Resource Allocation in Multi-user MIMO Networks: Interference Management and Cooperative Communications

Nowadays, wireless communications are becoming so tightly integrated in our daily lives, especially with the global spread of laptops, tablets and smartphones. This has paved the way to dramatically increasing wireless network dimensions in terms of subscribers and amount of flowing data. Therefore, the two important fundamental requirements for the future 5G wireless networks are abilities to support high data traffic and exceedingly low latency. A likely candidate to fulfill these requirements is multicell multi-user multi-input multiple-output (MU-MIMO); also termed as coordinated multi-point (CoMP) transmission and reception. To achieve the highest possible performance in MU-MIMO networks, a properly designed resource allocation algorithm is needed. Moreover, with the rapidly growing data traffic, interference has become a major limitation in wireless networks. Interference alignment (IA) has been shown to significantly manage the interference and improve the network performance. However, how practically use IA to mitigate interference in a downlink MU-MIMO network still remains an open problem. In this dissertation, we improve the performance of MU-MIMO networks in terms of spectral efficiency, by designing and developing new beamforming algorithms that can efficiently mitigate the interference and allocate the resources. Then we mathematically analyze the performance improvement of MUMIMO networks employing proposed techniques. Fundamental relationships between network parameters and the network performance is revealed, which provide guidance on the wireless networks design. Finally, system level simulations are conducted to investigate the performance of the proposed strategies

Precoding Schemes for Millimeter Wave Massive MIMO Systems

In an effort to cut high cost and power consumption of radio frequency (RF) chains, millimeter wave (mmWave) multiple input multiple output (MIMO) deploys hybrid architecture in which precoding is implemented as a combination of digital precoding and analog precoding, accomplished by using a smaller number of RF chains and a network of phase shifters respectively. The mmWave MIMO, which usually suffers from blockages, needs to be supported by Reconfigurable Intelligent Surface (RIS) to make communication possible. Along with the hybrid precoding in mmWave MIMO, the passive precoding of Reconfigurable Intelligent Surface (RIS) is investigated in a downlink RIS-assisted mmWave MIMO. The hybrid precoding and passive precoding are challenged by the unit modulus constraints on the elements of analog precoding matrix and passive precoding vector. The coupling of analog and digital precoders further complicates the hybrid precoding. One of the approaches taken in proposed hybrid precoding algorithms is the use of alternating optimization in which analog precoder and digital precoder are optimized alternately keeping the other fixed. Analog precoder is determined by solving a semidefinite programming problem, and from the unconstrained least squares solution during each iteration. In another approach taken in the proposed methods, the hybrid precoding is split into separate analog and digital precoding subproblems. The analog precoding subproblems are simplified using some approximations, and solved by using iterative power method and employing a truncated singular value decomposition method in two different hybrid precoding algorithms. In the prooposed codebook-based precoder, analog precoder is constructed by choosing precoding vectors from a codebook to maximize signal-to-leakage-and-noise ratio (SLNR). The passive precoding at the RIS in a single user MIMO is designed to minimize mean square error between the transmit signal and the estimate of received signal by using an iterative algorithm that solves the joint optimization problem of precoding, passive precoding and combiner. The problem of designing energy efficient RIS is solved by maximizing energy efficiency which is a joint optimization problem involving precoder, passive precoding matrix and power allocation matrix. The proposed hybrid precoding and passive precoding algorithms deliver very good performances and prove to be computationally efficient

