Pilot Contamination and Mitigation Techniques in Massive MIMO Systems

A multi-antenna base station (BS) can spatially multiplex a few terminals over the same bandwidth, a technique known as multi-user, multiple-input multiple-output (MU-MIMO). A new idea in cellular MU-MIMO is the use of a large excess of BS antennas to serve several single-antenna terminals simultaneously. This so-called "massive MIMO" promises attractive gains in spectral efficiency with time-division duplex operation. Within a cell, the BS estimates the channel from mutually orthogonal reverse-link pilot sequences to formulate a receiver for the reverse link and (assuming reciprocity) a precoder for the forward link. The channel coherence is typically constrained in time as well as frequency, leading to a trade-off between the resources spent on pilots and those available for data symbols. This pilot overhead can be reduced by reusing pilot sequences in nearby cells, however this potentially introduces interference in the channel estimation phase, the so-called "pilot contamination" effect. In this thesis, we study the impact of pilot contamination in realistic environments and investigate schemes to mitigate it. We evaluate the mean squared error (MSE) of channel estimates in case of a plain-vanilla least-squares (LS) estimator and a minimum MSE (MMSE) estimator that exploits prior knowledge of second-order channel statistics. Next, we introduce a pilot open-loop power control (pilot OLPC) scheme to improve the SINR-fairness of received pilot signals at the BS. We evaluate the effect of relaxing the pilot reuse factor and also implement a soft pilot reuse (SPR) scheme to distribute pilot sequences efficiently. To study the trade-off between pilot and data symbols, we evaluate the achievable rate in forward link with maximum-ratio and zero-forcing precoding at the BS. We evaluate an inter-cell coordination scheme that exploits prior knowledge of all cross-channel covariance matrices to reuse pilots among spatially well-separated terminals. We simulate a 21-cell MU-MIMO setup with up to 100-antenna BSs and up to 24 single-antenna terminals per cell in an outdoor urban macro environment. We find that pilot reuse 1 causes severe impairment of the channel estimates, which can be improved with pilot OLPC. Pilot reuse 1/3 effectively mitigates pilot contamination, and can improve the achievable rate in the forward link. SPR also mitigates contamination but with a smaller increase in pilot overhead. Inter-cell coordinated pilot allocation, implemented using a greedy approach, provides gains over random allocation only for the initial few pilots. In general, maximum ratio precoding is more robust against pilot contamination than zero-forcing.A multi-antenna base station (BS) can be used to improve cellular communication performance. The signal at each antenna can be designed in a way that it increases received energy at the desired terminals, and attenuates it at other locations (reducing interference). This technique can be used to serve several terminals over the same time and frequency using independent data streams, known as multi-user MIMO (MU-MIMO). In this thesis, we investigate MU-MIMO approach for very large BS antenna arrays, also called massive MIMO. The performance of such systems depends critically on the quality of channel estimates the BS. We simulate realistic channel conditions in a multi-cell setup, which gives rise to interference during channel estimation. We evaluate the system performance in terms of quality of BS channel estimates and the achievable data rate within a cell. We evaluate different techniques for channel estimation, and for generating data streams from the BS to the terminals. Next, we evaluate schemes to improve the channel estimation. We conclude by noting the trade-offs involved in the various schemes and the conditions under which certain schemes might provide performance improvements

Baseband Processing for 5G and Beyond: Algorithms, VLSI Architectures, and Co-design

