Search

The future multiple-input multiple-output (MIMO) system design is demanding for ultra-reliable low-latency (URLLC) service as well as higher throughput. This brings challenges to MIMO downlink design in three ways. Firstly, to enable URLLC service, the single-shot reliability is of interest rather than the average throughput that was optimized in the conventional MIMO design. Secondly, to achieve higher throughput, the user antennas need to be increased. In this case, if the service-antennas are increased to keep a high ratio of transmit/receive antennas as in conventional massive-MIMO systems, the Rayleigh distance of the base station will increase. Then, the wireless channel is no longer independent and identically distributed (i.i.d.) Rayleigh fading. This leads to the design problem of extremely large aperture array (ELAA). Lastly, if the transmit-antennas are not increased, the MIMO structure becomes (near) symmetric, where the channel is extremely ill-conditioned. To handle this issue, nonlinear precoding (NLP) needs to be employed. However, current NLP techniques have serious scalability problem to support large number of data-streams. This thesis deals with each challenge mentioned above, respectively. To enable URLLC, a beamforming gain prediction approach, namely the Chernoff lower-bound (Cher-LB), is proposed to fulfill extreme single-shot reliability requirement when the transmitter has imperfect channel knowledge. Compared to the literature, the Cher-LB demonstrates good tightness. For ELAA systems, the investigation is focused on the network-ELAA, where multiple access points (APs) are placed on a line with equal distance. An optimal cluster size of APs is obtained to achieve the best beamforming gain. Using this optimal cluster size significantly improves the coverage of network-ELAA for both URLLC and eMBB services. For scalable NLP, the constellation-oriented perturbation (COP) is proposed to provide a better performance-complexity trade-off than the literature. Moreover, the complexity of COP is flexibly controlled and does not change with the MIMO size.

All-digital architectures enable taking full advantage of the large number of antennas that can be integrated into mmWave transceivers, with fully flexible beamforming that enables the number of simultaneous users K sharing the band to scale with the number of antennas N. The small carrier wavelength in these bands allows realization of antenna arrays with a large number of elements with a relatively small footprint, opening a path to truly massive Multiple Input Multiple Output (MIMO) systems. However, two key bottlenecks to realizing this potential are the cost/power consumption of Radio Frequency (RF) frontends at high carrier frequencies and the high complexity incurred in the digital baseband processing due to the large number of antennas. In this dissertation, we develop approaches for addressing these bottlenecks by adapting signal processing architectures and algorithms to hardware design considerations, while taking advantage of the unique characteristics of the mmWave band. We first develop an analytical model for the impact of nonlinearities such as RF amplifiers and Analog-to-Digital Converters (ADCs) on the performance of a mmWave massive MIMO uplink. We illustrate the utility of this model in providing specific guidelines for hardware design based on desired system-level perfor- mance. For example, the framework allows specification of the desired 1 dB compression point for RF amplifiers and the desired number of bits of ADC precision in order for the system outage at a target bit error rate to be below 5%. These hardware design prescriptions depend on coarse system-level parameters such as the number of antennas N,the number of simultaneous users K, and the maximum and minimum link distances to be supported. An important conclusion from the analytical framework is that hardware specifications can be substantially relaxed by reducing the load factor, defined as the ratio β = K/N. We then consider the problem of scaling digital signal processing in this regime. For a relatively small number of antennas and users, the Linear Minimum Mean Squared Error (MMSE) approach is a standard technique for handling multiuser interference at reasonable complexity. However, for fixed load factor β, the complexity of computing the LMMSE detector, as well as the complexity of using it for demodulation, grow polynomially with the number of antennas. We propose complexity reduction techniques that substantially improve scaling by taking advantage of the spatial sparsity of the mmWave channel. Specifically, we use a spatial Discrete Fourier transform (DFT) across antennas to create N discrete beams, transforming from antenna space to beamspace. We show that each user’s energy is concentrated in a small number of DFT bins in beamspace. Assuming ideal single path channels, we show that each user can be demodulated reliably using a local LMMSE detector which employs a beamspace window whose size does not scale with the number of antennas. The local LMMSE detector approaches the performance of standard LMMSE detection at substantially reduced complexity, and these performance-complexity tradeoffs become more favorable at lower system load factor β. For larger load factors, the beamspace window required by the local LMMSE detector increases, but we show that it is possible to scale well in such regimes by adding a layer of nonlinear interference cancellation on top of the local LMMSE receiver. Next, drawing on the duality between uplink linear multiuser detection and downlink linear precoding, we demonstrate the efficacy of beamspace techniques for linear precoding on the downlink, in order to reduce the interference seen by a given user due to signals directed at other users by the base station. Finally, we address the problem of simultaneous scaling of bandwidth and number of antennas. As bandwidth and hence symbol rate increase, the signal from a given user impinging on a large antenna array incurs a multi-symbol delay spread across the array, which smears the spatial frequency for each user across the band. We introduce a novel technique that combines DFTs in the spatial and time domains, together with an interpolation technique that limits the dispersion of spatial frequency across the band. We show that this results in significantly reduced complexity in computing LMMSE weights for uplink multiuser detection.

Abstract: Multiuser multiple-input multiple-output (MU-MIMO) antenna techniques and heterogeneous network (HetNet) layouts are two promising approaches to dramatically increase throughput in future cellular networks to meet the exploding demand for ever higher data rates. HetNets increase capacity of cellular systems by employing dense layouts of various types of base stations (BSs) in the coverage area. MU-MIMO techniques increase spectral efficiency by enabling spatial multiplexing of data streams transmitted to/from different users. However, effective MIMO spatial multiplexing is achievable only at relatively high signal-to-interference-plus-noise ratios (SINRs). To maximize their capacity, conventional cellular networks are designed to operate at low SINRs. Hence, mitigation of inter-cell interference is required to obtain the benefits of MIMO spatial multiplexing. The most promising approach to achieve it is network coordination. In this thesis, downlink transmission in a coordinated MU-MIMO HetNet is considered. We investigate the application of simulated annealing (SA) and particle swarm (PS) algorithms to perform user scheduling in a cluster of coordinated network nodes. Our proposed SA and PS algorithms are able to perform in discrete value search space to select users and determine their encoding order for various precoding methods. Moreover, a hybrid algorithm combining the traits of PS and a greedy scheduler is also proposed. We demonstrate that performance of the proposed algorithms, in terms of the achievable sum rate, is close to that of the optimal search at much lower complexity. To mitigate inter-cluster interference, we develop a rotating clustering scheme, which increases average achievable throughput to cluster-edge users. Considering two different cellular layouts, different rotating patterns of clusters are introduced and the performance of the network with the proposed clustering patterns is investigated. Our simulations demonstrate the effectiveness of the proposed cluster rotation approach and determine the speed of rotation, beyond which any further performance gains become negligible. Lastly, we investigate the behaviour of our proposed user scheduling algorithms with temporally correlated channel gains. We further develop our user scheduling algorithms to take advantage of temporal correlation of channel gains to improve their convergence rate and achievable throughput. Our user scheduling algorithms designed specifically for temporally correlated channels perform very close to the optimal exhaustive search at significantly reduced complexity.

