Massive MIMO systems for 5G: a systematic mapping study on antenna design challenges and channel estimation open issues

The next generation of mobile networks (5G) is expected to achieve high data rates, reduce latency, as well as improve the spectral and energy efficiency of wireless communication systems. Several technologies are being explored to be used in 5G systems. One of the main promising technologies that is seen to be the enabler of 5G is massive multiple-input multiple-output (mMIMO) systems. Numerous studies have indicated the utility of mMIMO in upcoming wireless networks. However, there are several challenges that needs to be unravelled. In this paper, the latest progress of research on challenges in mMIMO systems is tracked, in the context of mutual coupling, antenna selection, pilot contamination and feedback overhead. The results of a systematic mapping study performed on 63 selected primary studies, published between the year 2017 till the second quarter of 2020, are presented. The main objective of this secondary study is to identify the challenges regarding antenna design and channel estimation, give an overview on the state-of-the-art solutions proposed in the literature, and finally, discuss emerging open research issues that need to be considered before the implementation of mMIMO systems in 5G networks

Channel estimation in massive MIMO systems

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance

Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

D3.2 First performance results for multi -node/multi -antenna transmission technologies

This deliverable describes the current results of the multi-node/multi-antenna technologies investigated within METIS and analyses the interactions within and outside Work Package 3. Furthermore, it identifies the most promising technologies based on the current state of obtained results. This document provides a brief overview of the results in its first part. The second part, namely the Appendix, further details the results, describes the simulation alignment efforts conducted in the Work Package and the interaction of the Test Cases. The results described here show that the investigations conducted in Work Package 3 are maturing resulting in valuable innovative solutions for future 5G systems.Fantini. R.; Santos, A.; De Carvalho, E.; Rajatheva, N.; Popovski, P.; Baracca, P.; Aziz, D.... (2014). D3.2 First performance results for multi -node/multi -antenna transmission technologies. http://hdl.handle.net/10251/7675

Performance of Massive MIMO with Interference Decoding

In a massive MIMO system, base stations (BS) utilize a large number of antennas to simultaneously serve several (single or multi-antenna) users at once, where the number of BS antennas is normally assumed to be significantly larger than the number of users. In massive MIMO systems operating in time division duplex (TDD) mode, the channel state information (CSI) is estimated via uplink pilot sequences that are orthogonal in a cell but re-used in other cells. Re-using the pilots, however, contaminates the CSI estimate at BSs by the channel of the users sharing the same pilot in other cells; thus causing pilot contamination which creates coherent interference that, as the number of BS antennas grows, scales at the same rate as the desired signal. Hence, in the asymptotic limits of large antennas, the effects of non-coherent interference terms and noise disappear, except for the pilot contamination interference. A common technique used in the literature to deal with this interference is to treat it as noise (TIN). When using TIN, users' throughput will converge to a constant and thus the benefits of using an ever greater number of BS antennas saturate. However, it is known that the use of TIN in interference networks is only preferred in the weak interference regime, and it is sub-optimal in other regimes (e.g., moderate or strong interference). In this thesis, we show that as the number of BS antennas increases, the pilot contamination interference is no longer weak, and therefore it is beneficial to treat it differently (e.g., decode it jointly with the desired signal) to improve users’ throughput. In the first part of the thesis, we study the performance of interference decoding schemes based on simultaneous unique decoding (SD) and simultaneous non-unique decoding (SND), and show that by doing so the rate saturation effect is eliminated as the number of antennas increases; hence, the per-user rates grow unbounded. We analytically study the performance of two well-known linear combining/precoding methods, namely, MRC/MRT and ZF, for spatially correlated/uncorrelated Rayleigh fading channel models, and obtain closed-form expressions of rate lower bounds for these using a worst-case uncorrelated noise technique for multi-user channels. We compare the performance of the different interference management schemes, TIN/SD/SND, based on the maximum symmetric rate they can offer to the users. Specifically, we first obtain structural results for a symmetric two-cell setting as well as the high SINR regime, that provide insights into the benefits of using interference decoding schemes in different regimes of number of BS antennas. We numerically illustrate the performance of the different schemes and show that with a practical number of antennas, SND strictly outperforms TIN. This gain improves with increasing the number of antennas, and also ZF performs significantly better than MRC/MRT due to better mitigation of multi-user interference. Furthermore, we study the performance of regularized ZF (RZF) via Monte Carlo simulations, and observe that it achieves better rates than ZF for moderately small number of antennas only. Lastly, we numerically investigate the impact of increasing the number of cells, the cell radius, the number of users, the correlation of the channel across antennas and the degree of shadow fading on system performance. In the second part of the thesis, we study the performance of partial interference decoding based on rate splitting (RS) and non-unique decoding. Specifically, we propose to partition each user’s message into two independent layers, and partially decode the pilot contamination interference while treating the remaining part as noise based on a power splitting strategy. In particular, for a two-cell system, we investigate the benefits of an RS scheme based on the celebrated Han-Kobayashi (HK) region, which provides the best known achievable performance for a two-user interference channel (IC). In the case of more than two cells, we propose a generalized RS scheme that non-uniquely decodes each layer of the pilot contamination interference and uses only one power splitting coefficient per IC. In addition, we establish an achievable region for this generalized RS scheme using the non-unique decoding technique. In both cases of two cells and more than two cells and for a practical number of antennas, we numerically study the performance of the proposed RS schemes by numerically optimizing the power splitting coefficients, and show that they achieve significantly higher rates than TIN/SD/SND in all scenarios. Similar to the first part of the thesis, we also numerically examine the impact of increasing the number of cells, the cell radius, the number of users, the correlation of the channel across antennas and the degree of shadow fading on the performance of the RS schemes. Lastly, our simulation results reveal that by replacing the numerically optimized values of the power splitting coefficients with their pre-computed average values (over a large number of realizations), the performance loss is quite negligible, thus reducing the optimization complexity