A central feature of wireless networks is multiple users sharing a common medium. Cellular systems are among the most common examples of such networks. The main phenomenon resulting from this inter-user interaction is interference, and thus analyzing interference networks is critical to determine the capacity of wireless networks. The capacity region of an interference network is defined as the set of rates that the users can simultaneously achieve while ensuring arbitrarily small probability of decoding error. It is an inherently hard problem to find the capacity region of interference networks. Even the capacity region of a general 2-user interference channel is a prominent open problem in information theory. This work's goal is to derive achievable regions that are improved over known results, and when possible, capacity theorems, for K user interference networks. Another multiuser channel that is commonly found in wireless systems is a broadcast channel. Broadcast channels stand side by side with Interference channels as the two of the most important channels for which capacity results are still not completely known. In this work we develop inner and outer bounds on the capacity region of fading broadcast channels, using which we find a part of the capacity region under some conditions. In summary, this work first presents coding arguments for new achievable rate regions and, where possible, capacity results for K-user interference networks. Second, it provides inner and outer-bounds for a class of fading broadcast channels. / text

Multi-user wireless systems with multiple antennas can drastically increase the capac- ity while maintaining the quality of service requirements. The best performance of these systems is obtained at the presence of instantaneous channel knowledge. Since uplink-downlink channel reciprocity does not hold in frequency division duplex and broadband time division duplex systems, efficient channel quantization becomes important. This thesis focuses on different quantization techniques in a linearly precoded multi-user wireless system. Our work provides three major contributions. First, we come up with an end-to-end transceiver design, incorporating precoder, receive combining and feedback policy, that works well at low feedback overhead. Second, we provide optimal bit allocation across the gain and shape of a complex vector to reduce the quantization error and investigate its effect in the multiuser wireless system. Third, we design an adaptive differential quantizer that reduces feedback overhead by utilizing temporal correlation of the channels in a time varying scenario.

Multi-user wireless systems with multiple antennas can drastically increase the capac- ity while maintaining the quality of service requirements. The best performance of these systems is obtained at the presence of instantaneous channel knowledge. Since uplink-downlink channel reciprocity does not hold in frequency division duplex and broadband time division duplex systems, efficient channel quantization becomes important. This thesis focuses on different quantization techniques in a linearly precoded multi-user wireless system. Our work provides three major contributions. First, we come up with an end-to-end transceiver design, incorporating precoder, receive combining and feedback policy, that works well at low feedback overhead. Second, we provide optimal bit allocation across the gain and shape of a complex vector to reduce the quantization error and investigate its effect in the multiuser wireless system. Third, we design an adaptive differential quantizer that reduces feedback overhead by utilizing temporal correlation of the channels in a time varying scenario.

<p> There are two popular approaches in the communication between multiple receivers and a base station with multiple antennas: dirty paper coding and multiuser diversity. Dirty paper coding can be rather difficult to realize, which motivates people to find some practical schemes. When there are a lot of users, multiuser diversity requires a lot of feedback which decrease the uplink spectrum efficiency.</p> <p> In this paper, we aim to minimize the probability of error subject to the total transmit power constraint and decrease the amount of feedback required by the multiuser diversity instead of trying to achieve the dirty paper coding. There are two main results in this thesis: First, we formulate the minimization of the average probability of error of all the users as a convex optimization problem, subject to the peak or the average power constraints. The proposed transmitter represents a nonlinear one-to-one mapping between the transmitted data vector and the symbol vector. The transmitted data vector going through the base station antennas is obtained as a solution to the proposed convex error probability optimization problem that can be solved using computationally efficient interior point algorithms. Furthermore, we propose a random unitary beamforming technique to reduce the feedback by selecting a threshold for the users. To improve fairness, an equal ratio scheduling algorithm which could serve the users with different rate requirements is developed. We also give an upper and lower bound on the sum rate achievable in our approach. Monte Carlo simulation results is provided to verify the performance of the proposed algorithms.</p> / Thesis / Master of Applied Science (MASc)