Abstract: Current and future broadband cellular systems have to employ efficient techniques for the transmission and reception of high speed data. Equipping transmitters and receivers with multiple-antennas is a major step in this direction as it has the potential of providing a substantial spatial multiplexing gain. Unfortunately, interference from adjacent cells is an impediment to the spatial multiplexing gain promised by MIMO techniques. There exist solutions to mitigate the inter-cell interference in MIMO cellular systems, the most promising being coordinated multi-point (CoMP) transmission/reception (also known as network MIMO) and large-scale MIMO (also known as massive MIMO). The focus of this thesis is on multi-user MIMO techniques including precoding and user scheduling for large-scale and cooperative MIMO wireless systems. In this study, we design and analyze a near capacity-achieving non-linear precoding technique relying on vector perturbation (VP) along with a fair user scheduling algorithm for joint transmission network MIMO (usually operating in the frequency division duplex (FDD) mode). We consider practical conditions such as imperfect channel state information (CSI) due to the backhaul delay and per-base station (per-BS) power constraints. In addition, we propose an optimal VP technique minimizing the mean square error (MSE) of the received signal subject to per-BS power constraints. Although the array virtualization of network MIMO reduces the inter-cell interference to some extent (depending on the cluster size of coordinated BSs), the increase in transmit antenna array size is limited by the fading block length (coherence time of the radio channel). In the time division duplex (TDD) mode, the story is different thanks to the channel reciprocity. Massive MIMO or large-scale MIMO is a transmission/reception scheme for multi-cell MIMO, which works in the TDD mode and involves BSs, each with a large number of antennas, much larger than the number of users per cell. In this study, we design and analyze a non-linear precoding technique employing time domain VP (TDVP) for a large-scale (massive) MIMO system. To analyze the system we employ random matrix methods to avoid time-consuming Monte-Carlo simulations and get better insight into the problem. In addition, we propose a practical approach to mitigating pilot contamination for massive MIMO through a joint clustering and pilot reuse scheme. We propose pilot contamination precoding (PCP) as outer linear precoding prior to conventional precoding through a cooperative transmission scheme with three BSs involved in the coordination cluster.

Modern wireless transceivers use multiple transmit and/or receive antennas, higher order modulation and large bandwidth to satisfy high data rate requirements of voice, data and multimedia applications. As wireless systems become more complex, the need to make wireless transceivers more efficient, compact and cost effective becomes challenging. Attempt to design an effective cellular network has been receiving lots of attention and demand to serve more users in the network is increasing. Supporting more users cannot be achieved easily mainly due to multi-user interference. Cell collaboration, also known as downlink network MIMO, has been proven to suppress the adverse effects of inter-cell interference (ICI), and as a result, to improve the performance of cell edge users. Beamforming, also known as linear precoding, is a possible transmission strategy to reduce the interference in such networks, and thereby improve spectrum utilization.In this thesis, we propose three novel clustered linear precoding schemes applicable to network MIMO systems for different scenarios to enhance the quality of service and the edge user throughput. First, we assume there is full channel state information (CSI) available at the transmitter side, and using the concept of signal-to-leakage ratio (SLR), we develop a linear precoding scheme to increase the spectral efficiency. In the second part, we assume partial CSI at the transmitters, and design linear precoders with optimal power control that improve the sum-rate. In the third part of this thesis, we consider a precoder design when there is no CSI available at the transmitters. Finally, a comparison amongst these schemes is provided through numerical simulation.

Abstract: Interference is known as a major obstacle for the spectral efficiency increase promised by multiple-antenna techniques in cellular wireless communications. Recently, it has been shown that multi-cell coordination can mitigate interference and improve system performance dramatically. Hence, we concentrate on the downlink of multi-cell multiple-antenna (at both ends) wireless networks also known as network multiple-input multiple-output (MIMO) or coordinated multi-point (CoMP) transmission/reception systems. In multi-cell coordination, antennas from multiple base stations form a large MIMO system. Consequently, coordination comes with high signal processing overhead. In this dissertation we focus on reduced-complexity transmission and reception strategies in partially coordinated multi-cell systems, where the user data are partially shared between base stations. We first model partial coordination using MIMO interference channel with generalized linear constraints. Then, we investigate linear transmission strategies using this channel model. The contributions of this dissertation fall into the following categories of techniques: (i) Block diagonalization (multiple-antenna multi-user zero-forcing) transmit precoding under individual power constraints. (ii) Minimum mean square error (MMSE) linear precoding and equalization design; (iii) Worst-case robust precoding and equalization, where we consider imperfect channel state information available at the transmitter and receiver. Furthermore, our simulation setup accounts for realistic cellular parameters in evaluating the performance in multi-cell networks.

Wireless networks are increasingly interference-limited, which motivates the development of sophisticated interference management techniques. One recently discovered approach is interference alignment, which attains the maximum sum rate scaling (with signal-to-noise ratio) in many network configurations. Interference alignment is not yet well understood from an engineering perspective. Such design considerations include (i) partial rather than complete knowledge of channel state information, (ii) correlated channels, (iii) bursty packet-based network traffic that requires the frequent setup and tear down of sessions, and (iv) the spatial distribution and interaction of transmit/receive pairs. This dissertation aims to establish the benefits and limitations of interference alignment under these four considerations. The first contribution of this dissertation considers an isolated group of transmit/receiver pairs (a cluster) cooperating through interference alignment and derives the signal-to-interference-plus-noise ratio distribution at each receiver for each stream. This distribution is used to compare interference alignment to beamforming and spatial multiplexing (as examples of common transmission techniques) in terms of sum rate to identify potential switching points between them. This dissertation identifies such switching points and provides design recommendations based on severity of the correlation or the channel state information uncertainty. The second contribution considers transmitters that are not associated with any interference alignment cooperating group but want to use the channel. The goal is to retain the benefits of interference alignment amid interference from the out-of-cluster transmitters. This dissertation shows that when the out-of-cluster transmitters have enough antennas, they can access the channel without changing the performance of the interference alignment receivers. Furthermore, optimum transmit filters maximizing the sum rate of the out-of-cluster transmit/receive pairs are derived. When insufficient antennas exist at the out-of-cluster transmitters, several transmit filters that trade off complexity and sum rate performance are presented. The last contribution, in contrast to the first two, takes into account the impact of large scale fading and the spatial distribution of the transmit/receive pairs on interference alignment by deriving the transmission capacity in a decentralized clustered interference alignment network. Channel state information uncertainty and feedback overhead are considered and the optimum training period is derived. Transmission capacity of interference alignment is compared to spatial multiplexing to highlight the tradeoff between channel estimation accuracy and the inter-cluster interference; the closer the nodes to each other, the higher the channel estimation accuracy and the inter-cluster interference.

This thesis considers the multi-user multiple-input multiple-output (MIMO) broadcast channel. In the multi-user MIMO systems, multiple users are sent independent data streams in the same time slot and frequency band by a base station using spatial division multiple access (SDMA) which exploits the degrees of freedom offered by multiple transmit antennas. This simultaneous transmission to several users via SDMA creates multi-user interference at each receiver. An optimal transmission method to cancel the multi-user interference involves an appropriate scheduling of users and transmit processing (i.e.precoding) to achieve promised high capacity gains of a multi-user system over single user system. An optimal but computationally expensive transmission method which completely eliminates the multi-user interference is dirty-paper-coding (DPC). Alternatively, there are low complexity linear and non linear precoding methods which when combined with well designed user scheduling algorithms can provide good sum rate performance. Hence in this thesis we consider practical precoding methods which can achieve near-capacity performance and also propose several low-complexity scheduling algorithms. We focus on non-linear precoding scheme called vector perturbation precoding (VPP). We derive expressions for the sum rate in terms of the average energy of the precoded vector, and use this to derive a high signal-to-noise ratio (SNR) closed form upper bound. We then propose a low-complexity user selection algorithm that attempts to maximize the high-SNR sum rate upper bound. We also provide treatment of VPP under the presence of more practical conditions. Specifically, we generalize the system model to include: i) transmit side correlation, ii) presence of line-of-sight (LOS) component and, iii) spatial non-homogeneity among users. For each of these practical scenarios, we derive approximate the effective noise gain at the output of users' modulo demodulators which can in turn provide a good measure of system's sum rate performance. We also notice that although VPP provides near capacity sum rates at high-SNR but still there exists a gap between VPP and DPC at the low-SNR. In literature regularized VPP (R-VPP) is proposed to bridge this gap but analytical expressions for sum rate performance of R-VPP remains outstanding. We analyze the sum rate performance of R-VPP by first using an assumption that the received signal and interference are Gaussian. Then we propose a simpler approximation for sum rate based on the mean squared error (MSE). We also confirm that the R-VPP performs very close to DPC for the whole SNR range. We then turn our attention to another practical aspect of multi-user communication which is the design of multi-user MIMO systems when only a limited feedback of channel state information from users is available at the transmitter. Several scheduling algorithms with limited channel feedback are proposed when linear zero-forcing precoding is used at the transmitter. With little channel quality information (CQI) and channel direction information (CDI) at multi-antenna base station, multi-user scheduling algorithms are carefully designed in order to maximize the potential system throughput while reducing the feedback load. We first propose a scheduling algorithm which requires a quantized CDI and a threshold CQI. Then, we propose a scheduling which needs a limited feedback of a threshold CDI and a perfect CQI. Both scheduling algorithms are studied in terms of the achievable throughput and the feedback load. Analytical results show that the proposed scheduling with limited channel feedback has a small performance degradation compared to the scheduling with unlimited channel feedback when the thresholds for user scheduling are judiciously designed. Moreover, under a total bit rate constraint of the feedback channel, we propose a scheduling with the quantization of both CQI and CDI. It is demonstrated that allocating one bit to the CQI with an optimal threshold and the remaining bits to the CDI achieves the near optimal sum rate performance.

