenario will be Ricean fading [5]. 2.1.2 Time variance of the channel Multi-path channel becomes time variant due to the relative motion between the transmitter and the receiver. This relative motion causes the frequency of the received signal to be shifted relative to that of the transmitted signal. This Doppler shift f d , is proportional to the velocity of the receiver and the frequency of the transmitted signal, and the maximum doppler shift is formulated as follows [5]. Vmax fc (2.6) c Where Vmax is the maximum velocity of the mobile and c is the speed of light. Since the received signal travels over several reflected paths and each path have different doppler shift hence multi-path propagation creates spectral or Doppler spreading. A characterization of time variant channel in time domain is coherence time Tc . It is the measure of the expected time duration over which the channel response remains essentially invariant [3]. The coherence time could also be defined in terms of 90 percent or 50 percent correlation time. Time variation of the channel results into slow or fast fading on the channel. For instance if the coherence time of the channel is less than the symbol transmission time Ts , the channel is known as fast fading channel. However, if coherence time is larger than Ts , the channel is known to be slowly fading channel. A study of coherence time estimation done in [17] for the fixed wireless channel with parameters NLOS, LOS, Street view (SV) and no street view (NSV) reveals the time stability of fixed wireless channel. Following are the results fd = Table 2.1: Coherence time Tc [sec] LOS/NSV 3.9 LOS/SV 1.2 NLOS/NSV 3.8 NLOS/SV 1.1 Power spectral density (PSD) is an important parameter which shows the spread of signal power in frequency domain. It is obtained according to definition by taking the Fourier transform of the autocorrelation function of the received signal relative to the delay parameter τ . The autocorrelation function (ACF) is defined as follows Ar (τ1 , τ2 ; t, ∆t) = E[r∗ (τ1 ; t)r(τ2 ; t + ∆t)] (2.7) 6 Where r(τ1 , τ2 , t; t + ∆t) are the observations of the signal r (t) for durations τ1 and τ2 at time instants t and t + ∆t. An important assumption here is that the received signal r (t) passes through a channel which is wide sense stationary (WSS). Most channels in practice are WSS, such that the joint statistics of a channel measured at two different times depends only on the time difference [15]. The PSD is obtained by taking Fourier transform of ACF. Sr (f ) = F {Ar (τ )} (2.8) Where F{.} denotes Fourier transform. The power spectrum according to Jakes [15] is the usual spectrum that is observed for a mobile, with a plausible assumption used by Jakes that the angle of arrival of multi-paths are uniformly distributed. Jakes spectrum with a maximum doppler spread fd is shown in Figure 2.2. (a) (b) Figure 2.2: Jakes Spectrum and its assumption. 7 2.2 State of the art The complex path gain of any radio channel can quite generally be represented as having a fixed component plus a fluctuating (or scatter) component. The former might be due to a line-of-sight path between the transmitter and the receiver; the latter is usually due to echoes from multiple local scatterers, which causes variations in space and frequency of the summed multipath rays. The spatial variation is translated into a time variation when either end of the link is in motion. In the case of fixed wireless channels, time variation is a result of scatterers in motion. 2.2.1 Ricean K-Factor If the scatter component has a complex Gaussian distribution, as it does in the central limit (many echoes of comparable strength), the time-varying magnitude of the complex gain will have a Ricean distribution. The key parameter of this distribution is the Ricean K-Factor (or just K ), which is the power ratio of the fixed and scatter components [6]→[8]. It is a measure of the severity of fading. The case K =0 (no fixed component) corresponds to the most severe fading, and in this limiting case, the gain magnitude is said to be Rayleigh distributed. From the earliest days, most analyses of mobile cellular systems, e.g. [9], have assumed Rayleigh fading. What alters complex path gain is the slow motion of scatterers along the path, e.g., pedestrians, vehicles, and wind-blown leaves and foliage. As a result, the path gain at any given frequency will exhibit slow temporal variations as the relative phases of arriving echoes change. Because these variations are often slight, the condition of a dominant fixed component plus a smaller fluctuating component takes on a higher probability than that for mobile links. Furthermore, since temporal variations can occur on many scatter paths, the convergence of their sum to a complex Gaussian process is plausible. Therefore, we can expect a Ricean distribution for the gain magnitude, with a higher K-factor, in general, than that for mobile cellular channels. The complex path gain g(t) and K-factor model formulated according to [10] and [11] is as follows g(t) = V + v(t) (2.9) Where V is the fixed complex value and v (t) is the random time varying part because of the scattering environment effects with variance σ 2 . The K factor for Ricean fading is defined as the ratio of static power to the time varying power |V |2 (2.10) σ2 A comprehensive measurement campaign to study of K-factor dependence on height, wind, beam-width and seasons is done in [10] and [11]. K-factor is modelled as a random variable with log-normal distribution and a zero mean and variance depending upon the above mentioned factors. K= 8 Figure 2.3: K-factor measurement results. 