In recent years the number of connected devices and the demand for high data-rates have been significantly increased. This enormous growth is more pronounced by the introduction of the Internet of things (IoT) in which several devices are interconnected to exchange data for various applications like smart homes and smart cities. Moreover, new applications such as eHealth, autonomous vehicles, and connected ambulances set new demands on the reliability, latency, and data-rate of wireless communication systems, pushing forward technology developments. Massive multiple-input multiple-output (MIMO) is a technology, which is employed in the 5G standard, offering the benefits to fulfill these requirements. In massive MIMO systems, base station (BS) is equipped with a very large number of antennas, serving several users equipments (UEs) simultaneously in the same time and frequency resource. The high spatial multiplexing in massive MIMO systems, improves the data rate, energy and spectral efficiencies as well as the link reliability of wireless communication systems. The link reliability can be further improved by employing channel coding technique. Spatially coupled serially concatenated codes (SC-SCCs) are promising channel coding schemes, which can meet the high-reliability demands of wireless communication systems beyond 5G (B5G). Given the close-to-capacity error correction performance and the potential to implement a high-throughput decoder, this class of code can be a good candidate for wireless systems B5G. In order to achieve the above-mentioned advantages, sophisticated algorithms are required, which impose challenges on the baseband signal processing. In case of massive MIMO systems, the processing is much more computationally intensive and the size of required memory to store channel data is increased significantly compared to conventional MIMO systems, which are due to the large size of the channel state information (CSI) matrix. In addition to the high computational complexity, meeting latency requirements is also crucial. Similarly, the decoding-performance gain of SC-SCCs also do come at the expense of increased implementation complexity. Moreover, selecting the proper choice of design parameters, decoding algorithm, and architecture will be challenging, since spatial coupling provides new degrees of freedom in code design, and therefore the design space becomes huge. The focus of this thesis is to perform co-optimization in different design levels to address the aforementioned challenges/requirements. To this end, we employ system-level characteristics to develop efficient algorithms and architectures for the following functional blocks of digital baseband processing. First, we present a fast Fourier transform (FFT), an inverse FFT (IFFT), and corresponding reordering scheme, which can significantly reduce the latency of orthogonal frequency-division multiplexing (OFDM) demodulation and modulation as well as the size of reordering memory. The corresponding VLSI architectures along with the application specific integrated circuit (ASIC) implementation results in a 28 nm CMOS technology are introduced. In case of a 2048-point FFT/IFFT, the proposed design leads to 42% reduction in the latency and size of reordering memory. Second, we propose a low-complexity massive MIMO detection scheme. The key idea is to exploit channel sparsity to reduce the size of CSI matrix and eventually perform linear detection followed by a non-linear post-processing in angular domain using the compressed CSI matrix. The VLSI architecture for a massive MIMO with 128 BS antennas and 16 UEs along with the synthesis results in a 28 nm technology are presented. As a result, the proposed scheme reduces the complexity and required memory by 35%–73% compared to traditional detectors while it has better detection performance. Finally, we perform a comprehensive design space exploration for the SC-SCCs to investigate the effect of different design parameters on decoding performance, latency, complexity, and hardware cost. Then, we develop different decoding algorithms for the SC-SCCs and discuss the associated decoding performance and complexity. Also, several high-level VLSI architectures along with the corresponding synthesis results in a 12 nm process are presented, and various design tradeoffs are provided for these decoding schemes

Energy efficiency and interference management in long term evolution-advanced networks.

Doctoral Degree. University of KwaZulu-Natal, Durban.Cellular networks are continuously undergoing fast extraordinary evolution to overcome technological challenges. The fourth generation (4G) or Long Term Evolution-Advanced (LTE-Advanced) networks offer improvements in performance through increase in network density, while allowing self-organisation and self-healing. The LTE-Advanced architecture is heterogeneous, consisting of different radio access technologies (RATs), such as macrocell, smallcells, cooperative relay nodes (RNs), having various capabilities, and coexisting in the same geographical coverage area. These network improvements come with different challenges that affect users’ quality of service (QoS) and network performance. These challenges include; interference management, high energy consumption and poor coverage of marginal users. Hence, developing mitigation schemes for these identified challenges is the focus of this thesis. The exponential growth of mobile broadband data usage and poor networks’ performance along the cell edges, result in a large increase of the energy consumption for both base stations (BSs) and users. This due to improper RN placement or deployment that creates severe inter-cell and intracell interferences in the networks. It is therefore, necessary to investigate appropriate RN placement techniques which offer efficient coverage extension while reducing energy consumption and mitigating interference in LTE-Advanced femtocell networks. This work proposes energy efficient and optimal RN placement (EEORNP) algorithm based on greedy algorithm to assure improved and effective coverage extension. The performance of the proposed algorithm is investigated in terms of coverage percentage and number of RN needed to cover marginalised users and found to outperform other RN placement schemes. Transceiver design has gained importance as one of the effective tools of interference management. Centralised transceiver design techniques have been used to improve network performance for LTE-Advanced networks in terms of mean square error (MSE), bit error rate (BER) and sum-rate. The centralised transceiver design techniques are not effective and computationally feasible for distributed cooperative heterogeneous networks, the systems considered in this thesis. This work proposes decentralised transceivers design based on the least-square (LS) and minimum MSE (MMSE) pilot-aided channel estimations for interference management in uplink LTE-Advanced femtocell networks. The decentralised transceiver algorithms are designed for the femtocells, the macrocell user equipments (MUEs), RNs and the cell edge macrocell UEs (CUEs) in the half-duplex cooperative relaying systems. The BER performances of the proposed algorithms with the effect of channel estimation are investigated. Finally, the EE optimisation is investigated in half-duplex multi-user multiple-input multiple-output (MU-MIMO) relay systems. The EE optimisation is divided into sub-optimal EE problems due to the distributed architecture of the MU-MIMO relay systems. The decentralised approach is employed to design the transceivers such as MUEs, CUEs, RN and femtocells for the different sub-optimal EE problems. The EE objective functions are formulated as convex optimisation problems subject to the QoS and transmit powers constraints in case of perfect channel state information (CSI). The non-convexity of the formulated EE optimisation problems is surmounted by introducing the EE parameter substractive function into each proposed algorithms. These EE parameters are updated using the Dinkelbach’s algorithm. The EE optimisation of the proposed algorithms is achieved after finding the optimal transceivers where the unknown interference terms in the transmit signals are designed with the zero-forcing (ZF) assumption and estimation errors are added to improve the EE performances. With the aid of simulation results, the performance of the proposed decentralised schemes are derived in terms of average EE evaluation and found to be better than existing algorithms