Interference Alignment and Cancellation in Wireless Communication Systems

The Shannon capacity of wireless networks has a fundamental importance for network information theory. This area has recently seen remarkable progress on a variety of problems including the capacity of interference networks, X networks, cellular networks, cooperative communication networks and cognitive radio networks. While each communication scenario has its own characteristics, a common reason of these recent developments is the new idea of interference alignment. The idea of interference alignment is to consolidate the interference into smaller dimensions of signal space at each receiver and use the remaining dimensions to transmit the desired signals without any interference. However, perfect alignment of interference requires certain assumptions, such as perfect channel state information at transmitter and receiver, perfect synchronization and feedback. Today’s wireless communication systems, on the other and, do not encounter such ideal conditions. In this thesis, we cover a breadth of topics of interference alignment and cancellation schemes in wireless communication systems such as multihop relay networks, multicell networks as well as cooperation and optimisation in such systems. Our main contributions in this thesis can be summarised as follows: • We derive analytical expressions for an interference alignment scheme in a multihop relay network with imperfect channel state information, and investigate the impact of interference on such systems where interference could accumulate due to the misalignment at each hop. • We also address the dimensionality problem in larger wireless communication systems such as multi-cellular systems. We propose precoding schemes based on maximising signal power over interference and noise. We show that these precoding vectors would dramatically improve the rates for multi-user cellular networks in both uplink and downlink, without requiring an excessive number of dimensions. Furthermore, we investigate how to improve the receivers which can mitigate interference more efficiently. • We also propose partial cooperation in an interference alignment and cancellation scheme. This enables us to assess the merits of varying mixture of cooperative and non-cooperative users and the gains achievable while reducing the overhead of channel estimation. In addition to this, we analytically derive expressions for the additional interference caused by imperfect channel estimation in such cooperative systems. We also show the impact of imperfect channel estimation on cooperation gains. • Furthermore, we propose jointly optimisation of interference alignment and cancellation for multi-user multi-cellular networks in both uplink and downlink. We find the optimum set of transceivers which minimise the mean square error at each base station. We demonstrate that optimised transceivers can outperform existing interference alignment and cancellation schemes. • Finally, we consider power adaptation and user selection schemes. The simulation results indicate that user selection and power adaptation techniques based on estimated rates can improve the overall system performance significantly

MIMO designs for filter bank multicarrier and multiantenna systems based on OQAM

From the perspective of increasingly data rate requirements in mobile communications, it is deemed necessary to do further research so that the future goals can be reached. To that end, the radio-based communications are resorting to multicarrier modulations and spatial diversity. Until today, the orthogonal frequency division multiplexing (OFDM) modulation is regarded as the dominant technology. On one hand, the OFDM modulation is able to accommodate multiantenna configurations in a very straightforward manner. On the other hand, the poor stopband attenuation exhibited by the OFDM modulation, highlights that a definitely tight synchronization is required. In addition, the cyclic prefix (CP) has to be sufficiently long to avoid inter-block interference, which may substantially reduce the spectral efficiency. In order to overcome the OFDM drawbacks, the filter bank multicarrier modulation based on OQAM (FBMC/OQAM) is introduced. This modulation does not need any CP and benefits from pulse shaping techniques. This aspect becomes crucial in cognitive radio networks and communication systems where nodes are unlikely to be synchronized. In principle, the poor frequency confinement exhibited by OFDM should tip the balance towards FBMC/OQAM. However, the perfect reconstruction property of FBMC/OQAM systems does not hold in presence of multipath fading. This means that the FBMC/OQAM modulation is affected by inter-symbol and inter-carrier interference, unless the channel is equalized to some extent. This observation highlights that the FBMC/OQAM extension to MIMO architectures becomes a big challenge due to the need to cope with both modulation- and multiantenna-induced interference. The goal of this thesis is to study how the FBMC/OQAM modulation scheme can benefit from the degrees of freedom provided by the spatial dimension. In this regard, the first attempt to put the research on track is based on designing signal processing techniques at reception. In this case the emphasis is on single-input-multiple-output (SIMO) architectures. Next, the possibility of pre-equalizing the channel at transmission is investigated. It is considered that multiple antennas are placed at the transmit side giving rise to a multiple-input-single-output (MISO) configuration. In this scenario, the research is not only focused on counteracting the channel but also on distributing the power among subcarriers. Finally, the joint transmitter and receiver design in multiple-input-multiple-output (MIMO) communication systems is covered. From the theory developed in this thesis, it is possible to conclude that the techniques originally devised in the OFDM context can be easily adapted to FBMC/OQAM systems if the channel frequency response is flat within the subchannels. However, metrics such as the peak to average power ratio or the sensitivity to the carrier frequency offset constraint the number of subcarriers, so that the frequency selectivity may be appreciable at the subcarrier level. Then, the flat fading assumption is not satisfied and the specificities of FBMC/OQAM systems have to be considered. In this situation, the proposed techniques allow FBMC/OQAM to remain competitive with OFDM. In addition, for some multiantenna configurations and propagation conditions FBMC/OQAM turns out to be the best choice. The simulation-based results together with the theoretical analysis conducted in this thesis contribute to make progress towards the application of FBMC/OQAM to MIMO channels. The signal processing techniques that are described in this dissertation allow designers to exploit the potentials of FBMC/OQAM and MIMO to improve the link reliability as well as the spectral efficiency