2.2.2 K-Factor Estimation Various methods have been reported to estimate K using moments calculated from time series, e.g. [12]→[14]. The simple and widely used method of moments from [13] is as follows. Suppose R(t) be the envelop of the received signal, and that R(t) follows Ricean fading distribution i.e, the case in which we have a strong LOS component. According to [13] the factor γ is defined as follows γ= V [R2 ] (E[R2 ])2 (2.11) Where V{.} denotes the variance and E{.} denotes the mean of the time series. The K factor is derived as √ 1−γ √ K= (2.12) 1− 1−γ 2.2.3 Doppler Spectrum model for FWA channel In the classical Jakes Doppler spectrum, the receiver (or the transmitter) is assumed to move at a certain speed. However, in fixed wireless channel, both the transmitter and the receiver are static and time-variations are actually due to moving scatterers. A stochastic model for this sort of time-varying channels is introduced, which can be employed to more accurately simulate the performance of fixed wireless communications [16]. Interestingly the Doppler spectrum is different for the fixed channel case. It is peaky at centre and its shape depends on the maximum velocity of the scatterers and number of multi-path components which are non-stationary. Another interesting observation is that the bandwidth of Doppler spectrum is twice than that of Jakes 9 spectrum i.e it is 4fd . Figure 2.4 shows the Doppler spectrum and the parameter a is a measure of static multi-path scatterer components. Figure 2.4: Doppler spectrum for FWA channel. 10 Chapter 3 Methodology In order to investigate the proposed questions, two simultaneous approaches should be taken into account. On one hand we need to simulate the channel model and on the other hand we need to evaluate the measured data. In this chapter we describe the channel model used in the downlink measurements. 3.1 Channel model Orthogonal frequency division multiplexing (OFDM) channel model is used in the downlink measurements, as it is the multiple access scheme that is used in LTE downlink. The figure 3.1 shows the transmitter part of OFDM [15]. Figure 3.1: OFDM transmitter The input data stream is modulated by a QAM modulator, resulting in a complex symbol stream X[0],X[1],...,X[N -1]. This symbol stream is passed through a serial-to-parallel converter, whose output is a set of N parallel QAM symbols X[0],...,X[N -1] corresponding to the symbols transmitted over each of the subcarriers. Thus, the N symbols output from the serial-to-parallel converter are the discrete frequency components of the OFDM modulator output s(t) In order to generate s(t) , 11 these frequency components are converted into time samples by performing an inverse DFT on these N symbols, which is efficiently implemented using the IFFT algorithm. The IFFT yields the OFDM symbol consisting of the sequence x [n]= x [0],...,x [N -1] of length N , where N −1 1 X √ X[i] expj2πni/N , x[n] = N i=0 0≤n≤N −1 (3.1) This sequence corresponds to samples of the multicarrier signal i.e. the multicarrier signal consists of linearly-modulated subchannels, and the right hand side of Equation 3.1 corresponds to samples of a sum of QAM symbols X [i ] each modulated by an orthogonal carrier. The cyclic prefix is then added to the OFDM symbol, and the resulting time samples are ordered by the parallel-to-serial converter and passed through a D/A converter, resulting in the baseband OFDM signal x̃ (t). The transmitted signal is filtered by the channel impulse response h(t) and corrupted by additive noise, so that the received signal is y(t) = x̃(t) ∗ h(t) + n(t). This signal is down converted to baseband and filtered to remove the high frequency components. Figure 3.2: OFDM receiver The A/D converter samples the resulting signal to obtain y[n] = x̃[n] ∗ h[n] + v [n]. The cyclic prefix from y[n] is removed as the first µ samples. The result is a sequence y[n] of length N samples whose DFT without noise would be Y [i] = H[i]X[i] . These time samples are serial-to-parallel converted and FFT is performed. This 12 results in scaled versions of the original symbols H[i]X[i] , where H[i] = H(fi ) is the flat-fading channel gain associated with the ith sub-channel. The FFT output is parallel-to-serial converted and passed through a QAM demodulator to recover the original data. The beauty of OFDM is that the wideband channel is decomposed into a set of narrowband orthogonal sub-channels each having a different QAM symbol. The equalization can be done in frequency domain with knowledge of each subchannel gain X[i] = Y [i]/H[i]. A draw back for frequency domain equalization is the noise enhancement as noise in each subchannel is enhanced by 1/H[i] factor. 