Electromagnetic Lens-Focusing Antenna Enabled Massive MIMO: Performance Improvement and Cost Reduction

Although massive multiple-input multiple-output (MIMO) can enhance the overall system performance significantly, it could suffer from high cost and power consumption issues due to using a large number of radio frequency (RF) chains. Two different approaches are commonly exploited to overcome these issues. The first approach is using hybrid beamforming, which consists of analog and digital beamforming, to reduce the total number of RF chains. The second approach is adopting low-resolution analog-todigital converters (ADCs) for each RF chain. For both approaches, channel estimation becomes a difficult task. This paper addresses the problem of channel angle of arrival (AoA) estimation in massive MIMO using both hybrid beamforming and one-bit magnitudeaided (OMA) ADCs. An iterative algorithm is developed to estimate the channel AoA, and the appropriate threshold per iteration is analyzed. Numerical results show that the proposed technique can achieve sufficient AoA estimation performance with practical values of the signal-to-noise ratio (SNR).

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.

This paper designs a novel hybrid (a mixture of analog and digital) beamforming and examines the relation between the hybrid and digital beamformings for downlink multiuser massive multiple input multiple output (MIMO) systems. We assume that perfect channel state information is available only at the transmitter and we consider the total sum rate maximization problem. For this problem, the hybrid beamforming is designed indirectly by considering a weighed sum mean square error (WSMSE) minimization problem incorporating the solution of digital beamforming which is obtained from the block diagonalization technique. The resulting WSMSE problem is solved by applying the theory of compressed sensing. The relation between the hybrid and digital beamformings is studied numerically by varying different parameters, such as the number of radio frequency (RF) chains, analog to digital converters (ADCs) and multiplexed symbols. Computer simulations reveal that for the given number of RF chains and ADCs, the performance gap between digital and hybrid beamformings can be decreased by decreasing the number of multiplexed symbols. Moreover, for the given number of multiplexed symbols, increasing the number of RF chains and ADCs will increase the total sum rate of the hybrid beamforming which is expected.

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.

In this paper, we present a machine learning based radio resource management (RRM) scheme for a rank-constrained massive multiple-input multiple-output (MIMO) system. We combine the radio resource management and hybrid beamforming design constraint to desired number of radio frequency (RF) chains. The formulated optimization problem maximizes the sum throughput constraint to total transmit power and the number of RF chains. Being the computationally intensive part of the system, the radio resource allocation in multiuser, multi-carrier massive MIMO system has been replaced by the neural network employing the supervised learning. The proposed design renders the similar sum rate performance given by the CVX-based optimal algorithm, but with 28.72 times less execution time.

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 a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Lowcomplexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.

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.

This paper evaluates inter-satellite communication for a low earth orbit CubeSat network. This paper consists of two parts: determination and estimation of quality of service (QoS) parameters and evaluation of the feasibility of a Massive Multiple Input Multiple Output (MIMO) system for inter-satellite links. CubeSats are low-cost and small satellites that were initially designed by students during the educational process but are now launched for conducting space research and for commercial use. Recently, some companies and research studies promoted CubeSats to build a wireless communication networks for different proposes, such as earth observation, weather forecasting, providing access to the internet, internet of things and machine to machine services. The main distinguishing features of a CubeSat network include low-cost and fast deployment with a consequent advantage: the possibility to build a large network (hundreds of spacecraft) with limited resources. However, due to the limited size and function of CubeSats, the feasibility of a CubeSat network with inter-satellite links is debatable. In this paper, we investigate the QoS parameters for an inter-satellite link and factors affecting it. The theoretical design with a constructive drawing of massive MIMO has been made. The possibility and time duration of inter-satellite communication has been calculated for three different cases using real data and including Massive MIMO. The simulation of orbit movements for groups of CubeSats has been made. The correlation between relative positions of satellites in orbits, parameters of communication channels and the QoS was determined. The feasibility of massive MIMO for CubeSat communications has been estimated. Based on simulation results, suggestions and possible technical and non-technical solutions were highlighted together with future studies and simulations.

