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

Limited Feedback Techniques in Multiple Antenna Wireless Communication Systems

Multiple antenna systems provide spatial multiplexing and diversity benefits.These systems also offer beamforming and interference mitigation capabilities in single-user (SU) and multi-user (MU) scenarios, respectively. Although diversity can be achieved without channel state information (CSI) at the transmitter using space-time codes, the knowledge of instantaneous CSI at the transmitter is essential to the above mentioned gains. In frequency division duplexing (FDD) systems, limited feedback techniques are employed to obtain CSI at the transmitter from the receiver using a low-rate link. As a consequence, CSI acquired by the transmitter in such manner have errors due to channel estimation and codebook quantization at the receiver, resulting in performance degradation of multi-antenna systems. In this thesis, we examine CSI inaccuracies due to codebook quantization errors and investigate several other aspects of limited feedback in SU, MU and multicell wireless communication systems with various channel models. For SU multiple-input multiple-output (MIMO) systems, we examine the capacity loss using standard codebooks. In particular, we consider single-stream and two-stream MIMO transmissions and derive capacity loss expressions in terms of minimum squared chordal distance for various MIMO receivers. Through simulations, we investigate the impact of codebook quantization errors on the capacity performance in uncorrelated Rayleigh, spatially correlated Rayleigh and standardized MIMO channels. This work motivates the need of effective codebook design to reduce the codebook quantization errors in correlated channels. Subsequently, we explore the improvements in the design of codebooks in temporally and spatially correlated channels for MU multiple-input single-output (MISO) systems, by employing scaling and rotation techniques. These codebooks quantize instantaneous channel direction information (CDI) and are referred as differential codebooks in the thesis. We also propose various adaptive scaling techniques for differential codebooks where packing density of codewords in the differential codebook are altered according to the channel condition, in order to reduce the quantization errors. The proposed differential codebooks improve the spectral efficiency of the system by minimizing the codebook quantization errors in spatially and temporally correlated channels. Later, we broaden the scope to massive MISO systems and propose trellis coded quantization (TCQ) schemes to quantize CDI. Unlike conventional codebook approach, the TCQ scheme does not require exhaustive search to select an appropriate codeword, thus reducing computational complexity and memory requirement at the receiver. The proposed TCQ schemes yield significant performance improvements compared to the existing TCQ based limited feedback schemes in both temporally and spatially correlated channels. Finally, we investigate interference coordination for multicell MU MISO systems using regularized zero-forcing (RZF) precoding. We consider random vector quantization (RVQ) codebooks and uncorrelated Rayleigh channels. We derive expected SINR approximations for perfect CDI and RVQ codebook-based CDI. We also propose an adaptive bit allocation scheme which aims to minimize the network interference and moreover, improves the spectral efficiency compared to equal bit allocation and coordinated zero-forcing (ZF) based adaptive bit allocation schemes

Interference Management Algorithms for Multicell MIMO Systems

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 이광복.Intercell interference is one of the most challenging issues limiting the performance of cellular systems, especially when the spectrum is highly reused across cells.In multiple-input multiple-output (MIMO) systems, in particular, it has been reported that, in a multicell environment, the performance of spatial multiplexing is significantly degraded due to intercell interference. In this dissertation, we develop interference management algorithms for multicell MIMO systems. In the first part of this dissertation, an efficient user selection scheme for the downlink of multiuser MIMO systems is proposed in a multicell environment. In a multicell environment, the intercell interference is one of the most influential factors limiting the performance. Thus, a user selection scheme that considers intercell interference is essential to increase the sum rate. The proposed scheme is based on an interference aware precoding. It sequentially selects users such that the sum rate is maximized. In particular, we develop a simple incremental metric for the sum rate. The use of the derived metric enables a significant reduction in the computational complexity of the user selection process, as compared to the optimal exhaustive search. Numerical results show that the proposed scheme provides near-optimal performance with substantially reduced complexity. In the second part of this dissertation, we propose a one-shot (non-iterative) cooperative beamforming scheme for downlink multicell systems. Unlike previous noniterative beamforming schemes, the proposed cooperative beamforming strives to balance maximizing the desired signal power while minimizing the generated interference power to neighbors by maximizing the network-wide average sum rate. Based on the average sum rate analysis, we derive what we term a global selfishness that steers the egoistic-altruistic balance of the network to maximize average sum rate. The global selfishness enables an autonomous decision on the cooperative beamforming vector in each cell. The main advantage of our approach is that cooperative beamforming solutions are analytically derived not only for an ideal two-cell network scenario but also for a practical three-sectored cellular network scenario. The simulation results verify that the proposed one-shot cooperative beamforming outperforms other conventional non-iterative schemes especially in interference-limited regions, which implies that it is very effective for performance improvement of edge users. In the third part of this dissertation, we propose a distributed cell clustering algorithm for coordinated-multi-point (CoMP) system based on message passing algorithm. In 5G networks system, it is expected that a large number of base stations (BSs) serve simultaneously and BSs are deployed in a very high density. Because of this high density systems, the centralized coordination approaches typically lead to high computational burden for practical implementations. Moreover, the sum rate metric are all coupled and it is difficult to determine clusters of BSs. This motivates us to propose a distributed cell clustering scheme based on message passing algorithm. The simulation results verify that the proposed clustering algorithm outperform the conventional distributed algorithm and reduces computational burden compared to centralized clustering algorithms.Chapter 1 Introduction 1 1.1 Low Complexity User Selection Algorithm 1 1.2 Non-iterative Beamforming Scheme 2 1.3 Distributed Cell Clustering Algorithm Based on Message Passing 5 Chapter 2 Low Complexity User Selection Algorithm 7 2.1 System Model 7 2.2 Proposed User Selection Algorithm 10 2.2.1 User Selection Criterion 11 2.2.2 User Selection Algorithm 15 2.3 Numerical Results 18 Chapter 3 Non-iterative Beamforming Scheme 26 3.1 System Model 26 3.2 Proposed One-Shot Cooperative Beamforming 27 3.3 Determination of Global Selfishness 31 3.3.1 Ideal Two-cell Network Scenario 31 3.3.2 Practical Three-Sectored Cellular Network Scenario 36 3.3.3 Practical Cellular Network Scenario with Nt antennas 40 3.4 Numerical Results 43 Chapter 4 Distributed Cell Clustering Algorithm Based on Message Passing 53 4.1 System Model 53 4.2 Message Passing Algorithm 54 4.3 Numerical Results 57 Chapter 5 Conclusion 62 Bibliography 67 Abstract (In Korean) 73Docto