3.1.1 OFDM channel estimation Channel estimation is done with the help of pilot or reference symbols which are placed according to LTE standard in the time-frequency grid. Pilots are positioned according to the used number of MIMO antennas. The equation for independent Figure 3.3: Reference symbols grid for 1 antenna narrowband subchannels can be written as [17]. yk = hk xk + nk , k = 0.....N − 1 (3.2) where hk is the complex channel attenuation given by h = [h0 h1 ...hN −1 ]T = DFT N (g) and n = [n0 n1 ...nN −1 ]T . We can write in matrix notation as y = XFg + n (3.3) 13 Figure 3.4: Parallel narrowband subchannels Figure 3.5: OFDM channel estimation structure 14 where X is the matrix with elements of x on its diagonals and (the DFT matrix comes after that which is giving some error yet) F= is the DFT-matrix with 1 (3.4) WNnk = √ e−j2πnk/N N The lease square estimate [17] for the impulse response g minimizes (y H XFg) (y - XFg) and generates ĥLS = FQLS FH XH y (3.5) QLS = (FH XH XF)−1 (3.6) where the equation 3.5 is reduced to ĥLS = X−1 y (3.7) The least square estimator is also referred to as zero forcing estimator and has high mean square error. In case of a fixed wireless channel scenario the least squares estimate should be sufficient as there is not much variation in the channel state. The vector x in simple terms consists of pilot sub-carriers spread over the band, and y are the same pilot sub-carriers after passing through the channel. ĥLS would then represent the channel transfer function (CTF) in frequency domain. In order to find the channel impulse response (CIR) we need to take IFFT of the least squares channel estimate [19] that is ĥLS . CIR = IFFT(ĥLS ) (3.8) For instance figure 3.6 shows the CTF and CIR from a measurement. 15 Channel transfer function 60 55 50 45 0 20 40 60 80 100 120 Pilot Subcarriers over 20MHz band 1:200 140 160 180 200 Channel impulse response Absolute value CIR in dB 60 50 40 30 20 10 0 0 0.2 0.4 0.6 0.8 1 Channel response times [uSec] 1.2 1.4 1.6 1.8 Figure 3.6: CTF and CIR 3.2 Performance Performance is always the key to qualify a wireless channel and its characterization. How well wireless system is performing depends on some KPI (key parameter indications). We can judge the performance of a wireless system with many KPIs. Some of most commonly used KPIs to characterize wireless system performance are capacity, RSRP(Reference signal received power), RSRQ(Reference signal received quality), SNR (Signal to noise ratio), doppler spread, error probability, throughput, coverage, maximum number of users, cell edge rates etc. The important KPI for us in this study will be SNR, capacity and doppler spread. We will also look at the variance of individual subcarriers over the band which is normally not a well known KPI but in our case of channel characterization based on the scatters and small scale movements in channel with different scatter size serves our purpose and reveals useful information about the channel. 16 3.2.1 Signal and noise power estimation The calculation of Noise and Signal power is necessary to estimate the SNR at the receiver, which is important metric to analyse the performance. It is used in the calculation of capacity, also it is a measure of how good is the channel condition. We know from our signal processing basics the definition of power PX = E[kXk2 ] (3.9) In our case for the OFDM system the Noise power is calculated by measuring the power on unallocated subcarrier. By an unallocated subcarrier I mean it does not contain a pilot symbol neither it is allocated for any user data. We know from our allocation scheme the indices which have no allocation. Let k be a subcarrier which is used as a pilot and n be a subcarrier which has no allocation. Then The noise power would simply be power received on subcarrier n as there is no contribution from any other source on this subcarrier than noise. Let Pn denote the noise power and Pk denote the received power on pilot subcarrier, then we can calculate the signal power Ps easily using Ps = Pk − P n (3.10) And the SNR ρ in dB could be calculated using ρ = 10log( 3.2.2 Ps ) Pn (3.11) Subcarrier power variance An interesting parameter to study small scale movements in environments is subcarrier power variance. For 20MHz bandwidth there are 200 pilot subcarriers, so we will measure the variance of the power on each subcarrier over measurement time to see which carriers are affected more than others. If we can specify a bandwidth most affected by small scale movements in the environment. For s = 1,2,...200 Ps is the received signal power per subcarrier from which we have already subtracted the estimated noise power, refer to 3.10, let us denote T as the measurement duration in TTI (ms). The Variance power per pilot subcarrier in dB over time will be var[Ps ] = 10log[ T X (Ps − mean(Ps )2 )] (3.12) We will use 3.12 in our channel evaluation for the indoor and outdoor measurements. 17 3.2.3 Capacity By definition of information theory capacity of wireless channel is the upper bound on rate at which information can be reliably transmitted over the wireless channel. Similarly we interpret capacity provides the limiting rate of the amount of information that can be transferred though the channel with a negligible amount of error probability. Shannon the father of information theory had provided a mathematical model of the capacity which could be used to calculate the capacity of a given channel. It is common to represent the channel capacity within a unit band-with of the channel. The channel capacity is then measured in bits/s/Hz. Different kinds of transmission schemes could be possible when we want to transmit information, and it is desirable to use the one which maximizes the capacity. If we nominate input of and output of a memoryless