Massive multiple-input multiple-output (MIMO) technology – employing base stations with dozens or hundreds of antennas – has emerged over the past decade from a theoretical concept into a cornerstone of 5G wireless communications. A key enabler of massive MIMO’s spectral efficiency gains is beamforming, which adapts the antenna array’s radiation pattern to direct signal energy toward intended users (and away from interferers). By coherently combining signals across a large antenna array, massive MIMO can simultaneously serve multiple users on the same time-frequency resource, dramatically increasing capacity. Early works showed that as the number of antennas grows, simple linear precoding (beamforming) schemes like maximum-ratio transmission or zero-forcing can achieve near-optimal performance, eliminating inter-user interference under favorable propagation. These revelations, along with pioneering concepts around 2010, spurred intensive research into practical beamforming strategies over the last ten years. This literature review provides a comprehensive overview of beamforming strategies in massive MIMO systems, spanning roughly 2015–2025, covering both theoretical advancements and real-world applications. We chart the evolution from conventional analog and digital beamforming to modern hybrid techniques, and discuss associated optimization methods.

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 attracted much attention lately due to the many advantages they provide over single- antenna systems. Owing to the many antennas, low- cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with one-bit analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on one-bit quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with one-bit ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic rate. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.

Massive multiple input multiple output (MIMO), also known as a very large-scale MIMO, is an emerging technology in wireless communications that increases capacity compared to MIMO systems. The massive MIMO communication technique is currently forming a major part of ongoing research. The main issue for massive MIMO improvements depends on the number of transmitting antennas to increase the data rate and minimize bit error rate (BER). To enhance the data rate and BER, new coding and modulation techniques are required. In this paper, a generalized spatial modulation (GSM) with antenna grouping space time coding technique (STC) is proposed. The proposed GSM-STC technique is based on space time coding of two successive GSM-modulated data symbols on two subgroups of antennas to improve data rate and to minimize BER. Moreover, the proposed GSM-STC system can offer spatial diversity gains and can also increase the reliability of the wireless channel by providing replicas of the received signal. The simulation results show that GSM-STC achieves better performance compared to conventional GSM techniques in terms of data rate and BER, leading to good potential for massive MIMO by using subgroups of antennas.

The communication technology industry's energy consumption and the resulting energy-related pollution are becoming serious societal and economic challenges. One of the most essential parameters for selecting potential technologies for upcoming wireless communication networks is bandwidth efficiency (BE). However, the main objective is to develop new creative network architecture and technologies that are required to meet the rapid growth in cellular data demand while maintaining same amount of power.Also, Energy Efficiency (EE) has emerged as a key parameter for assessing the performance of wireless communication networks. In this context, the recently suggested massive multiple-input multiple-output (MIMO) technology, which comes under multiuser MIMO with an excess of base station (BS) antennas to support a substantial number of user equipment's (UE) is one of the main technology enablers for 5G.In this paper, new power consumption model is addressed and also novel EE maximization algorithm is proposed which optimizes the resource allocation in massive MIMO network.

In this paper, we develop the channel estimation algorithm for a massive multiple input multiple output (MIMO) system using one-bit analog-to-digital converters (ADCs). Although, one-bit quantization significantly reduces the deployment cost and power consumption of the massive MIMO, the great distortion induced by one-bit quantization makes channel estimation more difficult. Therefore, a channel estimation method by executing a deep neural network (DNN) over multiple signal segments is proposed for the uplink of one-bit massive MIMO. The average of the DNN’s outputs throughout all the segments is the final channel estimate for a transmission block. In order to improve the estimation accuracy without increasing the length of pilot, the data symbols got based on the initial channel estimates are used as the other part of pilots to refine the estimation result. Moreover, a sliding window based pilot segment method is adopted to increase the number of signal segments with a same pilot length. The simulation results show that the proposed scheme outperforms least squares (LS) and Bussgang linear minimum mean squared error (BLMMSE) channel estimators in the whole signal-to-noise-ratio (SNR) region.