Efficient Robust Beamforming for Downlink Transmission in Massive MIMO Systems

Recently, massive multiple input multiple output (MIMO) is considered as a promising technology to significantly improve the spectral and energy efficiency of fifth generation (5G) networks. Massive MIMO increases the energy efficiency (EE) by three orders of magnitude as compared to single antenna systems. In this paper, we review and analyze energy efficient resource allocation techniques for massive MIMO system. To this end, the energy efficiency of massive massive MIMO with linear processing is formulated. Analytical formulation is provided for the spectral efficiency (SE) and total system power consumption model. The tradeoff on the spectral and energy efficiency is analyzed. The impacts of propagation and system parameters on the SE-EE tradeoff are investigated. Then, large scale fading based analytical approximation for the energy efficiency is formulated. Finally, numerical simulation is done to validate the theoretical analysis.

As a combination of spatial modulation (SM) system and massive multiple-input multiple-output (MIMO) system, massive SM aided MIMO (SM-MIMO) system is recently proposed. In broadband scenarios, single-carrier (SC) modulation is introduced to massive SM-MIMO system, thus massive SC-SM MIMO system is proposed for uplink multi-user transmission over frequency-selective fading channels. In this study, the uplink spectral efficiency (SE) of multi-cell massive SC-SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi-cell massive SC-SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed-form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

Massive Multiple Input Multiple Output (MIMO) system is regarded as an emerging technology to enhance the capacity (achievable rate) of wireless communication systems. In this paper, we present an iterative algorithm that adapts transmit signal vector and power of the user using game theory to enhance the physical-layer security (achievable secrecy rate) of massive MIMO system in the presence of eavesdropper and jammers. The proposed algorithm takes account of impact of jammers while meeting the target Signal-to-Interference-plus-Noise Ratio (SINR) and the impact of both eavesdropper and jammers while evaluating the secrecy rate of the users. Furthermore, we present the existence and uniqueness of Nash equilibrium of the game used in this paper. We evaluate the performance of the algorithm using numerical results obtained from simulations. When both eavesdropper and jammers are present close to the users, secrecy rate of legitimate users degrade significantly.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

Massive multiple-input multiple-output (MIMO) systems have been drawing considerable interest. In the uplink, massive MIMO systems are commonly studied assuming that each base station (BS) decodes the signals of its user terminals separately and linearly while treating all interference as noise. Although this approach provides improved spectral efficiency (SE) in favorable channel conditions, it is generally sub-optimal from an information-theoretic perspective. In this work we characterize the SE of massive MIMO when the BSs are allowed to jointly decode the received signals. We consider two schemes for handling the interference, and derive their SEs for both finite and asymptotic number of antennas. Simulation tests of the proposed methods illustrate their gains in SE compared to standard separate linear decoding, and show that the standard approach fails to capture the actual achievable rates of massive MIMO systems, particularly when the interference is dominant.

In this paper, we investigate the suitability of single carrier frequency domain processing (SC-FDP) as the downlink transmission scheme in a massive multiple-input multiple-output (MIMO) system. By deriving the sum-rate of the SC-FDP massive MIMO system theoretically, we show that this method obtains a sum-rate similar to that of orthogonal frequency division multiplexing (OFDM) massive MIMO. We also derive the theoretical sum-rate of both SC-FDP and OFDM in a non-synchronized massive MIMO scenario and show that the rate of the former is significantly larger than that of the latter. Moreover, we theoretically analyze the sum-rate of both systems in the presence of power amplifier non-linearity. All the sum-rates are derived for both zero forcing and matched filter precoding schemes. The results show that the effect of power amplifier non-linearity on the sum-rate of both systems is similar when the number of users is large. We also compare SC-FDP with OFDM from the peak to average power ratio (PAPR) and complexity viewpoints. Although the PAPR of SC-FDP signals is lower than that of OFDM signals, for MIMO systems, the difference between their PAPR decreases as we increase the number of users. Thus, both techniques can have similar PAPR in massive MIMO systems. The overall complexity of SC-FDP and OFDM is similar. Due to the mentioned facts, SC-FDP can be considered as a promising transmission scheme for the downlink of the massive MIMO systems in the presence of carrier frequency offset and power amplifier non-linearities.

The base station (BS) density influences the coverage performance of cellular communication system. In massive multiple input multiple output (MIMO) system, each BS is equipped with large scale antenna array. The stochastic geometric model is employed because of its analytical tractability. This paper demonstrates that BS density has to be below a certain bound in order to satisfy the coverage requirement in massive MIMO system. Closed-form expression for the BS density bounded by maximum outage probability is derived using Lambert W function. Moreover, the expression for the upper bound of successful area spectral efficiency (ASE) is also derived. Our study claims that increasing BS density will lead to the deterioration of coverage performance in massive MIMO system.

Massive Multiple Input Multiple Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). To reduce the system complexity and hardware cost, low complexity antenna selection techniques can be used to choose the best antenna subset while keeping the system performance at a certain required level. In this paper, Tabu Search (TS) and three bio-inspired optimization algorithms were used for antenna selection in Massive MIMO systems. The three bio-inspired algorithms were: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Simulations showed promising results for the TS by achieving higher capacity with GA than PSO and ABC, and much shorter CPU time than any of the bio-inspired techniques.