channel respectively X and Y random variables. Figure 3.7: Channel Capacity The channel capacity is mathematically defined as using definition from [23] C = maxp(x) I (X;Y) (3.13) where I (X;Y) represents the mutual information between X and Y. The equation 3.13 means that mutual information I has to be maximum over all the possible statistical distributions of X and Y. We are aware about the definitions of the mutual information and entropy so they need not be explained here. Here is the general and mathematical concept about channel capacity. A useful deduction of the generalized capacity model could be derived for additive white Gaussian noise. in our performance calculation we will use the deduction of MIMO channel capacity which is given by [24] ρ HH∗ ] (3.14) N where M are the number of RX antennas and N are number of TX antennas, H is the complex MIMO channel matrix of size M x N, IM is the identity matrix of size M and ρ is the average SNR on each RX antenna. From this generalized MIMO capacity equation 3.14 we can find out the SISO capacity by setting M = N = 1, C = log2 det[IM + C = log2 [1 + ρ||h||2 ] (3.15) where h is the complex channel gain between the single existing TX-RX path. 18 3.3 Effect of Imperfect CSI on channel performance Let us assume a valid scenario when the FWA channel CSI which is assumed to be known at the transmitter end has changed and now we model the effect of this delta in the channel as . The least square equalizer which is a zero forcing equalizer will force the known channel h so a residue h will remain with the equalized signal. y= h− x+n h (3.16) y = [1 − ]x + n (3.17) h To keep it simple and study the effect of residue h , let us assume that the modulation scheme used is antipodal signaling. The antipodal signal constellation is given in fig.3.8 The ML error probability is found out by recalling derivation from Madhow text book [25] r 2Eb Pe,M L = Q( ) (3.18) No Where Eb is the energy per bit and No is the Gaussian Noise power. Now this is implied assuming that channel ’h’ did not include any unknown imperfect CSI, from the equation 3.17 it is clear that along with input signal the energy of factor needs to be included. The equation 3.18 will which is along with input signal x h− h take the form in this case s ||[1 − h ]||2 2Eb Pe,M L = Q( ) (3.19) No r 2Eb ) (3.20) Pe,M L = Q([1 − ] h No Now we study the effect of the this important ratio h by simulation in matlab. Eb With different SNR levels and sweep the ratio h from 0 to 3 and keeping a fixed No SNR at a time. It shows that error probability saturates to maximum soon when the ratio is more than 1 meaning the channel is completely unknown at this time when grows bigger than channel state h. Also we observe that with high SNR values the curve becomes more steep. 19 Figure 3.8: Antipodal signaling constellation Pe with different SNR 1 0.9 0.8 0.7 −10 dB − 5 dB 0 dB 5 dB 10 dB 15 dB 20 dB Pe,ML 0.6 0.5 0.4 0.3 0.2 0.1 0 0 0.5 1 1.5 Epsi / h 2 Figure 3.9: Performance with Imperfect CSI 2.5 3 20 Chapter 4 Simulation In this chapter we start by simulating an OFDM transceiver, to gain an in depth working idea of all the modules involved. The transceiver is then tested for performance by calculating symbol error rate(SER) curves for Gaussian, Rayleigh and Rician fading distributions. Next we simulate the fixed wireless channel model and produce Doppler spectrum plots for different cases with varying velocity of scatterers. 4.1 OFDM transceiver chain In this simulation we only include baseband part rather than in actual scenario according to Figure 3.1 passband is included to modulate the baseband signal to a certain carrier frequency. Table 4.1: OFDM Simulation parameters Parameter Sampling Frequency FFT size Cyclic prefix No. of symbols for each SNR Channel type Value 30.72 MHz 256 4 samples 1024 AWGN, Rayleigh, Rician Figure 4.1: OFDM baseband transmitter 21 The Figure 4.1 shows the transmitter modules and xb is the baseband signal produced at the end of transmitter chain. Data in form of bits R is available at the start which needs to be transmitted at transmitter and recovered at the receiver. This data is converted to QAM complex symbols and these symbols are then converted from serial to parallel. N number of QAM symbols are fed at the S/P block where N is the size of the FFT. After conversion of complex symbols to parallel IFFT operation is performed and time domain signal x is obtained. Next we add a cyclic prefix which is just the repetition of a number of starting samples extended at the end. The length of cyclic prefix could be variable but importantly should be greater than maximum delay spread of the channel to avoid inter symbol interference. Cyclic prefix length has been standardized for LTE. The signal xb is then passed through fading channel and AWGN noise is added. Three fading channels are simulated i.e Gaussian, Rayleigh and Rician fading channel. This noisy and faded baseband signal is then fed to the receiver. Figure 4.2: Baseband signal through channel y=x∗h+n (4.1) The noisy faded baseband signal is then fed to the receiver. According to Figure 4.3, First we remove the cyclic prefix according to the number of samples set at the transmitter. Then after serial to parallel conversion FFT operation is performed to get the signal in frequency domain. The frequency domain signal Y is fed to the equalizer