This dissertation consists of three themes. The first part is to investigate the performance issue and STF codes design for MIMO-UWB systems. The second part aims to investigate the two-ring channel model with a LOS com-ponent of MIMO Rician channels in mobile-to-mobile ad hoc networks. We analyze the ACF, LCR, AFD, and capacity of the proposed channel model.
The third part contains a cooperative network coding protocol and the anal-ysis of its outage probability and DMT.
The remaining chapters of this dissertation are organized as follows.
Chapter 2 reviews some pivotal subjects for UWB, e.g., the IEEE 802.15.3a and 802.15.4a channel models. Then we introduce the Gauss-Hermite for-mula. Literature surveys of some related works are also provided. In Chapter 3, we analyze the BER performance in the IEEE 802.15.3a and 802.15.4a
UWB channel models with Rake receiver and shadowing effect. In Chapter 4, we present an analytical expression for the SNR of the PPM signal in an UWB channel with multiple transmit and receive antennas. In Chapter 5, we turn to design a BER-minimized STF codes for MIMO highly frequency-selective block-fading channels. In Chapter 6, we derive the ACF, LCR, and AFD of the mobile-to-mobile Rician fading channel and verify the accuracy by simulations. Then, in Chapter 7, we suggest a sum-of-sinusoids MIMO mobile-to-mobile channel simulation method, which can characterize the spa-tial/temporal channel correlation and Rician fading effect. We examine how often the MIMO capacity experiences the fades and relate this to the Rician factor. In Chapter 8, we consider a relay channel and DF cooperative com-munications system combined with the network coding. We derive the outage probability and DMT for the proposed CNC protocol. At last, Chapter 9 provides the concluding remarks and some suggestions for future works.
Chapter 2
Background and Literature Survey
In this chapter, we survey related works to the performance analysis and STF code design for MIMO-UWB systems, channel modeling and statistical anal-ysis for MIMO Rician channels in mobile ad hoc networks, and the network coding for cooperative multiplexing. We also introduce the background for IEEE 802.15.3a and 802.15.4a UWB channel models. Then, we compares the two channel models. Finally, we review the Gauss-Hermite formula which is used for the BER analysis in IEEE 802.15.3a and 802.15.4a UWB channel models in our dissertation.
2.1 Literature Survey
2.1.1 Bit Error Rate Analysis in IEEE 802.15.3a and 802.15.4a UWB Channels
In the literature the current research related to the performance analysis of UWB systems can be categorized into two folds. Firstly, the UWB system has been investigated based on simpler channel models [29–32]. In [29], the authors derived the BER formula for the M-ary UWB signals under the AWGN channel and multiple access interference. In [30], the UWB systems was investigated in the presence of the interference from the wideband code division multiple access (WCDMA). [31] derived the BER performance of the UWB system under dispersive Rayleigh fading channels with timing jitter. In [32] a moment-generating function (MGF) approach was proposed to analyze the performance of a transmit-reference (TR) UWB system under a slowly fading channel.
Secondly, [7, 33–37] investigated the performance of UWB systems based on more sophisticated UWB channels, such as the IEEE 802.15.3a model. It is challenging to derive the distribution of the collected signal energy in the IEEE 802.15.3a channel model because the numbers of clusters and rays are random. In [7], the authors applied the techniques of counting integrals and shot noise to derive the computation BER formula in the IEEE 802.15.3a channel assuming the received waveform can be observed over a finite-length window. In [33], the output SNR statistics at the RAKE receiver in the IEEE 802.15.3a channel was presented, but the explicit BER formula for RAKE receivers taking account of shadowing was not presented. [34] analyzed the pairwise error probability (PEP) and outage probability of multiband orthog-onal frequency-division multiplexing (OFDM) systems in the IEEE 802.15.3a
channel model, but ignore the effect of the lognormal shadowing. [35] ana-lyzed the effect of multiple antennas on the UWB system under a general-ized UWB channel. In [36], the error performance of a multi-antenna RAKE receiver was analyzed over the frequency-selective UWB lognormal fading channels. [37] analyzed the signal-to-interference-plus-noise ratio (SINR) of direct sequence (DS) UWB systems in generalized Saleh–Valenzuela channels based on the theory of renewal process.