This dissertation focuses on downlink multi-antenna transmission with packet scheduling in a wireless packet data network. The topic is viewed as a critical system design problem for future high-speed packet networks requiring extremely high spectral efficiency. Our aim is to illustrate the interaction between transmission schemes at the physical layer and scheduling algorithms at the medium access control (MAC) layer from a sum-capacity perspective. Various roles of multiple antennas are studied under channel-aware scheduling, including diversity, beamforming and spatial multiplexing. At a system performance level, our work shows that downlink throughput can be optimized by joint precoding across multiple transmit antennas and exploiting small-scale fading of distributed multiple input and multiple output (MIMO) channels. There are three major results in this dissertation. First, it is shown that over a MIMO Gaussian broadcast channel, and under channel-aware scheduling, open-loop transmit antenna diversity actually reduces the achievable sum rate. This reveals a negative interaction between open-loop antenna diversity and the closed-loop multiuser diversity through scheduling. Second, a suboptimal dirty paper coding (DPC) approach benefits greatly from multiuser diversity by an efficient packet scheduling algorithm. Performance analysis of a suboptimal greedy scheduling algorithm indicates that, compared with the receiver-centric V-BLAST method, it can achieve a much larger scheduling gain over a distributed MIMO channel. Further, pre-interference cancellation allows for transmissions free of error propagation. A practical solution, termed Tomlinson-Harashima precoding (THP), is studied under this suboptimal scheduling algorithm. Similar to V-BLAST, a reordering is applied to minimize the average error rate, which introduces only a negligible sum-rate loss in the scenarios investigated. Third, for an orthogonal frequency division multiplexing (OFDM) system using MIMO precoding, it is shown that a DPC-based approach is readily applicable and can be easily generalized to reduce the peak-to-average power ratio (PAR) up to 5 dB without affecting the receiver design. Simulations show that in an interference-limited multi-cell scenario, greater performance improvement can be achieved by interference avoidance through adaptive packet scheduling, rather than by interference diversity or averaging alone. These findings suggest that, coordinated with channel-aware scheduling, adaptive multiplexing in both spatial and frequency domains provides an attractive downlink solution from a total capacity point of view. / Ph. D.

The proliferation of connected embedded devices has driven wireless communications into commercial, military, industrial, and personal systems. It is unreasonable to expect privacy and security to be inherent in these networks given the spatial density of these devices, limited spectral resources, and the broadcast nature of wireless communications systems. Communications for these systems must have sufficient information capacity and secrecy capacity while typically maintaining small size, light weight, and minimized power consumption. With increasing crowding of the electromagnetic spectrum, interference must be leveraged as an available resource. This work develops a new beamforming method for direction-dependent modulation that provides wireless communications devices with enhanced physical layer security and the ability to simultaneously communicate and harvest energy by exploiting co-channel interference. We propose a method that optimizes a set of time-varying array steering vectors to enable direction-dependent modulation, thus exploiting a new degree of freedom in the space-time-frequency paradigm. We formulate steering vector selection as a convex optimization problem for rapid computation given arbitrarily positioned array antenna elements. We show that this method allows us to spectrally separate co-channel interference from an information-bearing signal in the analog domain, enabling the energy from the interference to be diverted for harvesting during the digitization and decoding of the information-bearing signal. We also show that this method provides wireless communications devices with not only enhanced information capacity, but also enhanced secrecy capacity in a broadcast channel. By using the proposed method, we can increase the overall channel capacity in a broadcast system beyond the current state-of-the-art for wireless broadcast channels, which is based on static coding techniques. Further, we also increase the overall secrecy capacity of the system by enabling secrecy for each user in the system. In practical terms, this results in higher-rate, confidential messages delivered to multiple devices in a broadcast channel for a given power constraint. Finally, we corroborate these claims with simulation and experimental results for the proposed method. / PHD

