The Benefits of Large-Scale Attenuation over the Antenna Array in Massive MIMO 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.

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.

<p>This thesis considers the analysis of matched filtering (MF) processing in massive multi-user multiple-input-multiple-output (MU-MIMO) wireless communication systems. The main focus is the analysis of system performance for combinations of two linear processers, analog maximum ratio combining (MRC) and digital MRC. We consider implementations of these processing techniques both at a single base-station (BS) and in distributed BS layouts. We further consider extremely low complexity distributed variants of MRC for such systems. Since MRC relies on the massive MIMO properties of favourable propagation (FP) and channel hardening, we also present a detailed analysis of FP and channel hardening. This analysis employs modern ray-based models rather than classical channel models as the models are more reliable for the large arrays and higher frequencies envisaged for future systems. The importance of MRC processing is being driven by the emergence of massive MIMO and millimetre wave as strong candidates for next generation wireless communication systems. Massive MIMO explores the spatial dimension by providing significant increases in data rate, link reliability and energy efficiency. However, with a large number of antennas co-located in a fixed physical space, correlation between the elements of antennas may have a negative impact. Distributed systems, where the total number of antennas are divided into different locations, make this problem less serious. Also, linear processing techniques, analog MRC and digital MRC, due to their simplicity and efficiency, are more practical in massive MU-MIMO systems. For these reasons we consider MRC processing in both co-located and distributed scenarios. Although distributed systems reduce the adverse impact of correlation caused by closely-spaced large antenna arrays by dividing the antennas into multiple antenna clusters, the correlation within the cluster still exists. Thus, we extend MRC analysis for massive MIMO to correlated channels. Approximations of expected per-user spectrum efficiency (SE) with correlation effects for massive MIMO systems with analog MRC and digital MRC are derived. Useful insights are given for future system deployments. A convergence analysis of the interference behaviour under different correlation models is presented. Furthermore, a distributed fully cooperative system, where all the received signals are sent to the central processor, offers attractive performance gains but at the cost of high computational complexity at the central node. Thus, we propose four low-complexity, two-stage processors, where only processed signals after local processing (first-stage) are transmitted to the global processing node (second-stage). We present analytical expressions for the expected per user SINR in an uplink distributed MU-MIMO system with two-stage beam-forming. This leads to an approximation of expected per-user SE. The analysis of both millimetre wave and massive MIMO systems requires a strong link to the physical environment and ray-based models are more practical and suitable for such systems. However, it is unclear how the key properties in conventional MIMO systems, such as FP and channel hardening, will behave in a ray-based channel model. In this thesis, remarkably simple and general results are obtained demonstrating that: a) channel hardening may or may nor occur depending on the nature of the channel models; b) FP is guaranteed for all models as long as the ray angles are continuous random variables; c) we also propose a novel system metric, denoted large system potential (LSP) as the ratio of the mean desired signal power to the total mean interference power, where both the numbers of antennas and end-users are growing to infinity at a fixed ratio. We derive simple approximations to LSP and demonstrate that LSP will not normally hold as the mean interference power usually grows logarithmically relative to the mean signal power.</p>

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 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.

Massive Multiple-Input Multiple-Output (MIMO) is an extension of the conventional MIMO in the wireless systems which improves both of the access density and the spectral efficiency by adding a massive number of antenna array at the base station (BS). Massive MIMO increases the spectral efficiency by using the beamforming. Besides, the beamforming in massive MIMO improves the energy efficiency by focusing the energy in the desired direction instead of the omnidirectional propagation. In this paper, we propose and discuss different beamforming objectives in both the uplink and the downlink channels. These proposed objectives can be either use the beamforming of the desired signal without nulling the interference or use the beamforming with interference nulling. The beamforming with nulling objectives have better performance than those without nulling but this leads to a higher computational complexity as well. The results of this paper show and compare the performance of these objective including the spectral efficiency and energy efficiency as well as the computational complexity. Index Terms— Beamforming, Interefernce nulling, Energy efficiency, Massive MIMO, Spectral efficiency.

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.

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.

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.

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.

This paper considers the problem of massive multiple-input-multiple-output (MIMO) wireless communication systems with quasi-orthogonal space-time block code (QOSTBC) transmission in the presence of spatial correlation effect (SCE) and mutual coupling effect (MCE). Conventional MIMO channels with QOSTBC transmission suffer from several drawbacks. The number of transmit antennas is restricted to a few in order to achieve full transmission rate. Therefore, it is difficult to make a quasi-orthogonal space time block coded massive MIMO system to achieve full transmission rate. Moreover, MIMO channel with QOSTBC transmission is usually considered for the case where the number of user equipments (UEs) is one. We present a joint beamforming and spatial precoding method to deal with the above drawbacks. The proposed method incorporates a beamforming scheme and spatial precoding to formulate an appropriate optimization process to effectively alleviate the considered problems. The resulting optimization problem will be solved by using a cooperative coevolutionary particle swarm optimization algorithm under two proposed fitness functions. During the optimization process, the proposed method finds the optimal beamforming coefficients of the precoding matrix, the optimal normalized positions of transmit and receive antenna elements for the case of using linear antenna arrays, and the optimal angle differences of transmit and receive antenna elements for the case of using circular arrays. Based on the proposed method, we are able to cure the performance degradation of a quasi-orthogonal space time block coded massive MIMO system due to the SCE and MCE. Moreover, the proposed method makes QOSTBC MIMO communications with full transmission rate for any number of transmit antennas achievable. Several simulation examples are presented to show the superior bit error rate (BER) performances of QOSTBC wireless MIMO scenarios with linear as well as circular antenna arrays by using the proposed method as compared to the existing methods.

The combination of massive multiple-input multiple-output (MIMO) and single-carrier (SC) aided spatial modulation (SM), is recently proposed for uplink multi-user transmission over dispersive channels, which has also attracted plenty of research interest. In this letter, first the spectral efficiency (SE) lower bound with non-uniform power allocation for uplink single-cell massive SC-SM MIMO systems is proposed, in which, the base station (BS) applies maximum ratio combining and many non-ideal factors of the wireless channels are all considered. Then, a barrier function-based gradient projection method is proposed to allocate power of each transmit antenna (TA) in terms of SE maximization. The SE lower bound is shown to be tight via simulation results, and simulation results also show that the SE gain introduced by our proposed PA algorithm is mainly dependent on the TA correlation and number of TAs.

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.

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 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.