A novel algorithm for secrecy rate analysis in massive MIMO system with target SINR requirements

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 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.

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) 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.

In this paper, an energy efficient precoding scheme is proposed for massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme outperforms the conventional zero forcing (ZF) precoder in terms of the power consumption and the achievable transmission rate by including a complex valued regularization parameter such that the energy efficiency is improved. The proposed energy efficient complex regularized zero forcing precoder is analytically evaluated, verified by simulations and compared to the ZF precoder. The obtained results confirm the robustness of the proposed precoder in massive MIMO systems.

In this paper, the performance of an RF energy harvesting scheme for multi-user massive multiple-input multiple-output (MIMO) is investigated in the presence of multiple active jammers. The key idea is to exploit the jamming transmissions as an energy source to be harvested at the legitimate users. To this end, the achievable uplink sum rate expressions are derived in closed-form for two different antenna configurations. An optimal time-switching policy is also proposed to ensure user-fairness in terms of both harvested energy and achievable rate. Besides, the essential trade-off between the harvested energy and achievable sum rate are quantified in closed-form. Our analysis reveals that the massive MIMO systems can make use of RF signals of the jamming attacks for boosting the amount of harvested energy at the served users. Numerical results illustrate the effectiveness of the derived closed-form expressions over Monte-Carlo simulations.

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.

Massive multiple-input multiple-output (MIMO) is an emerging technology, which enables future broadband wireless connections. In this paper we investigate the optimization problem of signal-to- interference-plus-noise ratio (SINR) balancing for users in a typical massive MIMO network. We present a fast converging robust beamforming solution for the case of imperfect channel state information at the transmitter. The proposed method applies the Smith form reformulation to transform the original non-convex optimization problem into a standard second-order cone program. The matrix stuffing technique and the alternative direction method of multipliers are then used to provide an efficient solution to the problem. Simulation results show that the proposed solution gives SINR performance with lower accuracy averaging 97%, while it converges to a solution more than twice faster than the optimal solution, which uses CVX and the SeDuMi solver.

In this paper, a staggered frame structure is proposed for single-cell massive multiple-input multiple-output (MIMO) systems, and the key idea is that different users transmit training pilots at nonoverlapped time. As a result, users do not have to be synchronized strictly to send pilots and orthogonal pilots are not required. Moreover, we also propose two interference suppressed channel estimation methods, i.e., the linear minimum mean square error (LMMSE)-based and orthogonal projection based least squares (OPLS) methods for the staggered frame structure. Specifically, the LMMSE-based method minimizes the mean square error of the estimation, whereas the OPLS method only estimates the part of the user's channel response that is orthogonal to the other users. All conducted simulation results demonstrate that the massive MIMO system with the both proposed methods can achieve high average achievable rate. Furthermore, when the conjugate beamforming is employed, the massive MIMO system could obtain even higher average achievable data rate with the proposed OPLS method than that of the system with perfect channel state information. Moreover, the computational complexity of the proposed OPLS method is very low.

In this letter, we investigate the downlink performance of massive multiple-input multiple-output (MIMO) systems where the base station is equipped with one-bit analog-to-digital/digital-to-analog converters (ADC/DACs). We assume that the base station employs the linear minimum mean-squared-error channel estimator and treats the channel estimate as the true channel to precode the data symbols. We derive an expression for the downlink achievable rate for matched-filter precoding. A detailed analysis of the resulting power efficiency is pursued using our expression of the achievable rate. Numerical results are presented to verify our analysis. In particular, it is shown that, compared with conventional massive MIMO systems, the performance loss in one-bit massive MIMO systems can be compensated for by deploying approximately 2.5 times more antennas at the BS.

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.

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.

A massive multiple-input multiple-output (MIMO) system using a couple of hundred antenna elements has been arising as an important technology to keep the communication range in higher frequency bands. A further enhancement to increase the number of both transmit and receive antennas will be the next step. It requires the complexity proportional to at least the third power of the number of antenna elements to detect spatially-multiplexed signals in general. However, the belief propagation (BP)-based detector is implementable with lower complexity, i.e., the order of square of the number of elements, and achieves very good BER performance in a QPSK modulated massive MIMO system. In this paper, we apply the BP algorithm to a 16-QAM modulated massive MIMO system and propose a sequential reliability update to exploit a difference in error tolerance. The BER evaluation results have shown that the BP works well for a 16-QAM signal detection and that the sequential update is effective to the M-QAM modulated systems.

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.

Interference is considered as the major problem in the next-generation wireless systems to attain higher throughput. The conventional interference cancelation techniques attain a theoretical breach in endorsing the wireless systems. However, the interference in the signals causes an error or imprecise estimation of the channel for future fifth-generation (5 G) transmissions. Thus, this paper proposes a technique, named Fractional Sequential Likelihood Ascent Search (Fractional-SLAS) for mitigating the interference in massive Multiple Input Multiple Output (MIMO) systems in 5 G technology. The proposed Fractional-SLAS is the integration of Fractional calculus (FC) and Sequential Likelihood Ascent Search Detector (SLAS). Initially, the signals are transmitted through the transmitter and undergo the encoding and modulation process. Then, the obtained modulated signals are fetched using the transmitting antenna that transmits the signals to the receiver. At the receiver side, interference is generated due to the number of cross antennas. Thus, the interference in the signals is eradicated by applying the proposed Fractional-SLAS at the receiver end. Hence, the proposed interference mitigation method is completely eradicated from the signals without any delay or loss in the quality of the transmitted signal. The proposed Fractional-SLAS provides superior performance with minimal Bit Error Rate (BER), Symbol Error Rate (SER), and throughput with values 0.00006 dB, 0.00006 dB, and 0.96 bps.