Esquemas de cooperação entre estações base para o LTE no sentido descendente

The explosive growth in wireless traffic and in the number of connected devices as smart phones or computers, are causing a dramatic increase in the levels of interference, which significantly degrades the capacity gains promised by the point-to-point multi input, multi output (MIMO) based techniques. Therefore, it is becoming increasingly clear that major new improvements in spectral efficiency of wireless networks will have to entail addressing intercell interference. So, there is a need for a new cellular architecture that can take these factors under consideration. It is in this context that LTE-Advanced arises. One of the most promising LTE-Advanced technology is Coordinated Multipoint (CoMP), which allows base stations to cooperate among them, in order to mitigate or eliminate the intercell interference and, by doing so, increase the system’s capacity. This thesis intends to study this concept, implementing some schemes that fall under the CoMP concept. In this thesis we consider a distributed precoded multicell approach, where the precoders are computed locally at each BS to mitigate the intercell interference. Two precoder are considered: distributed zero forcing (DZF) and distributed virtual signal-to-interference noise ratio (DVSINR) recently proposed. Then the system is further optimized by computing a power allocation algorithm over the subcarriers that minimizes the average bit error rate (BER). The considered algorithms are also evaluated under imperfect channel state information. A quantized version of the CSI associated to the different links between the BS and the UT is feedback from the UT to the BS. This information is then employed by the different BSs to perform the precoding design. A new DVSINR precoder explicitly designed under imperfect CSI is proposed. The proposed schemes were implemented considering the LTE specifications, and the results show that the considered precoders are efficiently to remove the interference even under imperfect CSI.O crescimento exponencial no tráfego de comunicações sem-fios e no número de dispositivos utilizados (smart phones, computadores portáteis, etc.) está a causar um aumento significativo nos níveis de interferência, que prejudicam significativamente os ganhos de capacidade assegurados pelas tecnologias baseadas em ligações ponto-a-ponto MIMO. Deste modo, torna-se cada vez mais necessário que os grandes aperfeiçoamentos na eficiência espectral de sistemas de comunicações sem-fios tenham em consideração a interferência entre células. De forma a tomar em consideração estes aspectos, uma nova arquitectura celular terá de ser desenvolvida. É assim, neste contexto, que surge o LTE-Advanced. Uma das tecnologias mais promissoras do LTE-Advanced é a Coordenação Multi-Ponto (CoMP), que permite que as estações base cooperem de modo a mitigar a interferência entre células e, deste modo, aumentar a capacidade do sistema. Esta dissertação pretende estudar este conceito, implementando para isso algumas técnicas que se enquadram no conceito do CoMP. Nesta dissertação iremos considerar a implementação de um sistema de pré-codificação em múltiplas células, em que os pré-codificadores são calculados em cada BS, de modo a mitigar a interferência entre células. São considerados dois pré-codificadores: Distributed Zero Forcing (DZF) e Distributed Virtual Signal-to-Interferance Noise Ratio (DVSINR), recentemente proposto. De seguida o sistema é optimizado com a introdução de algoritmos de alocação de potência entre as sub-portadoras com o objectivo de minimizar a taxa média de erros (BER). Os algoritmos considerados são também avaliados em situações em que a informação do estado do canal é imperfeita. Uma versão quantizada da CSI associada a cada uma das diferentes ligações entre as BS e os UT é assim enviada do UT para a BS. Esta informação é então utilizada para calcular os diferentes pré-codificadores em cada BS. Uma nova versão do pré-codificador DVSINR é proposta de modo a lidar com CSI imperfeito. Os esquemas propostos foram implementados considerandos especificações do LTE, e os resultados obtidos demonstram que os pré-codificadores removem de uma forma eficiente a interferência, mesmo em situações em que a CSI é imperfeita