Distributed precoding systems in multi-gateway multibeam satellites: regularization and coarse beamforming

This paper deals with the problem of beamforming design in a multibeam satellite, which is shared by different groups of terminals -clusters-, each served by an Earth station or gateway. Each gateway precodes the symbols addressed to its respective users; the design follows an MMSE criterion, and a regularization factor judiciously chosen allows to account for the presence of mutually interfering clusters, extending more classical results applicable to one centralized station. More importantly, channel statistics can be used instead of instantaneous channel state information, avoiding the exchange of information among gateways through backhaul links. The on-board satellite beamforming weights are designed to exploit the degrees of freedom of the satellite antennas to minimize the noise impact and the interference to some specific users. On-ground beamforming results are provided as a reference to compare the joint performance of MMSE precoders and on-board beamforming network.Agencia Estatal de Investigación | Ref. TEC2016-76409-C2-2-RAgencia Estatal de Investigación | Ref. TEC2016-75103-C2-2-RXunta de Galici

Secure Rate Splitting Multiple Access: How Much of the Split Signal to Reveal?

Rate Splitting Multiple Access (RSMA) relies on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receiver. In RS the users’ messages are split into a common message and private messages, where the common part is first decoded by the all users, while the private part is decoded only by the intended user using SIC technique. This split of the users’ signals into common and private parts raises some interesting tradeoffs between maximizing sum rate versus secrecy rate. In this work we consider the secrecy performance of RSMA in multi-user multiple-input single-output (MU-MISO) systems, where secrecy is defined by the ability of any user to decode the signal intended for user k in the system. To that end, new analytical expressions for the ergodic sum-rate and ergodic secrecy rate are derived for two closed-form precoding techniques of the private messages, namely, 1) zero-forcing (ZF) precoding approach, 2) minimum mean square error (MMSE) approach. Then, based on the analytical expressions of the ergodic rates, novel power allocation strategies that maximize the sum-rate subject to a target secrecy rate for the two precoding schemes are presented and investigated. Our Monte Carlo simulations show a close match with our theoretical derivations. They also reveal that, by tuning the split of the messages, our power allocation approaches provide a scalable tradeoff between rate benefits and secrecy

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

Radio Resource Management for New Application Scenarios in 5G: Optimization and Deep Learning

The fifth-generation (5G) New Radio (NR) systems are expected to support a wide range of emerging applications with diverse Quality-of-Service (QoS) requirements. New application scenarios in 5G NR include enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communications (URLLC). New wireless architectures, such as full-dimension (FD) massive multiple-input multiple-output (MIMO) and mobile edge computing (MEC) system, and new coding scheme, such as short block-length channel coding, are envisioned as enablers of QoS requirements for 5G NR applications. Resource management in these new wireless architectures is crucial in guaranteeing the QoS requirements of 5G NR systems. The traditional optimization problems, such as subcarriers and user association, are usually non-convex or Non-deterministic Polynomial-time (NP)-hard. It is time-consuming and computing-expensive to find the optimal solution, especially in a large-scale network. To solve these problems, one approach is to design a low-complexity algorithm with near optimal performance. In some cases, the low complexity algorithms are hard to obtain, deep learning can be used as an accurate approximator that maps environment parameters, such as the channel state information and traffic state, to the optimal solutions. In this thesis, we design low-complexity optimization algorithms, and deep learning frameworks in different architectures of 5G NR to resolve optimization problems subject to QoS requirements. First, we propose a low-complexity algorithm for a joint cooperative beamforming and user association problem for eMBB in 5G NR to maximize the network capacity. Next, we propose a deep learning (DL) framework to optimize user association, resource allocation, and offloading probabilities for delay-tolerant services and URLLC in 5G NR. Finally, we address the issue of time-varying traffic and network conditions on resource management in 5G NR