Conventional Massive Massive Multiple-input Multiple-output Research Articles

Intelligent Analog Beam Selection and Beamspace Channel Tracking in THz Massive MIMO With Lens Antenna Array

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive exogenous (NARX) model for tracking and predicting the beamspace channel over the sequences of time. Benefiting from bounded generalization error, fast convergence, limited prediction variance, and negligible performance loss, the proposed scheme achieves up to 15% spectral efficiency (SE) gain over its counterparts. We further improve this performance by means of an ensemble learning technique for simultaneously training multiple NARX modules in parallel, thus leading to a 23% SE gain. Relying on the predicted beamspace channel, we propose a beamspace analog beam selection technique through fine-tuning the architecture of a pre-trained off-the-shelf GoogleNet, which brings up to 21% SE gain over similar baselines. With the aid of an ensemble learning technique, it is further indicated numerically that up to 34% SE improvement can be achieved, as compared to the counterparts.

Downlink Cell-Free Fixed Wireless Access: Architectures, Physical Realities, and Research Opportunities

Recently a new paradigm of wireless access, termed as cell-free massive multiple-input multiple-output (MIMO), has drawn significant research interest. Its primary distinction from conventional massive MIMO aided cellular networks is the ability to eliminate the detrimental inter-cell interference (ICI), or to convert ICI into extra power for the intended signal via a multi-cell cooperation approach originated from network MIMO. However, the information-theoretical limit of cell-free access is achieved at the expense of large network configuration overhead and high MIMO processing complexity. Because of the dynamic nature of wireless channels, the global channel state information (CSI) invoked for network MIMO quickly becomes outdated, leading to performance degradation. This paper focuses on the cell-free implementation of fixed wireless access (FWA), a complementary solution to fibre-to-the-premise (FTTP) where the latter is prohibitively expensive. In particular, we discuss the centralisation architectures and channel characteristics of cellfree FWA, as well as their joint implications on imperfect CSI performance. Moreover, measurement-based offline simulations show that the long coherence time ('quasi-static') assumption of real-world FWA channels is only valid against a completely motionless background, and thus it should not be used in FWA system design or performance analysis. Finally, we present new research opportunities for cell-free FWA in terms of physical infrastructure, data processing as well as machine learning.

A Novel Beam Domain Channel Model for B5G Massive MIMO Wireless Communication Systems

In this paper, a novel beam domain channel model (BDCM) is proposed for beyond fifth generation (B5G) massive multiple-input multiple-output (MIMO) wireless communication systems. Different from conventional massive MIMO BDCMs which assumed the far-field plane wavefront effect, the proposed BDCM considers more realistic spherical wavefront caused by near-field effect. We transform a massive MIMO geometry-based stochastic model (GBSM) from the antenna domain to the beam domain through specific algorithms to obtain the novel BDCM. The space-time-frequency correlations of both the GBSM and BDCM are studied, and the correlations for both models at the cluster level are similar. We also compare the quasi-stationary distance (QSD), computational complexity, and channel capacity for both models. Results show that in comparison to the GBSM, the novel BDCM has lower complexity and similar accuracy if the number of beams is sufficiently large. Furthermore, we compare the singular value spreads (SVSs) of both channel models with channel measurements under the same conditions. Both the novel BDCM and GBSM are close to the measurement. Through the above analysis, the novel BDCM is proved to be more convenient for information theory and signal processing researches than the conventional GBSMs.

Leakage Subspace Precoding and Scheduling for Physical Layer Security in Multi-User XL-MIMO Systems

We investigate the achievable secrecy spectral efficiency in a multi-user extra-large multiple-input multiple-output (XL-MIMO) system. In these beyond-5G scenarios, seen as the natural evolution of conventional massive MIMO systems, the distances from the mobile users to the base station become comparable to the antenna array dimensions. We show that the consideration of spherical-wavefront propagation inherent to these set-ups is beneficial for physical-layer security, as it provides immunity against eavesdroppers located in similar angular directions that would otherwise prevent secure communication under classical planar-wavefront propagation. A leakage subspace precoding strategy is also proposed for joint secure precoding and user scheduling, which allows to improve the secrecy spectral efficiency over a 40% compared to conventional zero-forcing methods, under different eavesdropper collusion strategies.

Antenna Selection Based on Matching Theory for Uplink Cell-Free Millimetre Wave Massive Multiple Input Multiple Output Systems

In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.

Power Scaling Law Analysis and Phase Shift Optimization of RIS-Aided Massive MIMO Systems With Statistical CSI

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.

