Analysis and Mitigation of Channel Non-Reciprocity in TDD MIMO Systems

The ever-growing demands for higher number of connected devices as well as higher data rates and more energy efficient wireless communications have necessitated the use of new technical solutions. One of the main enablers in this respect is Multiple-Input Multiple-Output (MIMO) systems in which transmitting and receiving sides are equipped with multiple antennas. Such systems need precise information of the MIMO radio channel available at the transmitter side to reach their full potential. Owing to the reciprocity of uplink and downlink channels in Time Division Duplexing (TDD) systems, Base Stations (BSs) may acquire the required channel state information for downlink transmission by processing the received uplink pilots. However, such reciprocity only applies to the physical propagation channels and does not take into consideration the so-called observable or effective uplink and downlink channels which also include the possible non-reciprocal behavior of the involved transceiver circuits and antenna systems. This thesis focuses on the channel non-reciprocity problem in TDD MIMO systems due to mismatches in Frequency Response (FR) and mutual coupling of transmitting and receiving chains of transceivers and associated antenna systems. The emphasis in the work and developments is placed on multi-user MIMO precoded downlink transmission. In this respect, the harmful impacts of channel non-reciprocity on the performance of such downlink transmission are analyzed. Additionally, non-reciprocity mitigation methods are developed seeking to reclaim TDD reciprocity and thus to avoid the involved performance degradations. Firstly, the focus is on the small-scale MIMO systems where BSs are equipped with relatively limited number of antennas, say in the order of 4 to 8. The provided analysis on Zero-Forcing (ZF) and eigen-based precoding schemes in single-cell scenario shows that both schemes experience considerable performance degradations in the presence of FR and mutual coupling mismatches. Whereas, in general, the system performance is more sensitive to i) non-reciprocity sources in the BS transceiver; and ii) mutual coupling mismatches. Then, assuming reasonably good antenna isolation, an Over-The-Air (OTA) pilot-based algorithm is proposed to efficiently mitigate the BS transceiver non-reciprocity. The numerical results indicate high accuracy in estimating the BS transceiver non- reciprocity parameters as well as considerable improvement in the performance of the system. In multi-cell scenario, both centralized and decentralized precoding approaches are covered while the focus is on the impacts of FR mismatches of UE transceivers. The how that there is severe degradation in the performance of decentralized precoding while centralized precoding is immune to such channel non-reciprocity impacts. Secondly, the so-called massive MIMO systems are considered in which the number of antennas in the BS side is increased with an order of magnitude or more. Based on the detailed developed signal models, closed-form analytical expressions are first provided for effective signal-to-interference-plus-noise ratios of both ZF and maximum ratio transmission precoding schemes. The analysis covers the joint impacts of channel non-reciprocity and imperfect uplink channel estimation and shows that while both precoding schemes suffer from channel non-reciprocity impacts, ZF is more sensitive to such non-idealities. Next, a concept and an algorithm are proposed, involving UE side measurements and processing, to be deployed in the UE side to efficiently estimate the level of BS transceiver non-reciprocity. This enables the UEs to inform the BS about the optimum time to perform channel non-reciprocity mitigation round and thus improves the spectral efficiency. Finally, in order to mitigate channel non-reciprocity in massive MIMO systems, an efficient iterative OTA pilot-based algorithm is proposed which estimates and mitigates transceiver non-reciprocity impacts in both BS and UE sides. Compared to the state-of-the-art methods, the simulation results indicate substantial improvements in system spectral efficiency when the proposed method is being used. Overall, the analyses provided in this thesis can be used as valuable tools to better understand practical TDD MIMO systems which can be very helpful in designing such systems. Furthermore, the channel non-reciprocity mitigation methods proposed in this thesis can be deployed in practical TDD MIMO syst channel reciprocity and thus significantly increase the spectral efficiency

Uplink Sounding Reference Signal Coordination to Combat Pilot Contamination in 5G Massive MIMO

To guarantee the success of massive multiple-input multiple-output (MIMO), one of the main challenges to solve is the efficient management of pilot contamination. Allocation of fully orthogonal pilot sequences across the network would provide a solution to the problem, but the associated overhead would make this approach infeasible in practical systems. Ongoing fifth-generation (5G) standardisation activities are debating the amount of resources to be dedicated to the transmission of pilot sequences, focussing on uplink sounding reference signals (UL SRSs) design. In this paper, we extensively evaluate the performance of various UL SRS allocation strategies in practical deployments, shedding light on their strengths and weaknesses. Furthermore, we introduce a novel UL SRS fractional reuse (FR) scheme, denoted neighbour-aware FR (FR-NA). The proposed FR-NA generalizes the fixed reuse paradigm, and entails a tradeoff between i) aggressively sharing some UL SRS resources, and ii) protecting other UL SRS resources with the aim of relieving neighbouring BSs from pilot contamination. Said features result in a cell throughput improvement over both fixed reuse and state-of-the-art FR based on a cell-centric perspective

Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems

We propose a subspace constrained precoding scheme that exploits the spatial channel correlation structure in massive MIMO cellular systems to fully unleash the tremendous gain provided by massive antenna array with reduced channel state information (CSI) signaling overhead. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace control matrix. The inner precoder is adaptive to the local CSI at each BS for spatial multiplexing gain. The Tx subspace control is adaptive to the channel statistics for inter-cell interference mitigation and Quality of Service (QoS) optimization. Specifically, the Tx subspace control is formulated as a QoS optimization problem which involves an SINR chance constraint where the probability of each user's SINR not satisfying a service requirement must not exceed a given outage probability. Such chance constraint cannot be handled by the existing methods due to the two stage precoding structure. To tackle this, we propose a bi-convex approximation approach, which consists of three key ingredients: random matrix theory, chance constrained optimization and semidefinite relaxation. Then we propose an efficient algorithm to find the optimal solution of the resulting bi-convex approximation problem. Simulations show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication

Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin