On the Performance of Physically Constrained Multi-Pair Two-Way Massive MIMO Relaying with Zero Forcing

In this paper, we consider a physically constrained multi-pair two-way massive multiple-input multiple-output (MIMO) decode-and-forward (DF) half-duplex relay system, where multiple single-antenna user pairs exchange information through a massive MIMO relay, and we employ zero-forcing reception/zero-forcing transmission (ZFR/ZFT) at the relay. When the number of relay antennas M becomes very large and tends to be infinite, we study the large-scale approximation of the sum spectral efficiency (SE) with the effect of spatial correlation generated by the constrained space. Furthermore, we investigate the energy efficiency (EE) with a practical power consumption model, and demonstrate the impact of the relay antenna number and the size of constrained space on the EE performance

Coordinated Multi-cell Beamforming for Massive MIMO: A Random Matrix Approach

We consider the problem of coordinated multi- cell downlink beamforming in massive multiple input multiple output (MIMO) systems consisting of N cells, Nt antennas per base station (BS) and K user terminals (UTs) per cell. Specifically, we formulate a multi-cell beamforming algorithm for massive MIMO systems which requires limited amount of information exchange between the BSs. The design objective is to minimize the aggregate transmit power across all the BSs subject to satisfying the user signal to interference noise ratio (SINR) constraints. The algorithm requires the BSs to exchange parameters which can be computed solely based on the channel statistics rather than the instantaneous CSI. We make use of tools from random matrix theory to formulate the decentralized algorithm. We also characterize a lower bound on the set of target SINR values for which the decentralized multi-cell beamforming algorithm is feasible. We further show that the performance of our algorithm asymptotically matches the performance of the centralized algorithm with full CSI sharing. While the original result focuses on minimizing the aggregate transmit power across all the BSs, we formulate a heuristic extension of this algorithm to incorporate a practical constraint in multi-cell systems, namely the individual BS transmit power constraints. Finally, we investigate the impact of imperfect CSI and pilot contamination effect on the performance of the decentralized algorithm, and propose a heuristic extension of the algorithm to accommodate these issues. Simulation results illustrate that our algorithm closely satisfies the target SINR constraints and achieves minimum power in the regime of massive MIMO systems. In addition, it also provides substantial power savings as compared to zero-forcing beamforming when the number of antennas per BS is of the same orders of magnitude as the number of UTs per cell

Power Allocation in Uplink NOMA-Aided Massive MIMO Systems

In the development of the fifth-generation (5G) as well as the vision for the future generations of wireless communications networks, massive multiple-input multiple-output (MIMO) technology has played an increasingly important role as a key enabler to meet the growing demand for very high data throughput. By equipping base stations (BSs) with hundreds to thousands antennas, the massive MIMO technology is capable of simultaneously serving multiple users in the same time-frequency resources with simple linear signal processing in both the downlink (DL) and uplink (UL) transmissions. Thanks to the asymptotically orthogonal property of users' wireless channels, the simple linear signal processing can effectively mitigate inter-user interference and noise while boosting the desired signal's gain, and hence achieves high data throughput. In order to realize this orthogonal property in a practical system, one critical requirement in the massive MIMO technology is to have the instantaneous channel state information (CSI), which is acquired via channel estimation with pilot signaling. Unfortunately, the connection capability of a conventional massive MIMO system is strictly limited by the time resource spent for channel estimation. Attempting to serve more users beyond the limit may result in a phenomenon known as pilot contamination, which causes correlated interference, lowers signal gain and hence, severely degrades the system's performance. A natural question is ``Is it at all possible to serve more users beyond the limit of a conventional massive MIMO system?''. The main contribution of this thesis is to provide a promising solution by integrating the concept of nonorthogonal multiple access (NOMA) into a massive MIMO system. The key concept of NOMA is based on assigning each unit of orthogonal radio resources, such as frequency carriers, time slots or spreading codes, to more than one user and utilize a non-linear signal processing technique like successive interference cancellation (SIC) or dirty paper coding (DPC) to mitigate inter-user interference. In a massive MIMO system, pilot sequences are also orthogonal resources, which can be allocated with the NOMA approach. By sharing a pilot sequence to more than one user and utilizing the SIC technique, a massive MIMO system can serve more users with a fixed amount of time spent for channel estimation. However, as a consequence of pilot reuse, correlated interference becomes the main challenge that limits the spectral efficiency (SE) of a massive MIMO-NOMA system. To address this issue, this thesis focuses on how to mitigate correlated interference when combining NOMA into a massive MIMO system in order to accommodate a higher number of wireless users. In the first part, we consider the problem of SIC in a single-cell massive MIMO system in order to serve twice the number of users with the aid of time-offset pilots. With the proposed time-offset pilots, users are divided into two groups and the uplink pilots from one group are transmitted simultaneously with the uplink data of the other group, which allows the system to accommodate more users for a given number of pilots. Successive interference cancellation is developed to ease the effect of pilot contamination and enhance data detection. In the second part, the work is extended to a cell-free network, where there is no cell boundary and a user can be served by multiple base stations. The chapter focuses on the NOMA approach for sharing pilot sequences among users. Unlike the conventional cell-free massive MIMO-NOMA systems in which the UL signals from different access points are equally combined over the backhaul network, we first develop an optimal backhaul combining (OBC) method to maximize the UL signal-to-interference-plus-noise ratio (SINR). It is shown that, by using OBC, the correlated interference can be effectively mitigated if the number of users assigned to each pilot sequence is less than or equal to the number of base stations. As a result, the cell-free massive MIMO-NOMA system with OBC can enjoy unlimited performance when the number of antennas at each BS tends to infinity. Finally, we investigate the impact of imperfect SIC to a NOMA cell-free massive MIMO system. Unlike the majority of existing research works on performance evaluation of NOMA, which assume perfect channel state information and perfect data detection for SIC, we take into account the effect of practical (hence imperfect) SIC. We show that the received signal at the backhaul network of a cell-free massive MIMO-NOMA system can be effectively treated as a signal received over an additive white Gaussian noised (AWGN) channel. As a result, a discrete joint distribution between the interfering signal and its detected version can be analytically found, from which an adaptive SIC scheme is proposed to improve performance of interference cancellation

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin