Massive MIMO Multicasting in Noncooperative Cellular Networks

We study the massive multiple-input multiple-output (MIMO) multicast transmission in cellular networks where each base station (BS) is equipped with a large-scale antenna array and transmits a common message using a single beamformer to multiple mobile users. We first show that when each BS knows the perfect channel state information (CSI) of its own served users, the asymptotically optimal beamformer at each BS is a linear combination of the channel vectors of its multicast users. Moreover, the optimal combining coefficients are obtained in closed form. Then we consider the imperfect CSI scenario where the CSI is obtained through uplink channel estimation in timedivision duplex systems. We propose a new pilot scheme that estimates the composite channel which is a linear combination of the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The pilot power control for optimizing the multicast beamformer at each BS is also derived. Numerical results show that the asymptotic performance of the proposed scheme is close to the ideal case with perfect CSI. Simulation also verifies the effectiveness of the proposed scheme with finite number of antennas at each BS.Comment: to appear in IEEE JSAC Special Issue on 5G Wireless Communication System

Higher-rank Transmit Beamforming Using Space Time Block Coding

With the rapid development of wireless communications, there has been a massive growth in the number of wireless communications users and progressively more new high data rate wireless services will emerge. With these developments taking place, wireless spectral resources are becoming much more scarce and precious. As a result, research on spectrally efficient transmission techniques for current and future communication networks attracts considerable interest. As a promising multi-antenna communication technique, transmit beamforming is widely recognized as being able to improve the capacity of wireless systems without requiring additional spectral resources. In conventional (rank-one) beamforming, each user is served by a single beamformer. For certain transmit beamforming applications, the beamforming performance may be poor if the degrees of freedom in the conventional beamformer design become insufficient. The scope of this thesis is to address the beamforming performance degradation problems induced by the insufficient degrees of freedom in the beamformer design in certain practical scenarios. In this thesis, a fundamentally new idea of higher-rank (>1) transmit beamforming is proposed to improve the beamforming performance. Instead of a single beamformer assigned to each user, multiple beamformers are designed and correspondingly the degrees of freedom in the beamformer design are multiplied, i.e., the increase of the degrees of freedom consists in the increase of the number of design variables. To implement higher-rank beamforming, the central idea is to combine beamforming with different space time block coding (STBC) techniques. Conventionally, STBCs are used to exploit the transmit diversity resulting from the independent fading for different transmit antennas. However, the use of STBCs in the higher-rank beamforming approaches is not for the sake of transmit diversity, but for the sake of design diversity in the sense of degrees of freedom in the beamformer design. The single-group multicast beamforming problem of broadcasting the same information to all users is firstly considered in the thesis. It is assumed that the transmitter knows the instantaneous channel state information (CSI) which describes the short-term channel conditions of a communication link and can be estimated in modern communication systems. In the conventional approach, a single beamforming weight vector is designed to steer the common information to all users. In the case of a large number of users, the performance of the conventional approach usually degrades severely due to the limited degrees of freedom offered by a single beamformer. In order to mitigate this drawback, a rank-two beamforming approach is proposed in which two independent beamforming weight vectors are designed. In the rank-two beamforming approach, single-group multicast beamforming is combined with the two dimentional Alamouti STBC, and each user is simultaneously served with two Alamouti coded symbols from two beamformers. The degrees of freedom in the beamformer design are doubled and significant performance improvement is achieved. The multi-group multicast beamforming problem of transmitting the same information to users in the same group while transmitting independent information to users in different groups, is studied next in the thesis, also assuming that instantaneous CSI is available at the transmitter. The rank-two beamforming approach, originally devised for single-group multicasting networks that are free of multiuser interference, is extended to multi-group multicasting networks, where multiuser interference represents a major challenge. By combining multi-group multicast beamforming with Alamouti STBC, two independent beamforming weight vectors are assigned to each user and the degrees of freedom in the beamformer design are doubled resulting in drastically improved beamforming performance. Then, the multiuser downlink beamforming problem of delivering independent information to different users with additional shaping constraints is investigated in the thesis, also assuming instantaneous CSI at the transmitter. Additional shaping constraints are used to incorporate a variety of requirements in diverse applications. When the number of shaping constraints is large, the degrees of freedom in the beamformer design can be rather deficient. In order to address this problem, a general rank beamforming approach is proposed in which multiuser downlink beamforming is combined with high dimensional (>2) real-valued orthogonal space time block coding (OSTBC). In the general rank beamforming approach, the number of beamforming weight vectors for each user and the associated degrees of freedom in the beamformer design are multiplied by up to eight times, which lead to significantly increased flexibility for the beamformer design. Since instantaneous CSI can be difficult to acquire in certain scenarios, the use of statistical CSI describing the long-term statistical characteristics of the channel can be more practical in these scenarios. The rank-two beamformer designs based on instantaneous CSI can be straightforwardly applied in the case of statistical CSI. However, it is impossible to extend the general rank beamforming approach for the multiuser downlink beamforming problem with additional shaping constraints based on instantaneous CSI to the case of statistical CSI straightforwardly. Therefore, multiuser downlink beamforming with additional shaping constraints using statistical CSI at the transmitter is then studied and an alternative general rank beamforming approach is proposed in the thesis. In the general rank beamforming approach using statistical CSI, multiuser downlink beamforming is combined with quasi-orthogonal space time block coding (QOSTBC). The increased number of beamforming weight vectors and the associated degrees of freedom are much beyond the limits that can be achieved by Alamouti STBC in the beamformer design. Simulation results demonstrate that the proposed higher-rank transmit beamforming approaches can achieve significantly improved performance as compared to the existing approaches

Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A State-of-the-art, Classification and Challenges

Precoding has been conventionally considered as an effective means of mitigating or exploiting the interference in the multiantenna downlink channel, where multiple users are simultaneously served with independent information over the same channel resources. The early works in this area were focused on transmitting an individual information stream to each user by constructing weighted linear combinations of symbol blocks (codewords). However, more recent works have moved beyond this traditional view by: i) transmitting distinct data streams to groups of users and ii) applying precoding on a symbol-per-symbol basis. In this context, the current survey presents a unified view and classification of precoding techniques with respect to two main axes: i) the switching rate of the precoding weights, leading to the classes of block-level and symbol-level precoding, ii) the number of users that each stream is addressed to, hence unicast, multicast, and broadcast precoding. Furthermore, the classified techniques are compared through representative numerical results to demonstrate their relative performance and uncover fundamental insights. Finally, a list of open theoretical problems and practical challenges are presented to inspire further research in this area

Non-convex Quadratically Constrained Quadratic Programming: Hidden Convexity, Scalable Approximation and Applications

University of Minnesota Ph.D. dissertation. September 2017. Major: Electrical Engineering. Advisor: Nicholas Sidiropoulos. 1 computer file (PDF); viii, 85 pages.Quadratically Constrained Quadratic Programming (QCQP) constitutes a class of computationally hard optimization problems that have a broad spectrum of applications in wireless communications, networking, signal processing, power systems, and other areas. The QCQP problem is known to be NP–hard in its general form; only in certain special cases can it be solved to global optimality in polynomial-time. Such cases are said to be convex in a hidden way, and the task of identifying them remains an active area of research. Meanwhile, relatively few methods are known to be effective for general QCQP problems. The prevailing approach of Semidefinite Relaxation (SDR) is computationally expensive, and often fails to work for general non-convex QCQP problems. Other methods based on Successive Convex Approximation (SCA) require initialization from a feasible point, which is NP-hard to compute in general. This dissertation focuses on both of the above mentioned aspects of non-convex QCQP. In the first part of this work, we consider the special case of QCQP with Toeplitz-Hermitian quadratic forms and establish that it possesses hidden convexity, which makes it possible to obtain globally optimal solutions in polynomial-time. The second part of this dissertation introduces a framework for efficiently computing feasible solutions of general quadratic feasibility problems. While an approximation framework known as Feasible Point Pursuit-Successive Convex Approximation (FPP-SCA) was recently proposed for this task, with considerable empirical success, it remains unsuitable for application on large-scale problems. This work is primarily focused on speeding and scaling up these approximation schemes to enable dealing with large-scale problems. For this purpose, we reformulate the feasibility criteria employed by FPP-SCA for minimizing constraint violations in the form of non-smooth, non-convex penalty functions. We demonstrate that by employing judicious approximation of the penalty functions, we obtain problem formulations which are well suited for the application of first-order methods (FOMs). The appeal of using FOMs lies in the fact that they are capable of efficiently exploiting various forms of problem structure while being computationally lightweight. This endows our approximation algorithms the ability to scale well with problem dimension. Specific applications in wireless communications and power grid system optimization considered to illustrate the efficacy of our FOM based approximation schemes. Our experimental results reveal the surprising effectiveness of FOMs for this class of hard optimization problems