which performs frequency domain zero forcing equalization i.e dividing the faded noisy signal with the recovered channel Y/H with important assumption of perfect channel knowledge at the receiver. The zero forcing equalizer is simple but it has a draw back of noise enhancement. Next the equalized signal is fed to the detector which performs maximum likelihood detection. With assumption of equal symbol probability for each symbol of the constellation ML detector works on the principle of defining decision boundaries. In Figure 4.4 The green lines represent the decision boundaries for QAM modulated symbols. Any received symbols that falls in their respective areas will be detected accordingly. The ML detector is also extendible to higher order of QAM or for other types of symbol modulations. After parallel to serial conversion X̂ is the 22 Figure 4.3: OFDM baseband receiver Figure 4.4: ML detector 23 detected QAM symbols sequence which is sent to QAM demodulator to recover the original bit stream which was transmitted. Symbol error probability curves for QPSK(4−QAM) 0 10 theory−QPSK simulation−AWGN simulation Rayleigh simulation Rician −1 Symbol Error Rate 10 −2 10 −3 10 −4 10 −5 10 0 5 10 15 20 Es/No, dB 25 30 35 40 45 Figure 4.5: Symbol error rate curves The symbol error rates are calculated for the three types of channels with varying SNR in Figure 4.5. The theoretic and simulated AWGN produced follow each other showing the method is concise. The Rayleigh and Rician symbol error rates show a similar behaviour but Rician fading has lower errors because of a LOS component. 24 4.2 Doppler spectrum fixed wireless channel In this section we simulate behaviour of multipath fading channel through the generation of time series, and their statistical analysis by producing Doppler spectrum curves to gain more insight into this phenomenon. To achieve this goal, we introduce a multiple point-scatterer model based on a Cartesian coordinate system. We are able to simulate Rayleigh and Rician fading with a sum-ray model in which there is a fixed base station and a fixed mobile station. By a ray we mean a complex exponential with a certain wavelength depending upon carrier frequency. As the scatter moves it introduces a change in frequency which depends upon the distance the wave travels from transmitter to receiver. The multi point scatterers are able to move because of which there is a change in path which each ray takes after reflecting from a certain scatterer. rk = e−j2πfc tk = e−j2πfc −j2π =e Dk c Dk λc (4.2) = e−jkc Dk where vector rk represents a ray which travels from transmitter to k th scatterer to the receiver, fc is the carrier frequency, kc = 2π is the constant for convenience λc th and Dk is the distance vector for k scatterer. For a Rayleigh fading channel without any LOS component, R is the envelop signal at receiver. R= k X rk (4.3) However for a Rician fading channel, where Dd represents a direct ray from transmitter to receiver and a0 is the magnitude of direct signal R= k X rk + a0 e−jkc Dd (4.4) The power spectrum of the received envelop signal R represents the doppler spectrum, which can be mathematically calculated by taking the absolute squared of the fourier transformed autocorrelation function of R Pd = |FFT(ACFR )|2 (4.5) The Figure 4.6 shows a realization of the model with scatterers in a circle to form uniform arrival angle distribution, an important condition for the Doppler spectrum according to Clarke [20]. The triangle represents a base station and mobile 25 Table 4.2: Scatter simulation parameters Parameter Value Sampling Frequency 1 KHz Carrier Frequency 2GHz Number of time samples 10000 Number of scatterers 100 Radius of fixed scatterers 200m Distance between BS and MS 1000m FFT size 1024 Channel type Rayleigh, Rician station is at the centre of circle whose position remains fixed. Now there are two types of scatterers, one which are moving and other one which remain still. We select a given number of moving scatterers and place them initially at random positions within the circle, which move with time at random velocities with a certain maximum velocity. We can set the number of moving scatterers as a factor of the total number of scatterers and the maximum velocity of movement is adjustable. We do an analysis based on varying maximum velocity of scatterer and the number of moving scatterers and observe interesting results that Doppler spectrum is widened by increasing the maximum velocity and also by increasing the number of scatterers which are moving. 26 500 400 Propagation scenario. Distance (m) 300 200 100 0 −100 −200 −300 −400 −500 −200 0 200 400 600 Propagation scenario. Distance (m) 800 1000 Figure 4.6: Scatter simulation model 27 Figure 4.7: Doppler spectrum analysis 28 Chapter 5 Measurements and Field trials In this chapter we describe the field trials for downlink measurements done with LTE advanced test-bed at TU-Dresden. We further describe the measurement setup for indoor and outdoor measurements. The motivation was to find out a measure of stability of wireless channel in a scattering environment. Measurement campaign included initial indoor measurements and a downlink outdoor and uplink outdoor measurement. From the relay deployment point of view an optimum antenna height and position for best performance and an efficient channel state feedback scheme. The LTE standard channel state feedback scheme provides an overhead considering that for a fixed wireless channel it is deduced from the measurements evaluation that channel state does not vary as much over time. So there is an opportunity to gain advantage of the fixed relay scenario and utilize some reference OFDM symbols for data which are used for channel feedback in standard. 