2.1.2 On the Performance of Using Multiple Transmit and Receive Antennas in Pulse-Based Ultrawide-band Systems
In general, the UWB system can be classified into three kinds: the first one is the multiband orthogonal frequency division multiplexing approach, the second kind is the time hopping ultra-wideband (TH-UWB) system, and the third kind is the DS-UWB [38]. In this part, we focus on the TH-UWB system with pulse position modulation (PPM). Through modulating an information bit over extremely large bandwidth of several gigahertz, the TH-UWB system can possess many nice properties, including: high path resolution in the dense multipath fading environment [39–41], smooth noise-like frequency-domain characteristics [39]; carrierless transmission [40] and low transmission power operation [10, 39, 40].
Besides UWB, space-time processing transmit diversity techniques, such as space-time block codes (STBC) or space-time trellis codes (STTC), is another important research area recently [42–45]. It is noteworthy that these space-time processing transmit diversity schemes are originally designed for signals with information bits modulated by the amplitude or phase of a signal,
rather than the occurrence time of a signal. Since a PPM signal represents its data information bit according to the pulse displacement from a specified time reference. Thus, directly applying STTC or STBC in the PPM based UWB system may not be easy, especially in a highly dense frequency selective fading channel [46].
In spite of numerous advantages for the UWB system, it is crucial to make the best use of the radiation power because of its extremely low transmitted power. Consequently, although fading may not be serious in the pulsed mode UWB system, receive antenna diversity is suggested for the UWB system to improve energy capture [18, 47]. In the literature, fewer papers have been reported to address the issue of employing transmit diversity for the pulsed-UWB system, except [48] and [49]. In [48], the authors evaluated the performance of the pulse-amplitude modulation (PAM) signals in the UWB MIMO channel. In [49], the authors proposed a space-time block code scheme for the PPM based UWB system in the flat fading real channel, where the received pulses through the radio channel are assumed to be orthogonal with each other.
2.1.3 BER-Minimized Space-Time-Frequency Codes for MIMO Highly Frequency-Selective Block-Fading Channels
Here, we introduce some related works about space-time-frequency codes (STFC) for the MIMO-OFDM systems. In [50], the authors investigated STFC for MIMO-OFDM and found an equivalence between antennas and subcarriers. The authors then suggested a complexity-reduced scheme with coding across subcarriers only. In [51], the authors proposed an adaptive
STFC scheme according to the space-frequency water-filling procedure for OFDM systems. In [52], the authors considered STFC over MIMO-OFDM block-fading channels and derived a sphere packing lower bound on the average word error probability and an upper bound for pairwise word error probability, but they did not show how to design the optimal codes to achieve these bounds. In [1], authors proposed a systematic design method for high-rate full-diversity STF codes for broadband MIMO block-fading channels. In [2], authors presented rate-two STF block codes for multiband UWB-MIMO communication systems using rotated multidimensional modu-lation. We will show by simulation that our proposed STF codes have better BER performance than the codes in [1] and [2] do.
2.1.4 Statistical Analysis of A Mobile-to-Mobile Ri-cian Fading Channel Model
In the literature, most channel models for wireless communications were mainly developed for the conventional base-to-mobile cellular radio systems [23, 53–55]. Whether these mobile-to-base channel models are applicable to the mobile-to-mobile communication systems remains unclear. Some, but not many, channel models had been previously studied. In [56], the the-oretical performance of the mobile-to-mobile channel was developed. The authors in [57] introduced the discrete line spectrum method for modeling the mobile-to-mobile channel. However, the accuracy of this method was assured only for short-duration waveforms as discussed in [58]. A simple but accurate sum-of-sinusoids method was proposed for modeling the mobile-to-mobile Rayleigh fading channel in [58]. The inverse fast Fourier transform (IFFT)-based mobile-to-mobile channel model was also proposed in [59]. Al-though most accurate compared with the discrete line spectrum and the
sum-of-sinusoids methods, the IFFT-based method requires a complex ellip-tic integration. In [60], the authors presented an analysis of measured radio channel statistics and their possible influence on the system performances in outdoor-to-indoor mobile-to-mobile communication channels. However, in [20, 56–60], the effects of the line-of-sight (LOS) are all ignored.