Shannon's classical separation result holds only in the limit of infinite source code dimension and infinite channel code block length. In addition, Shannon theory does not address the design of good source codes when the probability of channel error is nonzero, which is inevitable for finite-length channel codes. Thus, for practical systems, a joint source and channel code design could improve performance for finite dimension source code and finite block length channel code, as well as complexity and delay. Consider a multicast system over a broadcast channel, where different end users typically have different capacities. To support such user or capacity diversity, it is desirable to encode the source to be broadcasted into a scalable bit stream along which multiple resolutions of the source can be reconstructed progressively from left to right. Such source coding technique is called multiresolution source coding. In wireless communications, joint source channel coding (JSCC) has attracted wide attention due to its adaptivity to time-varying channels. However, there are few works on joint source channel coding for network multicast, especially for the optimal source coding over broadcast channels. In this work, we aim at designing and analyzing the optimal multiresolution vector quantization (MRVQ) in conjunction with the subsequent broadcast channel over which the coded scalable bit stream would be transmitted. By adopting random index assignment (RIA) to link MRVQ for the source with superposition coding for the broadcast channel, we establish a closed-form formula of end-to-end distortion for a tandem system of MRVQ and a broadcast channel. From this formula we analyze the intrinsic structure of end-to-end distortion (EED) in a communication system and derive two necessary conditions for optimal multiresolution vector quantization over broadcast channels with random index assignment. According to the two necessary conditions, we propose a greedy iterative algorithm for jointly designed MRVQ with channel conditions, which depends on the channel only through several types of average channel error probabilities rather than the complete knowledge of the channel. Experiments show that MRVQ designed by the proposed algorithm significantly outperforms conventional MRVQ designed without channel information. By building an closed-form formula for the weighted EED with RIA, it also makes the computational complexity incurred during the performance analysis feasible. In comparison with MRVQ design for a fixed index assignment, the computation complexity for quantization design is significantly reduced by using random index assignment. In addition, simulations indicate that our proposed algorithm shows better robustness against channel mismatch than MRVQ design with a fixed index assignment, simply due to the nature of using only the average channel information. Therefore, we conclude that our proposed algorithm is more appropriate in both wireless communications and applications where the complete knowledge of the channel is hard to obtain. Furthermore, we propose two novel algorithms for MRVQ over broadcast channels. One aims to optimize the two corresponding quantizers at two layers alternatively and iteratively, and the other applies under the constraint that each encoding cell is convex and contains the reconstruction point. Finally, we analyze the asymptotic performance of weighted EED for the optimal joint MRVQ. The asymptotic result provides a theoretically achievable quantizer performance level and sheds light on the design of the optimal MRVQ over broadcast channel from a different aspect.

Shannon's classical separation result holds only in the limit of infinite source code dimension and infinite channel code block length. In addition, Shannon theory does not address the design of good source codes when the probability of channel error is nonzero, which is inevitable for finite-length channel codes. Thus, for practical systems, a joint source and channel code design could improve performance for finite dimension source code and finite block length channel code, as well as complexity and delay. Consider a multicast system over a broadcast channel, where different end users typically have different capacities. To support such user or capacity diversity, it is desirable to encode the source to be broadcasted into a scalable bit stream along which multiple resolutions of the source can be reconstructed progressively from left to right. Such source coding technique is called multiresolution source coding. In wireless communications, joint source channel coding (JSCC) has attracted wide attention due to its adaptivity to time-varying channels. However, there are few works on joint source channel coding for network multicast, especially for the optimal source coding over broadcast channels. In this work, we aim at designing and analyzing the optimal multiresolution vector quantization (MRVQ) in conjunction with the subsequent broadcast channel over which the coded scalable bit stream would be transmitted. By adopting random index assignment (RIA) to link MRVQ for the source with superposition coding for the broadcast channel, we establish a closed-form formula of end-to-end distortion for a tandem system of MRVQ and a broadcast channel. From this formula we analyze the intrinsic structure of end-to-end distortion (EED) in a communication system and derive two necessary conditions for optimal multiresolution vector quantization over broadcast channels with random index assignment. According to the two necessary conditions, we propose a greedy iterative algorithm for jointly designed MRVQ with channel conditions, which depends on the channel only through several types of average channel error probabilities rather than the complete knowledge of the channel. Experiments show that MRVQ designed by the proposed algorithm significantly outperforms conventional MRVQ designed without channel information. By building an closed-form formula for the weighted EED with RIA, it also makes the computational complexity incurred during the performance analysis feasible. In comparison with MRVQ design for a fixed index assignment, the computation complexity for quantization design is significantly reduced by using random index assignment. In addition, simulations indicate that our proposed algorithm shows better robustness against channel mismatch than MRVQ design with a fixed index assignment, simply due to the nature of using only the average channel information. Therefore, we conclude that our proposed algorithm is more appropriate in both wireless communications and applications where the complete knowledge of the channel is hard to obtain. Furthermore, we propose two novel algorithms for MRVQ over broadcast channels. One aims to optimize the two corresponding quantizers at two layers alternatively and iteratively, and the other applies under the constraint that each encoding cell is convex and contains the reconstruction point. Finally, we analyze the asymptotic performance of weighted EED for the optimal joint MRVQ. The asymptotic result provides a theoretically achievable quantizer performance level and sheds light on the design of the optimal MRVQ over broadcast channel from a different aspect.