5.1 Test-bed equipment The measurement equipment for field trials was SORBAS LTE advanced test equipment by Signalion for research purpose [21]. Detail on SORBAS test setting and possibilities is available in appendix. The test equipment can be used as a UE as well as an enB with a change in firmware and layer 1/layer 2 level signal analysis could be performed. It can be used for uplink and downlink measurement purposes and generates a dump file for the measurement duration already set. The dump file is stored on an attached computer for evaluation. Figure 5.1 shows the SORBAS test equipment. Two antenna elements per UE and per enB can be attached via cable depending on the used MIMO configuration. 5.2 Indoor measurements Initial measurements were done indoor to verify the stability of test-bed and initial evaluation to check stability of channel under static condition. Further measurements were done with one person moving, two persons moving and three persons moving in the lab. Initial motivation was to identify if possible the taps in the channel impulse response to certain scatterers with observation of over time. The Figure 5.2 shows the indoor measurement setup and Figure 5.3 shows dimensions of indoor environment. The Table 5.1 shows the important measurement parameters. 29 Figure 5.1: SORBAS test equipment 30 Figure 5.2: Indoor measurement environment 31 Figure 5.3: Lab dimensions and movement description 32 Table 5.1: Indoor Measurement parameters Parameter Value Sampling Frequency 30.72 MHz Measurement type Downlink Antenna elements 2 per enB and 2 per UE Antenna polarization Cross polarized MIMO setting 2x1 (Dump taken on 1 UE antenna) Measurement duration 2100 TTI (2.1 sec) Measurement Scenarios Static, 1 person moving, 2 persons moving, 3 persons moving Antenna transmit power 10.4 dBm per antenna measured with spectrum analyser During indoor measurements campaign there were several different scenarios tested. • Static environment • 1 person moving in defined direction as in figure 5.3 • 2 persons moving in defined direction as figure 5.3 • 4 persons randomly • 1 person moving along with the board We have observed that the channel becomes more and more chaotic as we add more scatters in the environment. We look at the CTF through the measurement duration to see the channel behaviour while moving scatters. We further look at the variance mean variance over the measurement duration of subcarriers to understand which are the subcarriers most affected by the small scale movements. This will give us an insight about the frequencies affected by small scale movements. For sure more and more statistics will be required to strengthen the claims done during the measurement evaluation but the current observation are based on the few iterations that are listed above. The CTF of all the iterations are given from figure 5.4 to figure 5.7 33 Figure 5.4: Indoor Static environment Figure 5.5: Indoor 1 person moving environment 34 Figure 5.6: Indoor 2 persons moving environment Figure 5.7: Indoor 4 persons moving environment 35 5.3 Outdoor measurements After successful indoor measurement campaign, the integrity of test equipment was tested to be working fine. A downlink Outdoor measurement campaign was carried out in Dresden area. The motivation was to find out ideal height and location of a relay considering deployment scenario based on performance and channel correlation characteristics. The scatterers were basically cars on the road whose impact was studied on the channel with increasing height. Figure 5.8 shows the map of the testbed in Dresden. The 3 enB sectors cooperating during the measurement are Hbf Sud 60o (8), Hbf 240o (3) and Lenneplatz 180o (6). Two configurations were measured for each antenna height • enB antenna downtilt 7o • enB antenna downtilt 15o Figure 5.8: Test-bed map Each measurement dumps taken for a duration of about 1 sec and evaluation is done with a post processing dump analysis tool where we estimate the channel and 36 calculate the important metrics. The configuration for the outdoor measurements are listed in the table 5.2. Table 5.2: Outdoor Measurement parameters Parameter Sampling frequency Measurement type Antenna elements Antenna polarization MIMO setting Measurement duration Rx Antenna height Tx Antenna downtilt Antenna transmit power Value 30.72 MHz Downlink 2 per enB and 2 per UE Cross polarized 2x6 1 sec 2.5m, 4m, 5.5m, 7.5m 7o and 15o 10.4 dBm per antenna measured with spectrum analyser Some captures from the outdoor measurement campaign are given in figure 5.9 and 5.10 5.3.1 Evaluation chain The measurement dumps taken during the downlink campaign are evaluated in order to estimate/equalize the channel and calculate important metrics. The dump made from SORBAS is of the baseband signal which already includes the frequency error corrections done by the frontend. So what we receive is a baseband signal ready to be processed. The whole processing and evaluation MATLAB chain is summarized in the figure 5.11 37 Figure 5.9: Measurement location and Van 38 Figure 5.10: Measurement location and Van 39 Figure 5.11: Post dump analysis chain 40 Chapter 6 Results In this chapter we will discuss the important results based on the downlink indoor and outdoor measurement campaigns. We see some interesting relation and observation in wireless channel based on the small scale movements in environment as well as with the antenna height. 