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters. The analysis parameters are channel noise, signal-to-noise ratio (SNR) and bit-error-rate (BER) using the hybrid precoder and combiner computation algorithms – Quantized Sparse Hybrid Beamforming (QSHB) and Hybrid Beamforming Peak Search (HBPS). Therefore, the proposed massive MIMO system model gives the less channel noise in the received signal, higher SNR and lower BER as compared to the traditional massive MIMO receiver systems.

In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next generation wireless communication systems and for green communications. There are two operation modes: time-division duplex (TDD) and frequency-division duplex (FDD) for massive MIMO systems, and the debate on the two modes is still discussed in the literature. The originator of massive MIMO systems, Marzetta, has presented the basic frame structures for both the TDD and the FDD modes. However, the basic frame structures do not carefully take the hardware impairments such as phase noise (PN) into account. In this paper, by taking the PN as the key hardware impairment into account, we recommend new and efficient frame structures for both the TDD and the FDD modes. The corresponding performance comparison between the two modes is also provided by using the data transmission efficiency (DTE) as the performance measurement. The comparison may help determine which mode should be used for massive MIMO systems.

Massive multiple-input multiple-output (MIMO) using a large number of antennas at both transmitter and receiver sides based on quasi-orthogonal space time block code (QOSTBC) is presented. Space-time block code (STBC) is a MIMO transmit strategy that applies transmit diversity and high reliability. QOSTBC is attractive because it achieves higher code rate than orthogonal STBC and lower decoding complexity than non-orthogonal STBC. We present the performance of massive MIMO systems using the QOSTBC with multiple antennas up to the 16 × 16 configuration. The performances of 2 × 2, 4 × 4, 8 × 8 and 16 × 16 massive MIMO systems have been presented. Simulation results show that the massive MIMO systems with QOSTBC give significant performance improvement with full rate and full diversity, compared with previously considered massive MIMO systems.

This chapter, the techniques of massive multiple-input-multiple-output (MIMO) and network MIMO are studied. First, massive MIMO is compared with the conventional point-to-point MIMO used in current cellular systems in terms of capacity and energy efficiency. The comparison shows that massive MIMO has huge gains even under the modest conditions using simple linear processing. Then, the implementation of massive MIMO is discussed by focusing on the channel estimation method, the signal detection method and the precoding method. Following this, several main issues in massive MIMO are examined and relevant measures are discussed. Finally, network MIMO is studied by introducing cooperation between base stations to completely remove the inter-cell interference, at the cost of even higher complexity that places limits on the backhaul and fronthaul links.

Massive multiple-input multiple-output (MIMO) is a potential candidate key technology for the fifth generation of wireless communication systems. In previous research, different power loss and shadowing effects on different individual antenna elements have been neglected. When characterizing the channel of a massive MIMO system and investigating the system performance, the large scale attenuation (LSA) over the antenna array has not been previously considered. In this paper, based on our proposed accurate geometrical propagation model, the spectral efficiency (in terms of bits/s/Hz sum-rate) result of maximal ratio combining (MRC) detection is investigated. From the simulation results, we find that the sum-rate performance for the MRC detection of our proposed more realistic channel model exceeds the results using the conventional model (where the LSA effect is not included). This proposed model LSA model is beneficial and informative for the research, design, and evaluation of the next generation of wireless communication systems employing massive MIMO configurations.

With a large number of antennas at the transmitter and receiver, massive multiple-input multiple-output (MIMO) systems can achieve substantially high spectral efficiency as well as high stability and reliability. Nevertheless, one of the major challenges for its practical use is its computational complexity for symbol detection that grows exponentially with the number of antennas. In this work, we focus on a low complexity implementation of massive MIMO detector based on matched filter (MF) detection with parallel interference cancellation (PIC). In general, MF-based detector performs poorly in the high SNR region. Nevertheless, with the help of powerful channel coding such as low-density parity-check (LDPC) codes and the well-designed PIC, the resulting error performance of the proposed MF-PIC can approach or even outperform the conventional minimum mean square error (MMSE)-based detector with much less complexity. Furthermore, due to its parallel structure, low latency implementation based on parallel architecture is possible and thus is suitable for practical massive MIMO systems.

Massive multiple-input multiple-output (MIMO) is a key technology in fifth generation (5G) communication systems. Although the maximum likelihood (ML) obtains an optimal performance, it is prohibited in realization because of its high computational complexity. Linear detectors are an alternative solution, but they contain a matrix inversion which is not hardware friendly. Several methods have been proposed to approximate or to avoid the computation of exact matrix inversion. This chapter garners those methods and study their applicability in massive MIMO system so that a generalist in communication systems can differentiate between different algorithms from a wide range of solutions. This chapter presents the performance-complexity profile of a detector based on the Neuamnn-series (NS), Newton iteration (NI), successive over relaxation (SOR), Gauss-Seidel (GS), Jacobi (JA), Richardson (RI), optimized coordinate descent (OCD), and conjugate-gradient (CG) methods in 8×64, 16×64, and 32×64 MIMO sizes, and modulation scheme is 64QAM.