We consider the problem of practical communication over a doubly selective (DS)channel, i.e., a time and frequency selective channel. The problem is approached in two different ways: coherent communication and noncoherent communication, and for each communication scheme we propose practical and near-optimal equalizers and maximum-diversity precoders. Toward these ends, we adopt 1) basis expansion (BE) modeling of the channel, which allows for an efficient and unied way of describing a DS channel in both time and frequency domain; and 2) tree-search algorithms (TSAs), which facilitate near-optimal performance with low complexity.For practical coherent communication, we focus on the pulse-shaped (PS) multicarrier modulation (MCM), where controlled inter-symbol-interference (ISI) and inter-carrier-interference (ICI) can be leveraged for computationally efficient receiver structures. Then, we propose a novel channel adaptive TSA with a novel fast minimum mean-squared error (MMSE) generalized decision-feedback equalizer (GDFE) preprocessing, and a rank-reduced channel estimation by using the BE channel model. Also, a new finding about optimality of MMSE-GDFE preprocessing is presented, which states that under constant modulus constellation the minimum distance property is preserved by the MMSE-GDFE preprocessing.Then, two practically realizable noncoherent equalization schemes are proposed:a sequential algorithm and a Bayesian expectation maximization (EM)-based algorithm. The sequential algorithm is derived from the optimal noncoherent metric, and made practical by a fast algorithm and a TSA to evaluate and search over the metric. The Bayesian EM-based noncoherent algorithm is derived from optimal maximum a posteriori (MAP) estimation of the BE parameters, and efficiently implemented via iteration between soft coherent equalizer and soft channel estimator. Efficient operations are accomplished using fast algorithms whose overall complexities grow linearly in the block size and quadratically in the number of BE parameters. Also, we demonstrate that the noncoherent equalization can be readily applied to the communication problem in a highly spread underwater acoustic channel (UAC).Finally, we establish maximum-diversity conditions for each affine and linear precoder, which imply that under some mild channel assumptions almost any randomaffine (linear) precoder facilitates the maximum-diversity noncoherent (coherent) reception.