6.1 Indoor measurements The different iterations as explained in the Measurements chapter are done for indoor trials. By Inspection of the CTF provided for the measurements we can deduce important observations. • Not all the frequencies are affected by the moving persons • Only a certain range of subcarriers are affected around DC subcarrier (100), by the inspection in plots figure 5.4 to figure 5.7, We see that approx 30 pilot subcarriers are mostly affected around DC subcarrier. It is an interesting result as it enables us to distinguish the band out of total bandwidth which will be affected the most for small scale movements. Let’s say W be the total bandwidth and γ be the factor of the bandwidth which is affected by small scale movements, then Ws = γW (6.1) where Ws is the part of total bandwidth W which is affected by scatter movements. By our OFDM knowledge we know that spacing between pilot subcarriers is 6*15KHz, so by inspection i.e 30th SC on both sides around DC subcarrier would mean a bandwidth of 2*6*30*15KHz = 5.4MHz (Ws ) bandwidth is affected in this s = indoor measurements scenario out of 20MHz(W), so the bandwidth factor γ = W W 0.27. It is desirable to have γ as small as possible because in that case we have a fairly static channel and then CSI will then be reliable for longer time. The limitation of this result is the statistics, we only had enough much time to do these 5-6 iterations base on this we present these measurement results, it is desirable to carry out more of similar experiments to have statistically significant data. 41 6.1.1 Sub-carrier power variance The power variance of subcarriers as described in 3.12 is calculated from the evaluation chain and the figure 6.1 shows the results for indoor iterations. We can see that variance of the subcarriers around DC are actually more than ones on the mid to end range of the bandwidth. We find out a mean curve for all the variances combined and there is a peak around DC subcarrier with a width of 20-30 subcarriers. This also matches to the result we observed above in equation 6.1. 6.2 Outdoor measurements [h] For the outdoor measurement we will also look at the power variance of subcarriers also we will measure the performance with MIMO capacity . 6.2.1 Sub-carrier power variance The power variance of subcarriers in outdoor environment shows that in an uncontrolled environment attenuation can not be limited to a specific bandwidth. The variation is distributed over the band and the movements can not be limited to small scale because environment can have many factors which cause the attenuation. There are different size and shapes of scatters also could be many multipath sources. The results shown in figure 6.2 shows the variance of each MIMO TxRx path. The solid lines denote the RxAnt1 and the dotted lines denote the RxAnt2 for the same Tx. We can see that mean variance is existing over the complete bandwidth and not over a subset. It is observed that even in the case of no moving cars seen, still the subcarrier power variance is distributed over the bandwidth, which is because of characteristic of multipath and other scatters than the prime ones i.e road traffic. The possible scatters causing this kind of distribution in attenuation could be e.g moving trees because of wind. It shows us that outdoor environment can’t be completely static. 6.3 Capacity Capacity is most important metric in performance qualification of a channel. The theoretical background about capacity was discussed in section 3.2.3, here we present the results of the capacity based on 5.2. We see a difference in capacity with a downtilt angle changed from 7o to 15o mostly because the LOS component is reduced and Fresnel zone is lost in this case, it impacts capacity as it directly impacts the received SNR at the receiver antenna which is a function of capacity. We deduce several interesting observation from figure 6.3. • Generalized result show that capacity is higher for LOS MIMO wireless channel than NLOS 42 • No cars study, shows relation between antenna height and capacity is such that antenna height doesn’t affect capacity which is more or less consistent for the downtilt 7o , however for downtilt 15o we see a trend of increasing capacity with height. We can deduce that with strong LOS component capacity is independent of Rx Antenna height, however with weak LOS component increasing height improves the capacity. • The moving cars study, shows that moving scatters introduce the diversity effect for LOS MIMO wireless channel and capacity numbers are unexpectedly higher in the presence of scattering environment, however for weak LOS channel (downtilt 15o ), Capacity remains consistent with increasing height. Notable point for a peak in downtilt 15o with cars measurement at 2.5m height as it is a near LOS channel condition. 