To evaluate the performance of the physical layer, a simple channel sim-ulator, such as Jake’s method in conventional cellular systems, is necessary.
Related works on the mobile-to-mobile Rician fading channel include the following. In [21], a statistical model for a mobile-to-mobile Rician fading channel with Doppler shifts is presented. In [22], the model in [21] is em-ployed to obtain the probability density function (PDF) of the received signal envelope, the time-correlation function and radio frequency (RF) spectrum of the received signal, LCR, and AFD.
2.1.5 Modeling and Capacity Fades Analysis of MIMO Rician Channels in Mobile Ad Hoc Networks
In the literatures, some MIMO channel models have been reported. In [61], the authors described the capacity behavior of outdoor MIMO channels as a function of scattering radii, antenna beamwidths, antenna spacing, and the distance between the transmit and receive arrays. We only consider the antenna spacing for simplicity, but we consider Rician fading, LCR, AFD, and the impact of the number of scatterers. In [62], the author derived a general model for the MIMO wireless channel which considered the inter-dependency of directions-of-arrival and directions-of-departure, angle disper-sion by far clusters, and rank reduction of the transfer function matrix. This MIMO wireless channel model based on several physical phenomena such as scattering by far clusters, diffraction, waveguiding effects, and the
interde-pendency of the directions-of-arrival and the directions-of-departure. Our proposed MIMO channel is an extension of Jake’s model, which can help channel simulation by only considering the channel correlation in both the spatial and time domain. In [63], the authors derived the MIMO capacity, LCR, and AFD considering the impact of spatial/temporal channel corre-lation. However, the model in [63] considered the one-ring model which is more suitable for the mobile to base station scenario. In this chapter, the two-ring scattering model is adopted to capture the channel characteristics of the mobile-to-mobile communication. Further, we consider the impact of the Rician K factor and the number of scatterers on the total channel capacity, both of which are not considered in [63]. In [64], the authors pro-posed a single-bounce two-ring statistical model for the time-varying MIMO flat Rayleigh fading channels and derived the spatial-temporal correlations, LCR, AFD, and the instantaneous mutual information (IMI). However, they did not consider the impact of Rician K factor, number of scatterers, and the antenna separation. In [65], the authors investigated the effects of fading correlations in MIMO systems using the one-ring model. We consider the general two-ring model and derive the LCR, AFD, and an upper bound for the average channel capacity. In [66], the author presented the narrowband one-ring and two-ring models but did not consider the LOS component. In our channel model, we include the LOS component and consider the impact of Rician K factor on channel capacity, LCR, and AFD.
2.1.6 Network Coding for Cooperative Multiplexing
Many cooperative communication protocols were proposed to improve diver-sity gain, such as orthogonal amplify and forward (OAF) [67], nonorthogo-nal amplify and forward (NAF) [68], space-time coded (STC) cooperative
diversity protocols [69–71], dynamic decode-and-forward (DDF) [68], en-hanced static and-forward (ESDF), and enen-hanced dynamic decode-and-forward (EDDF) [72]. However, how to provide multiplexing gain by taking advantage of relays has not received much attention so far. Com-bining the network coding with the cooperative communications, or called the cooperative network coding (CNC) [73–84], have a potential to exploit the multiplexing gain in many relay nodes (virtual antennas). The diversity-multiplexing tradeoff (DMT) analysis of CNC has not been seen in the liter-ature.