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Energy-Efficient and Robust Hybrid Analog-Digital Precoding for Massive MIMO Systems

The fifth-generation (5G) and future cellular networks are expected to facilitate wireless communication among tens of billions of devices with enormously high data rate and ultra-high reliability. At the same time, these networks are required to embrace green technology by significantly improving the energy efficiency of wireless communication to reduce their carbon footprint. The massive multiple-input multiple-output (MIMO) systems, in which the base stations are equipped with hundreds of antenna elements, can provide immensely high data rates and support a large number of users by employing the precoding at the base stations. However, the conventional precoding techniques - which require a dedicated radio-frequency chain for each antenna element - become prohibitively expensive for massive MIMO systems. To address this shortcoming, the hybrid analog-digital precoding architecture is proposed, which requires fewer radio-frequency chains than the antenna elements. The reduced hardware costs in this novel architecture, however, comes at the expense of reduced degrees of freedom for the precoding, which deteriorates the energy efficiency of the network. In this thesis, we consider the design of energy-efficient hybrid precoding techniques in multiuser downlink massive MIMO systems. These systems are fundamentally interference limited. To mitigate the interference, we adopt two interference management strategies while designing the hybrid precoding schemes. They are, namely, interference suppression-based hybrid precoding, and interference exploitation-based hybrid precoding. The former approach results in a lower computational complexity - as the resulting precoders remain the same as long as the channel is unchanged when compared to the latter approach. On the other hand, the interference exploitation-based hybrid precoding is more energy efficient due to judicious use of transmit symbol information, as compared to the interference suppression-based hybrid precoding. In the hybrid analog-digital precoding, analog precoders are implemented in analog radio-frequency domain using a large number of phase shifters, which are relatively inexpensive. These phase shifters, however, typically suffer from artifacts; their actual values differ from their nominal values. These imperfect phase shifters can lead to symbol estimation errors at the users, which may not be tolerable in many applications of future cellular networks. To establish a high-reliable communication under the plight of imperfect phase shifters in the hybrid precoding architecture, in this thesis, we propose an energy-efficient, robust hybrid precoding technique. The designed scheme guarantees 100% robustness against the considered hardware artifacts. Moreover, the thesis demonstrates that the proposed technique can save up to 12% transmit power when compared to a conventional method. Another critically important requirement of the future cellular networks - apart from ultra-high reliability and energy efficiency - is ultra-low latency. Some envisioned extreme real-time applications of 5G, such as autonomous driving and remote surgery, demand an end-to-end latency smaller than one millisecond. To fulfill such a stringent demand, we devise an efficient implementation scheme for the proposed robust hybrid precoding technique to reduce the required computational time. The devised scheme exploits special structures present in the algorithm to reduce the computational complexity and can compute the precoders in a distributed manner on a parallel hardware architecture. The results show that the proposed implementation scheme can reduce the average computation time of the algorithm by 35% when compared to a state-of-the-art method. Finally, we consider the hybrid precoding in heterogeneous networks, where the cell edge users typically experience severe interference. We propose a coordinated hybrid precoding technique based on the interference exploitation approach. The numerical results reveal that the proposed coordinated hybrid precoding results in a significant transmit power savings when compared to the uncoordinated hybrid precoding