Multiple input multiple output (MIMO) systems are now a proven area in current and future telecommunications research. MIMO wireless channels, in which both the transmitter and receiver have multiple antennas, have been shown to provide high bandwidth efficiency. In this thesis, we cover MIMO communications technology with a focus on cellular systems and the MIMO broadcast channel (MIMO-BC). Our development of techniques and analysis for the MIMO-BC starts with a study of single user MIMO systems. One such single user technique is that of antenna selection. In this thesis, we discuss various flavours of antenna selection, with the focus on powerful, yet straightforward, norm-based algorithms. These algorithms are analyzed and the results of this analysis produce a powerful and flexible power scaling factor. This power scaling factor can be used to model the gains of norm-based antenna selection via a single signal-to-noise ratio (SNR)-based parameter. This provides a powerful tool for engineers interested in quickly seeing the effects of antenna selection on their systems. A novel low complexity power allocation scheme follows on from the selection algorithms. Named “Poor Man’s Waterfilling” (PMWF), this scheme can provide significant gains in low SNR systems with very little extra complexity compared to selection alone. We then compare a variety of algorithms for the MIMO-BC, ranging from selection to beamforming, to the optimal, yet complex, iterative waterfilling (ITWF) solution. In this thesis we show that certain algorithms perform better in different scenarios, based on whether there is shadow fading or not. A power scaling factor analysis is also performed on these systems. In the cases where the user’s link gains are widely varying, such as when shadowing and distance effects are present, user fairness is impaired when optimal and near optimal throughput occurs. This leads to a key problem in the MIMO-BC, the balance between user fairness and throughput performance. In an attempt to find a suitable balance between these two factors, we modify the ITWF algorithm by both introducing extra constraints and also by using a novel utility function approach. Both these methods prove to increase user fairness with only minor loss in throughput over the optimal systems. The introduction of MIMO systems to the cellular domain has been hampered by the effects of interference between the cells. In this thesis we move MIMO to the cellular domain, addressing the interference using two different methods. We first use power control, where the transmit power of the base station is controlled to optimize the overall system throughput. This leads to promising results using low complexity methods. Our second method is a novel method of collaboration between base stations. This collaboration transforms neighbouring cell sectors into macro-cells and this results in substantial increases in performance.

Delay is an important quality-of-service measure for the design of next-generation wireless networks. This dissertation considers the problem of delay-limited communication over block-fading channels, where the channel state information is available at the receiver but not at the transmitter. For this communication scenario, the difference between the ergodic capacity and the maximum achievable expected rate (the expected capacity) for coding over a finite number of coherent blocks represents a fundamental measure of the penalty incurred by the delay constraint. This dissertation introduces a notion of worst-case expected-capacity loss. Focusing on the slow-fading scenario (one-block delay), the worst-case additive and multiplicative expected-capacity losses are precisely characterized for the point-to- point fading channel. Extension to the problem of writing on fading paper is also considered, where both the ergodic capacity and the additive expected-capacity loss over one-block delay are characterized to within one bit per channel use. The problem with multiple-block delay is considerably more challenging. This dissertation presents two partial results. First, the expected capacity is precisely characterized for the point-to-point two-state fading channel with two-block delay. Second, the optimality of Gaussian superposition coding with indirect decoding is established for a two-parallel Gaussian broadcast channel with three receivers. Both results reveal some intrinsic complexity in characterizing the expected capacity with multiple-block delay.