Enabling Efficient Communications Over Millimeter Wave Massive MIMO Channels Using Hybrid Beamforming

The use of massive multiple-input multiple-output (MIMO) over millimeter wave (mmWave) channels is the new frontier for fulfilling the exigent requirements of next-generation wireless systems and solving the wireless network impending crunch. Massive MIMO systems and mmWave channels offer larger numbers of antennas, higher carrier frequencies, and wider signaling bandwidths. Unleashing the full potentials of these tremendous degrees of freedom (dimensions) hinges on the practical deployment of those technologies. Hybrid analog and digital beamforming is considered as a stepping-stone to the practical deployment of mmWave massive MIMO systems since it significantly reduces their operating and implementation costs, energy consumption, and system design complexity. The prevalence of adopting mmWave and massive MIMO technologies in next-generation wireless systems necessitates developing agile and cost-efficient hybrid beamforming solutions that match the various use-cases of these systems. In this thesis, we propose hybrid precoding and combining solutions that are tailored to the needs of these specific cases and account for the main limitations of hybrid processing. The proposed solutions leverage the sparsity and spatial correlation of mmWave massive MIMO channels to reduce the feedback overhead and computational complexity of hybrid processing. Real-time use-cases of next-generation wireless communication, including connected cars, virtual-reality/augmented-reality, and high definition video transmission, require high-capacity and low-latency wireless transmission. On the physical layer level, this entails adopting near capacity-achieving transmission schemes with very low computational delay. Motivated by this, we propose low-complexity hybrid precoding and combining schemes for massive MIMO systems with partially and fully-connected antenna array structures. Leveraging the disparity in the dimensionality of the analog and the digital processing matrices, we develop a two-stage channel diagonalization design approach in order to reduce the computational complexity of the hybrid precoding and combining while maintaining high spectral efficiency. Particularly, the analog processing stage is designed to maximize the antenna array gain in order to avoid performing computationally intensive operations such as matrix inversion and singular value decomposition in high dimensions. On the other hand, the low-dimensional digital processing stage is designed to maximize the spectral efficiency of the systems. Computational complexity analysis shows that the proposed schemes offer significant savings compared to prior works where asymptotic computational complexity reductions ranging between $80\%$ and $98\%$. Simulation results validate that the spectral efficiency of the proposed schemes is near-optimal where in certain scenarios the signal-to-noise-ratio (SNR) gap to the optimal fully-digital spectral efficiency is less than $1$ dB. On the other hand, integrating mmWave and massive MIMO into the cellular use-cases requires adopting hybrid beamforming schemes that utilize limited channel state information at the transmitter (CSIT) in order to adapt the transmitted signals to the current channel. This is so mainly because obtaining perfect CSIT in frequency division duplexing (FDD) architecture, which dominates the cellular systems, poses serious concerns due to its large training and excessive feedback overhead. Motivated by this, we develop low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency (RF) analog precoders from statistically skewed DFT-based codebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to interference noise ratio for the single-user and multi-user cases, respectively. Mathematical analysis shows that the proposed algorithms are asymptotically optimal as the number of transmit antennas goes to infinity and the mmWave channel has a limited number of paths. Moreover, it shows that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the mmWave channel or equivalently the transmit channel covariance matrix when only the statistical channel knowledge is available at the transmitter. Further, we verify the performance of the proposed algorithms numerically where the obtained results illustrate that the spectral efficiency of the proposed algorithms can approach that of the optimal precoder in certain scenarios. Furthermore, these results illustrate that the proposed hybrid precoding schemes have superior spectral efficiency performance while requiring lower (or at most comparable) channel feedback overhead in comparison with the prior art

D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies

This document provides the most recent updates on the technical contributions and research challenges focused in WP3. Each Technology Component (TeC) has been evaluated under possible uniform assessment framework of WP3 which is based on the simulation guidelines of WP6. The performance assessment is supported by the simulation results which are in their mature and stable state. An update on the Most Promising Technology Approaches (MPTAs) and their associated TeCs is the main focus of this document. Based on the input of all the TeCs in WP3, a consolidated view of WP3 on the role of multinode/multi-antenna transmission technologies in 5G systems has also been provided. This consolidated view is further supported in this document by the presentation of the impact of MPTAs on METIS scenarios and the addressed METIS goals.Aziz, D.; Baracca, P.; De Carvalho, E.; Fantini, R.; Rajatheva, N.; Popovski, P.; Sørensen, JH.... (2015). D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies. http://hdl.handle.net/10251/7675

Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book