Massive Multi-Antenna Communications with Low-Resolution Data Converters

Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in future cellular communication systems. In massive MU-MIMO systems, the number of antennas at the base station (BS) is scaled up by several orders of magnitude compared to traditional multi-antenna systems with the goals of enabling large gains in capacity and energy efficiency. However, scaling up the number of active antenna elements at the BS will lead to significant increases in power consumption and system costs unless power-efficient and low-cost hardware components are used. In this thesis, we investigate the performance of massive MU-MIMO systems for the case when the BS is equipped with low-resolution data converters.First, we consider the massive MU-MIMO uplink for the case when the BS uses low-resolution analog-to-digital converters (ADCs) to convert the received signal into the digital domain. Our focus is on the case where neither the transmitter nor the receiver have any a priori channel state information (CSI), which implies that the channel realizations have to be learned through pilot transmission followed by BS-side channel estimation, based on coarsely quantized observations. We derive a low-complexity channel estimator and present lower bounds and closed-form approximations for the information-theoretic rates achievable with the proposed channel estimator together with conventional linear detection algorithms. Second, we consider the massive MU-MIMO downlink for the case when the BS uses low-resolution digital-to-analog converters (DACs) to generate the transmit signal. We derive lower bounds and closed-form approximations for the achievable rates with linear precoding under the assumption that the BS has access to perfect CSI. We also propose novel nonlinear precoding algorithms that are shown to significantly outperform linear precoding for the extreme case of 1-bit DACs. Specifically, for the case of symbol-rate 1-bit DACs and frequency-flat channels, we develop a multitude of nonlinear precoders that trade between performance and complexity. We then extend the most promising nonlinear precoders to the case of oversampling 1-bit DACs and orthogonal frequency-division multiplexing for operation over frequency-selective channels.Third, we extend our analysis to take into account other hardware imperfections such as nonlinear amplifiers and local oscillators with phase noise.The results in this thesis suggest that the resolution of the ADCs and DACs in massive MU-MIMO systems can be reduced significantly compared to what is used in today\u27s state-of-the-art communication systems, without significantly reducing the overall system performance

On the performance of hybrid beamforming for millimeter wave wireless networks

The phenomenal growth in the demand for mobile wireless data services is pushing the boundaries of modern communication networks. Developing new technologies that can provide unprecedented data rates to support the pervasive and exponentially increasing demand is therefore of prime importance in wireless communications. In existing communication systems, physical layer techniques are commonly used to improve capacity. Nevertheless, the limited available resources in the spectrum are unable to scale up, fundamentally restricting further capacity increase. Consequently, alternative approaches which exploit both unused and underutilised spectrum bands are highly attractive. This thesis investigates the use of the millimeter wave (mmWave) spectrum as it has the potential to provide unlimited bandwidth to wireless communication systems. As a first step toward realising mmWave wireless communications, a cloud radio access network using mmWave technology in the fronthaul and access links is proposed to establish a feasible architecture for deploying mmWave systems with hybrid beamforming. Within the context of a multi-user communication system, an analytical framework of the downlink transmission is presented, providing insights on how to navigate across the challenges associated with high-frequency transmissions. The performance of each user is measured by deriving outage probability, average latency and throughput in both noise-limited and interference-limited scenarios. Further analysis of the system is carried out for two possible user association configurations. By relying on large antenna array deployment in highly dense networks, this architecture is able to achieve reduced outages with very low latencies, making it ideal to support a growing number of users. The second part of this work describes a novel two-stage optimisation algorithm for obtaining hybrid precoders and combiners that maximise the energy efficiency (EE) of a general multi-user mmWave multiple-input, multiple-output (MIMO) interference channel network involving internet of things (IoT) devices. The hybrid transceiver design problem considers both perfect and imperfect channel state information (CSI). In the first stage, the original non-convex multivariate EE maximization problem is transformed into an equivalent univariate problem and the optimal single beamformers are then obtained by exploiting the correlation between parametric and fractional programming problems and the relationship between weighted sum rate (WSR) and weighted minimum mean squared error (WMMSE) problems. The second stage involves the use of an orthogonal matching pursuit (OMP)-based algorithm to obtain the energy-efficient hybrid beamformers. This approach produces results comparable to the optimal beam-forming strategy but with much lower complexity, and further validates the use of mmWave networks in practice to support the demand from ubiquitous power-constrained smart devices. In the third part, the focus is on the more practical scenario of imperfect CSI for multi-user mmWave systems. Following the success of hybrid beamforming for mmWave wireless communication, a non-traditional transmission strategy called Rate Splitting (RS) is investigated in conjunction with hybrid beamforming to tackle the residual multi-user interference (MUI) caused by errors in the estimated channel. Using this technique, the transmitted signal is split into a common message and a private message with the transmitted power dynamically divided between the two parts to ensure that there is interference-free transmission of the common message. An alternating maximisation algorithm is proposed to obtain the optimal common precoder. Simulation results show that the RS transmission scheme is beneficial to multi-user mmWave transmissions as it enables remarkable rate gains over the traditional linear transmission methods. Finally, the fourth part analyses the spectral efficiency (SE) performance of a mmWave system with hybrid beamforming whilst accounting for real-life practice transceiver hardware impairments. An investigation is conducted into three major hardware impairments, namely, the multiplicative phase noise (PN), the amplified thermal noise (ATN) and the residual additive transceiver hardware impairments (RATHI). The hybrid precoder is designed to maximise the SE by the minimisation of the Euclidean distance between the optimal digital precoder and the noisy product of the hybrid precoders while the hybrid combiners are designed by the minimisation of the mean square error (MSE) between the transmitted and received signals. Multiplicative PN was found to be the most critical of the three impairments considered. It was observed that the additive impairments could be neglected for low signal-to-noise-ratio (SNR) while the ATNs caused a steady degradation to the SE performance