An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accuracy of highly separable channels with narrow angular spread by realizing the “divide-and-conquer” policy. Specifically, we introduce a novel attention-aided DL channel estimation framework for conventional massive MIMO systems and devise an embedding method to effectively integrate the attention mechanism into the fully connected neural network for the hybrid analog-digital (HAD) architecture. Simulation results show that in both scenarios, the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead. Furthermore, strong robustness under different system and channel parameters can be achieved by the proposed approach, which further strengthens its practical value. We also investigate the distributions of learned attention maps to reveal the role of attention, which endows the proposed approach with a certain degree of interpretability.

Throughput‐based energy and spectral efficiency tradeoff for massive MIMO systems

In massive MIMO systems, energy efficiency (EE) and spectral efficiency (SE) can be balanced by tuning the number of the transmit antennas and the transmit power. However, two key parameters i.e. the modulation order and the bit-error-rate (BER) are seldom considered in the conventional EE-SE tradeoff for the massive MIMO systems. To address this problem, the throughput-based EE (TEE) and SE (TSE) tradeoff for both the conventional massive MIMO and the full-dimensional (FD) massive MIMO systems are investigated. Firstly, a TEE and TSE tradeoff problem is formulated and the transition property of the modulation order is analysed. The analysis reveals that due to the discrete property of the modulation order, the TEE-TSE tradeoff curve is continuously non-differentiable at the transition point of the modulation order. By utilising the transition property, a modified normal-boundary-intersection particle-swarm-optimisation (NBI-PSO) algorithm is developed to solve the TEE-TSE problem effectively. From the numerical results, it is found that the throughput-based problem has a relatively smaller EE and SE on the Pareto front than the capacity-based one and selecting an improper combination of the modulation order, the transmit power and the number of the antennas may cause the EE-SE relation sub-optimal from the point view of Pareto efficiency. Through the numerical results, the most energy efficient modulation order is obtained and it is not the commonly assumed low-order modulation scheme.

Performance Analysis of Physical Layer Network Coding in Massive MIMO Systems With M-QAM Modulations

In this paper, we develop a practical approach for deploying Physical Layer Network Coding (PNC) in multi-user M-Ary Quadrature Amplitude Modulation (M-QAM) Massive Multiple-Input Multiple-Output (MIMO) systems. We formulate a PNC mapping scheme as a function of clusters of estimated summation and difference (SD) of the transmitted symbols from user pairs. Utilizing existing linear detection schemes, such as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE), a cluster of SD symbols are detected using an SD linearly transformed channel matrix. Furthermore, utilizing Maximum a Posteriori (MAP) soft decoding, the SD symbols are mapped to the PNC symbols, leveraging on the PNC symbol that maximizes the likelihood function. For each variant of M-QAM, we derive and simplify a specialization of the generalized PNC mapping function. The error performance results, through simulation, reveal that the proposed PNC scheme achieves twice the spectral efficiency in Massive MIMO, without changing the latter's underlying framework and without any degradation in the bit-error-rate (BER). In fact, our investigation has proved that the BER of the proposed Massive MIMO and PNC is slightly better than that of the conventional Massive MIMO. The feasibility of deploying our proposed PNC scheme in Massive MIMO systems paves way for NC applications to be realized in cellular systems.

Power Allocation for Multiple User-Type Massive MIMO Systems

In this paper, we propose a multiple user-type massive multiple-input-multiple-output (MIMO) system and present power allocation solutions for this proposed network. Unlike conventional massive MIMO systems in which all users are treated equally, the proposed system consists of two types of users: quality-of-service (QoS) users, who are guaranteed to have good and stable quality, and non-QoS users, who are similar to users in conventional massive MIMO systems. We formulate two power allocation optimization problems which guarantee quality-of-service for QoS users and maximize the sum-rate of non-QoS users simultaneously. These two problems are solved by two proposed successive convex approximation algorithms, and we show that these two algorithms surely converge to a local maximum Karush-Kuhn-Tucker point. The numerical results show the effectiveness of our proposed power allocation algorithms in multiple user-type massive MIMO systems.

Preamble-Data Superposition Random Access in Massive MIMO Systems

In this letter, we propose a massive multiple input multiple output (MIMO) based preamble-data superposition random access (RA) (mPDSRA) scheme to improve the spectral efficiency scarcity of the conventional massive MIMO based grant-free RA (mGFRA). The key idea of the mPDSRA is to allow a group of RA users transmit preambles, while simultaneously, another group of RA users, whose channel information has already been obtained, transmit data. Thanks to asymptotic orthogonality of channel vectors in massive MIMO, simultaneous transmissions of preamble and data can be allowed and sequentially detected/decoded. Both theoretical analysis and simulation results reveal promising significant performance gains in terms of access success rate and achievable sum rate by the proposed mPDSRA over the conventional mGFRA schemes.