A primary design objective in next generation wireless systems is to make efficient use of available bandwidth while keeping error rates low. Multiple Input, Multiple Output (MIMO) systems have shown promise toward fulfilling this objective. Space-time codes spread data symbols in time and space in order to create redundancy, which improves bit error rates (BER) but does not improve capacity. Spatial multiplexing techniques decompose the matrix channel into subchannels. This improves capacity but the resulting systems are not optimized with respect to BER. Linear Dispersion Codes (LDCs) are linear codes which spread data symbols using dispersion matrices. These codes guarantee high spectral efficiency and have been shown to exhibit good error performance. LDC designs based on frame theory of wavelets explicitly optimize both capacity and error performance. Conventional space-time codes such as Orthogonal Space-Time Block Codes (OSTBC) and spatial multiplexing systems assume that the transmitter has no knowledge of the channel. In some cases, however, feedback from receiver to transmitter can be established to convey channel state information (CSI). Such systems are called precoding systems. In this dissertation, we propose Precoded Linear Dispersion Codes (P-LDCs), a family of precoders which assume that the receiver has perfect channel knowledge while the transmitter has statistical information about the transmit and receive correlation matrices. P-LDCs can be viewed as a unifying design strategy which utilizes the LDC structure to implement an optimal capacity-achieving transmit covariance strategy. Utilizing partial CSI, P-LDCs form a linear space-time precoder using dispersion matrices derived according to capacity optimality criteria. P-LDC dispersion matrices are designed according to a two-fold objective: maximize spectral efficiency, then minimize pairwise codeword error probability for high signal to noise ratio (SNR). Finally, we analyze frame-based LDCs and demonstrate that existing designs are subject to a capacity penalty under certain circumstances heretofore unexplored. We illustrate this capacity penalty and devise a structural constraint for the LDC precoder based on Grassmannian beamforming to reduce the possibility of this capacity penalty.

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.

Transmitter diversity in the downlink for Code Division Multiple Access (CDMA) systems provides a means to achieve similar performance gains as for the mobile station (MS) receiver diversity without the complexity of a MS receiver antenna array. Transmitter based methods enable to shift signal processing to the transmitter where power and computational complexity are more abundant, thus simplifying receiver units. We examine feasibility of several transmitter diversity techniques for the Wideband CDMA (W-CDMA) systems. We also investigate an optimal method to combine transmitter diversity and precoding that achieves the gain of maximum ratio combining of all space and frequency diversity branches. Under severe channel conditions (i.e. multipath), the multiple access interference (MAI) becomes the major source of performance degradation for direct sequence CDMA (DS/CDMA) systems. This is because of the loss of orthogonality between the spreading codes used by each user due to the multipath channel effects. To overcome this problem, many receiver based multiuser detection (MUD) techniques have been proposed. These techniques demand high computational complexity, power and knowledge of spreading codes of all users. As a result, in the downlink of a CDMA system it is not feasible to employ such methods at the MS. Alternatively, transmitter based techniques were proposed to shift computational complexity and power consumption to the BS, where they can be afforded. It was shown that these methods are very effective in removing the MAI. Although these methods are powerful, they are high in complexity and require the accurate knowledge of the channel. since MAI cancellation filters need to be updated continuously as fading coefficients vary. We propose a less complex method with similar performance improvements. In the proposed method, the functions of multipath combining and MAI cancellation are separated. Thus the MAI cancellation matrix does not depend on rapidly time-varying fading coefficients. Transmitter diversity and multiuser precoding can be combined to further improve the performance. Multiuser precoding preserves the multipath diversity while removing the MAI. Extending multiuser precoding to multiple antennas results in space diversity in addition to multipath diversity. Both transmitter diversity and multiuser precoding require the knowledge of the channel state information (CSI). The CSI can be estimated at the receiver and sent to the transmitter via a feedback channel. To enable the studied adaptive techniques for practical systems, we employ the long range prediction (LRP) algorithm, which characterizes the fading channel using the autoregressive (AR) model and computes the Minimum Mean Squared Error (MMSE) estimate of a future fading coefficient sample based on a number of past observations. Numerical, simulation and theoretical results are presented to show that transmitter diversity and multiuser precoding can be used to remove MAI and achieve frequency and space diversity through multipath channels and multiple antennas.