43 60 NoMove Move1 Move2 MoveBoard Move3dir Move4 AvgVar 50 Variance dB 40 30 20 10 0 0 20 40 60 80 100 Pilot Subcarriers 120 140 160 180 200 Figure 6.1: Indoor environment SC power variance 44 12 10 Variance dB 8 6 4 2 0 0 20 40 60 80 100 Pilot Subcarriers 120 140 160 180 Figure 6.2: Outdoor environment SC power variance 200 45 MIMO Capacity calculation using eq.3.14 33 MIMO 2x6 Capacity (bps/Hz) 32 LOS, no moving cars LOS, with moving cars NLOS, no moving cars LOS, with moving cars 31 30 29 28 27 2.5 3 3.5 4 4.5 5 5.5 increasing antenna height (m) 6 6.5 Figure 6.3: Measured MIMO capacity 7 7.5 46 Chapter 7 Conclusions In this chapter we will summarize important conclusions which are drawn from the scatter simulation, indoor and outdoor measurements. 7.1 Concluding remarks • Doppler spectrum analysis fig. 4.7 shows that doppler spectrum bandwidth increases with increasing number of moving scatters, as well as increasing the speed of moving scatters. This means that when there are more scatters around e.g moving people, moving cars, wind blowing trees etc. the channel will become more varying in time as coherence time is inversely related to doppler spread and the duration for which channel state remains consistent will be lesser. The same relation holds with increasing speed of scatters. • Realtime measurement shows that channel becomes increasingly chaotic by increasing moving scatters this confirms the simulation results which are presented in the first conclusion above. Also another observation shows small scale movements contribute to the channel attenuation to a maximum factor of γ = 0.27 of the total bandwidth. The derivation is well explained in results section 6.1. • MIMO channel capacity increase in NLOS condition in relation to antenna height whereas in LOS condition it is mostly independent of the height. This conclusion is applicable on the height of the receiver antenna/relay keeping the same condition of scatters. This conclusion has been deduced as a result from capacity plot in fig. 3.14. • Scatters improve the channel capacity in LOS channel condition, whereas in NLOS condition the presence of scatters show no significant effect on the capacity of channel. This conclusion has been deduced as a result from capacity plot in fig. 3.14. 7.2 Future directions We have studied the affects of moving scatters on the FWA channel, several interesting facts are concluded. The studies could be further improved by improving statistics of the measurements, as the results are based on limited number of iterations. The aim to possibly identify a specific channel tap in CIR for a moving scatter so it could help in efficient cancellation of scattering attenuation couldn’t be achieved, because maybe the goal was too optimistic and channel doesn’t behave like that. This could 47 be further studied in future to stress on the time domain effects of scatters. The control of the scatters could be improved and even more defined movements could be tested and studied. Measurement of imperfect channel state should be done in realtime so that we can tell at what point in time the state becomes bad enough so that no more decoding is possible. 48 Bibliography [1] http://www.howmanyarethere.org/how-many-mobile-phone-users-in-theworld/ [2] Cisco visual networking index (VNI), Global mobile data traffic forecast(20112016) [3] B.Sklar, Digital Communications Fundamentals and Applications. Prentice Hall PTR Second Edition, 2002 [4] Tafzeel UR Rehman.A, Link Reliability in Cooperative Relaying Using Network Coding PhD Dissertation [5] J. G. Proakis,Digital Communications. McGraw-Hill International Edition, 2001 [6] D. Greenwood and L. Hanzo, Characterization of mobile radio channels, in Mobile Radio Communications, R. Steele, Ed. London, U.K.:Pentech, 1992, pp. 163185. [7] M. Schwartz, W. R. Bennett, and S. Stein, Communication Systems and Techniques. 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[25] Text book Fundamentals of digital communication by Upamanyu Madhow section 3.5.1 about performance with binary signaling 1 Appendix A SORBAS test equipment A.1 AVAILABLE SORBAS TEST UE OPTIONS Basis Option FDD (UE Category 1,2,3) High Throughput Option FDD (UE Category 4) Basis Option TDD (UE Category 1,2,3) High throughput option FTD (UE Category 4) Multi UE (simulation of up to 64 UEs with one Sorbas Test UE) A.2 DUPLEX MODES FDD and TDD is supported (Test UE options) A.3 RADIO FREQUENCY FDD: Band 1-14, 17-19 UHF 790-862 MHz(Digital dividend) TDD: Band 33-40 UHF 790-862 MHz(Digital Dividend) A.4 PHYSICAL LAYER FEATURES Standard Specification: Signalion will provide ongoing standard adaptation. The UEPHY implementation is according to 3GPP Release 8: TS 36.201 ,TS 36.211 ,TS 36.212 ,TS 36.213 , A.4.1 Downlink Characteristics OFDM including HARQ; Modulation QPSK, 16 QAM, 64 QAM; SISO, SFBC , RX diversity,2x2 MIMO A.4.2 Uplink Characteristics SC-FDMA UL Modulation QPSK, 16 QAM; 2 A.4.3 PhysicalSignals and Channels P-SCH/ S-SCH / RS; PBCH; PRACH; PUSCH; PUCCH, PDSCH; PDCCH; PCFICH; PHICH SRS; A.5 LAYER 2 FEATURES Standard Specification: Signalion will provide ongoing standard adaptation. A.5.1 The UE MAC and RLC implementation according to 3GPP Release 8 TS 36.321, TS 36.322 MAC: Channel multiplexing, Random Access, HARQ support, Priority handling, Buffer status reporting RLC: Transparent Mode (TM) Unacknowledged Mode (UM) Acknowledged Mode (AM); PDCP: Part of the protocol test Option Transport Channels: RACH; UL-SCH; DL-SCH; PCH; BCH/D-BCH TRITA -ICT-EX-2015:135 www.kth.se