Performance Analysis of Large Intelligent Surfaces (LISs): Asymptotic Data Rate and Channel Hardening Effects

The concept of a large intelligent surface (LIS) has recently emerged as a promising wireless communication paradigm that can exploit the entire surface of man-made structures for transmitting and receiving information. An LIS is expected to go beyond massive multiple-input multiple-output (MIMO) system, insofar as the desired channel can be modeled as a perfect line-of-sight. To understand the fundamental performance benefits, it is imperative to analyze its achievable data rate, under practical LIS environments and limitations. In this paper, an asymptotic analysis of the uplink data rate in an LIS-based large antenna-array system is presented. In particular, the asymptotic LIS rate is derived in a practical wireless environment where the estimated channel on LIS is subject to estimation errors, interference channels are spatially correlated Rician fading channels, and the LIS experiences hardware impairments. Moreover, the occurrence of the channel hardening effect is analyzed and the performance bound is asymptotically derived for the considered LIS system. The analytical asymptotic results are then shown to be in close agreement with the exact mutual information as the number of antennas and devices increase without bounds. Moreover, the derived ergodic rates show that hardware impairments, noise, and interference from estimation errors and the non-line-of-sight path become negligible as the number of antennas increases. Simulation results show that an LIS can achieve a performance that is comparable to conventional massive MIMO with improved reliability and a significantly reduced area for antenna deployment.

Large-System Mutual Information Analysis of Receive Spatial Modulation in Correlated Multi-Cell Massive MIMO Networks

In this paper, the receive spatial modulation (RSM) scheme is studied in the downlink of the multi-cell multi-user systems, assuming that the base station (BS) is equipped with a large number of antennas and serves a large number of multi-antenna users, while the user-antenna ratio is bounded. The system model is practical and applicable in a comprehensive manner, accounting for channel estimation, spatially correlated channels, pilot contamination, and path loss. Combining the concepts of RSM and massive multiple-input multiple-output (MIMO), the spectral efficiency of linear preprocessing zero-forcing (ZF) and regularized ZF (RZF) methods is studied by approximating the probability of antenna detection and deriving a lower bound for the spatial-domain mutual information. With the aid of the large-system analysis, the deterministic equivalent expressions are provided for the achievable rates that are asymptotically exact. The Monte Carlo simulations validate this claim and show that for limited dimensions, the proposed deterministic equivalents yield accurate approximations, even for strongly correlated channels. The results further show that in comparison with the conventional massive MIMO systems with single-antenna users, equipping each user with multiple antennas and applying the RSM scheme enhances the spectral efficiency when each cell is not heavily loaded.

Antenna Selection in TDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) system installs a large number of antennas on base station, it requires one radio frequency (RF) chain for each antenna. When the number of antennas is small, the power consumption of the RF chain is negligible compared with the transmitting power. However, with the increase in the number of antennas and the number of RF chains, the ratio of the power consumption of the RF chain to the total power is gradually increasing. Therefore, the energy efficiency (EE) of the system tends to be saturated gradually and decreases with the further increase of antennas. An antenna selection method based on EE in uplink massive MIMO systems is proposed in this paper. Based on the channel state information (CSI) and the 2-norm of channel matrix, the antennas with better CSI are selected for data transmission. By switching of the RF chain among multiple antennas dynamically over time, simulation results show that the EE can be enhanced compared with conventional massive MIMO system, and the trade-off between EE and spectral efficiency (SE) of the system can be achieved.

Securing Downlink Massive MIMO-NOMA Networks With Artificial Noise

In this paper, we focus on securing the confidential information of massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks by exploiting artificial noise (AN). An uplink training scheme is first proposed with minimum mean squared error estimation at the base station. Based on the estimated channel state information, the base station precodes the confidential information and injects the AN. Following this, the ergodic secrecy rate is derived for downlink transmission. An asymptotic secrecy performance analysis is also carried out for a large number of transmit antennas and high transmit power at the base station, respectively, to highlight the effects of key parameters on the secrecy performance of the considered system. Based on the derived ergodic secrecy rate, we propose the joint power allocation of the uplink training phase and downlink transmission phase to maximize the sum secrecy rates of the system. Besides, from the perspective of security, another optimization algorithm is proposed to maximize the energy efficiency. The results show that the combination of massive MIMO technique and AN greatly benefits NOMA networks in term of the secrecy performance. In addition, the effects of the uplink training phase and clustering process on the secrecy performance are revealed. Besides, the proposed optimization algorithms are compared with other baseline algorithms through simulations, and their superiority is validated. Finally, it is shown that the proposed system outperforms the conventional massive MIMO orthogonal multiple access in terms of the secrecy performance.

