Spectral efficiency analysis and pilot reuse factor optimisation for multi‐cell massive SC‐SM MIMO

In this paper, we consider secure downlink transmission in a multi-cell massive multiple-input multiple-output (MIMO) system where the numbers of base station (BS) antennas, mobile terminals, and eavesdropper antennas are asymptotically large. The channel state information of the eavesdropper is assumed to be unavailable at the BS and hence, linear precoding of data and artificial noise (AN) are employed for secrecy enhancement. Four different data precoders (i.e., selfish zero-forcing (ZF)/regularized channel inversion (RCI) and collaborative ZF/RCI precoders) and three different AN precoders (i.e., random, selfish/collaborative null-space based precoders) are investigated and the corresponding achievable ergodic secrecy rates are analyzed. Our analysis includes the effects of uplink channel estimation, pilot contamination, multi-cell interference, and path-loss. Furthermore, to strike a balance between complexity and performance, linear precoders that are based on matrix polynomials are proposed for both data and AN precoding. The polynomial coefficients of the data and AN precoders are optimized respectively for minimization of the sum mean squared error of and the AN leakage to the mobile terminals in the cell of interest using tools from free probability and random matrix theory. Our analytical and simulation results provide interesting insights for the design of secure multi-cell massive MIMO systems and reveal that the proposed polynomial data and AN precoders closely approach the performance of selfish RCI data and null-space based AN precoders, respectively.

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.

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 study the problem of secure transmission in the downlink of a multi-cell massive multiple-input multiple-output (MIMO) system, where pilot contamination is considered due to non-orthogonal uplink training sequence. The channel state information (CSI) of the eavesdropper is assumed to be unavailable at the base station (BS) and hence, the BS cooperation and an artificial noise(AN) are investigated for secrecy enhancement. Based on regularized channel inversion (RCI), we derive a closed-form expression for ergodic secrecy sum rate in the large-system regime, in which the number of users per cell K, the number of antenna at BS N and the number of antenna at eavesdropper N E tend to infinity. Our analysis shows that the secrecy sum-rate saturates at high signal-to-noise ratio (SNR) with BSs cooperation and AN under pilot contamination.

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 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.

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.

A multicell massive multiple-input multiple-output (MIMO) system, which utilizes a large number of base-station antennas to simultaneously serve a set of users, suffers from pilot contamination (PC) due to unavoidable reuse of pilots in adjacent cells. In this paper, a weighted-graph-coloring-based pilot decontamination (WGC-PD) scheme is proposed to mitigate PC for multicell massive MIMO systems. Specifically, based on limited cooperation among cells, an edge-weighted interference graph (EWIG) is first constructed to depict the potential PC relationship among users, whereby two users in different cells are connected by a weighted edge, indicating the strength of potential PC when they reuse the same pilot. Then, inspired by classical graph coloring algorithms, we develop the WGC-PD scheme by denoting each color as a pilot and each vertex as a user in the EWIG, which is able to mitigate PC by assigning different pilots to connected users with a large weight in a greedy way with insufficient pilot resource. Compared with exhaustive search among numerous pilot assignment solutions, the proposed WGC-PD scheme is able to mitigate PC with significantly reduced complexity, which is verified by numerical results.

Recently, unmanned aerial vehicle (UAV) communications have drawn significant research interests. Meanwhile, cell-free (CF) massive multiple-input multiple-output (MIMO) is proposed as a promising technology to achieve the ultimate performance limits. In this paper, we investigate the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. Take hardware impairments of UAV into account, novel closed-form downlink HE and uplink spectral efficiency (SE) expressions are derived. UAV communications with small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. Our results reveal that CF massive MIMO achieves two and four times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. To this end, SE is a concave function of the time-splitting fraction, and the optimal time-splitting fraction for maximizing SE is determined by the altitude and hardware impairment factor of the UAV.

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.

Cell-free (CF) Massive multiple-input multiple-output (MIMO) system is one of the most promising technologies for the fifth generation (5G) wireless communication and beyond, in which the concept of cell boundaries is not respected. This new paradigm aims to cover the users simultaneously through many distributed access points (APs) over the same time/frequency resources based on time-division duplex (TDD) system. In this paper, the uplink spectral efficiency (SE) of a CF Massive MIMO system is investigated based on the stochastic geometry (SG) tool and over Rician fading channels. The distribution of APs is assumed to be random based on the Poisson point process (PPP) to emulate the real AP behavior over the mobile network which has not been considered previously. However, deploying the network's APs irregularly may worsen the phase noise effect and thus the SE of the system. The uplink SE of cell free massive MIMO is derived considering the maximum ratio combining (MRC) at the AP receivers and minimum mean-square error (MMSE) to estimate the channels stats. The simulation results have confirmed that CF Massive MIMO system provides higher SE gain when the APs density is unevenly and largely distributed. However, a considerable cost of the uplink SE is observed when the length of the uplink training period increases for the perfect Channel State Information (CSI) case. Moreover, the performance gap between the perfect and imperfect (CSI) converges when the uplink training increases.

The performance of the hybrid beamforming system based on the full-connected structure is close to that of the full-digital one in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, due to the high complexity of circuit wiring in practical applications, it has been greatly restricted. This paper investigates the hybrid (analog/digital) beamforming design of a multiuser mmWave massive MIMO system with sub-connected structure. For this system, we focus on a sum-rate maximization probelm. Considering the joint design of the receiver, and transmitter, a two-stage design method is adopted. In the analog stage, the analog beamformer, and combiner are designed by the proposed piecewise dual joint iterative approximation (PDJIA) method, which not only can obtain closed-form solutions but also has linear property. Based on the criterion of avoiding the loss of information, the problem of digital beamforming is solved by exploiting the baseband piecewise successive approximation (BPSA) method, which can effectively increase the number of users served. Simulation results show that the proposed scheme outperforms the state-of-the art MIMO hybrid beamforming design schemes, and more accurately approximates to that of the full-digital system.

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) technology for wireless communication is considered a key technology for the future fifth-generation (5G) communication networks. To reduce the propagation loss of channel, MIMO systems with analog or digital hybrid precoders and combiners (HPC) architecture have been used. This review paper presents the various types of analog and digital HPC designs for the massive MIMO communication systems that were investigated within the last decade. The reviewed systems are useful for both single-user (SU) and multi-user MIMO (MU-MIMO) systems. The key parameter of the study was spectral efficiency (SE), signal-to-noise ratio (SNR), signal-to-interference pulse noise ratio (SINR), system sum-rate (SR) performance, computational complexity (CC), co-channel interference (CCI), inter-primary base station interference (IPBSI), multiuser interference (MUI), effective desired channel gain (EDCG), and minimum mean square error (MMSE).

Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.