Joint optimization of transmit beamforming and base station cache allocation in multi-cell C-RAN

Cloud radio access network (C-RAN) with wireless backhaul has been considered as a possible solution for small cell deployment. Limited capacity of wireless backhaul encourages people to equip the base stations (BSs) with cache storages to mitigate peak-time data traffic. We study a downlink C-RAN consisting of a central processor (CP) and multiple cache-enabled BSs connected to the CP through wireless backhaul links. Through joint optimization of beamforming and cache size allocation, we aim to distribute cache sizes among BSs such that long-term delivery time of files is minimized. Assuming zero-forcing (ZF) beamforming is adopted at CP, cache size allocation problem becomes convex which can be solved by convex optimization techniques. Simulation results show that loss of optimality due to ZF assumption is negligible for large antenna sizes at CP. Moreover, the superiority of our proposed caching scheme over several heuristic ones, including proportional caching, is demonstrated by simulation

Advanced Symbol-level Precoding Schemes for Interference Exploitation in Multi-antenna Multi-user Wireless Communications

The utilization of multi-antenna transmitters relying on full frequency reuse has proven to be an effective strategy towards fulfilling the constantly increasing throughput requirements of wireless communication systems. As a consequence, in the last two decades precoding has been a prolific research area, due to its ability to handle the interference arising among simultaneous transmissions addressed to different co-channel users. The conventional precoding strategies aim at mitigating the multi-user interference (MUI) by exploiting the knowledge of the channel state information (CSI). More recently, novel approaches have been proposed where the aim is not to eliminate the interference, but rather to control it so as to achieve a constructive interference effect at each receiver. In these schemes, referred to as symbol-level precoding (SLP), the data information (data symbols) is used together with the CSI in the precoding design, which can be addressed following several optimization strategies. In the context of SLP, the work carried out in this thesis is mainly focused on developing more advanced optimization strategies suitable to non-linear systems, where the per-antenna high-power amplifiers introduce an amplitude and phase distortion on the transmitted signals. More specifically, the main objective is to exploit the potential of SLP not only to achieve the constructive interference at the receivers, but also to control the per-antenna instantaneous transmit power, improving the power dynamics of the transmitted waveforms. In fact, a reduction of the power variation of the signals, both in the spatial dimension (across the different antennas) and in the temporal dimension, is particularly important for mitigating the non-linear effects. After a detailed review of the state of the art of SLP, the first part of the thesis is focused on improving the power dynamics of the transmitted signals in the spatial dimension, by reducing the instantaneous power imbalances across the different antennas. First, a SLP per-antenna power minimization scheme is presented, followed by a related max-min fair formulation with per-antenna power constraints. These approaches allow to reduce the power peaks of the signals across the antennas. Next, more advanced SLP schemes are formulated and solved, with the objective of further improving the spatial dynamics of the signals. Specifically, a first approach performs a peak power minimization under a lower bound constraint on the per-antenna transmit power, while a second strategy minimizes the spatial peak-to-average power ratio. The second part of this thesis is devoted to developing a novel SLP method, referred to as spatio-temporal SLP, where the temporal variation of the transmit power is also considered in the SLP optimization. This new model allows to minimize the peak-to-average power ratio of the transmitted waveforms both in the spatial and in the temporal dimensions, thus further improving the robustness of the signals to non-linear effects. Then, this thesis takes one step further, by exploiting the developed spatio-temporal SLP model in a different context. In particular, a spatio-temporal SLP scheme is proposed which enables faster-than-Nyquist (FTN) signaling over multi-user systems, by constructively handling at the transmitter side not only the MUI but also the inter-symbol interference (ISI). This strategy allows to benefit from the increased throughput provided by FTN signaling without imposing additional complexity at the user terminals. Extensive numerical results are presented throughout the thesis, in order to assess the performance of the proposed schemes with respect to the state of the art in SLP. The thesis concludes summarizing the main research findings and identifying the open problems, which will constitute the basis for the future work