Uplink Performance of Cell-Free Massive MIMO with Access Point Selections

Cell-free massive multiple-input multiple-output (MIMO), in which a massive number of access points (APs) distributed over a large area serve a smaller number of users in the same time and frequency resources, inherits advantages from conventional massive MIMO (i.e. favourable propagation and channe

Worst Cell Based Pilot Allocation in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) has been viewed as an advanced technique in future 5G networks. Conventional massive MIMO systems consist of cellular base stations (BS) equipped with a very large number of antennas to simultaneously serve many single-antenna users. Unfortunately, massive MIMO system’s performance is limited by pilot contamination (PC) problem. Conventionally, all users in massive MIMO systems are assigned pilot randomly. In this paper, we propose a pilot allocation algorithm based on a cell with the worst channel quality (WCPA) algorithm to improve the uplink achievable sum rate of the system. Specifically, WCPA exploits the large-scale coefficients of fading channels between the BSs and users. According to the number of available orthogonal pilot sequences, we choose some of the highest inter-cell interfering users and assign each of them a unique pilot sequence if the number of pilot sequences is more than the number of users in a cell. Next, we choose a target cell with the worst channel quality, and gather the highest channel gain user in the target cell and the lowest interfering user in the other cells in the same group in a sequential way by assigning them the same pilot sequence. The simulation results show the outperformance of the proposed algorithm compared to the conventional pilot allocation schemes.

Hybrid Time-Switching and Power Splitting SWIPT for Full-Duplex Massive MIMO Systems: A Beam-Domain Approach

In this paper, we consider the hybrid time switching (TS) and power splitting (PS) simultaneous wireless information and power transfer (SWIPT) protocol design in a full-duplex (FD) massive MIMO system. In this system, an FD base station (BS) serves a set of half-duplex (HD) users and a set of fixed HD sensors. The whole protocol can be divided into two phases based on the idea of TS. The first phase is Training Phase, which is designed for users uplink training and sensors energy harvesting as well as downlink training. Specifically, users transmit uplink pilots for beam-domain (BD) uplink channel estimation at the BS, and the BS transmits energy signals to sensors. Based on the idea of PS, sensors utilize the received energy signals for energy harvesting and BD downlink channel estimation. In the second phase, that is Information Transmission Phase, the BS intelligently schedules users and sensors based on the BD distributions of channels to mitigate self-interference and improve transmission spectral efficiency (SE). Then, the BS forms transmit beamformers for transmitting information to users and receive beamformers for receiving signals transmitted by sensors. By optimizing transmit powers at the BS during the two phases and the TS ratio, the system achievable sum-rate is maximized. Simulation results show the superiority of the proposed protocol on SE compared with conventional massive MIMO SWIPT protocol.

Resource allocation for hybrid TS and PS SWIPT in massive MIMO system

In this paper, we consider the hybrid time switching (TS) and power splitting (PS) simultaneous wireless information and power transfer (SWIPT) protocol design in massive MIMO system. In this system, the base station (BS) simultaneously serves a set of half-duplex (HD) sensor nodes which are uniformly distributed in its coverage. The whole protocol can be divided into two phases based on the idea of TS. The first phase is designed for sensor nodes energy harvesting as well as downlink training. During this phase, the BS transmits energy signals to the sensor nodes. Based on the idea of PS, the sensor nodes utilize the received energy signals for energy harvesting and downlink channel estimation. In the second phase, the BS schedules the sensors intelligently based on the beam-domain distributions of channels to mitigate interference between sensors and enhance transmission spectral efficiency. Then, the BS forms the receive beamformers for the reception of signals transmitted by sensors. By optimizing transmit powers at the BS and the TS ratio, the system achievable sum rate performance is maximized. Simulation results show the superiority of the proposed protocol on spectral efficiency compared with conventional massive MIMO SWIPT protocol.

Massive Connectivity With Massive MIMO—Part II: Achievable Rate Characterization

This two-part paper aims to quantify the cost of device activity detection in an uplink massive connectivity scenario with a large number of devices but device activities are sporadic. Part I of this paper shows that in an asymptotic massive multiple-input multiple-output (MIMO) regime, device activity detection can always be made perfect. Part II of this paper subsequently shows that despite the perfect device activity detection, there is nevertheless significant cost due to device detection in terms of overall achievable rate, because of the fact that non-orthogonal pilot sequences have to be used in order to accommodate the large number of potential devices, resulting in significantly larger channel estimation error as compared to conventional massive MIMO systems with orthogonal pilots. Specifically, this paper characterizes each active user's achievable rate using random matrix theory under either maximal-ratio combining (MRC) or minimum mean-squared error (MMSE) receive beamforming at the base-station (BS), assuming the statistics of their estimated channels as derived in Part I. The characterization of user rate also allows the optimization of pilot sequences length. Moreover, in contrast to the conventional massive MIMO system, the MMSE beamforming is shown to achieve much higher rate than the MRC beamforming for the massive connectivity scenario under consideration. Finally, this paper illustrates the necessity of user scheduling for rate maximization when the number of active users is larger than the number of antennas at the BS.