RIS-assisted cell-free massive MIMO systems with reflection area: AP number reduction

Cell-free massive multiple-input multiple-output (MIMO) has been regarded as a promising technology due to high spectral efficiency. However, as large number of access points (APs) are deployed with fibers connecting to the central processing unit, the increase of energy consumption and hardware cost raise concerns. The reconfigurable intelligent surface (RIS) with impressing potential for low energy and cost finds a way to solve this problem. In this paper, we investigate the performance of the cell-free massive MIMO system with a RIS. As RIS can only reflect signals from the front, user equipments (UEs) and APs are divided into two categories according to their relative position with the RIS, i.e., one is on the reflection area of the RIS and the other is not. A closed-form approximation of the UE achievable downlink rate is derived. Based on it, we obtain the optimal RIS position and phase shift that can maximize the UE sum rate, through alternating optimization method. It is found that compared with the cell-free massive MIMO system without RIS, to achieve the same rate performance, the number of required AP in the RIS-assisted system can be significantly reduced. Moreover, as the RIS component increases, the number of required AP can be reduced almost linearly without rate reduction, which means the hardware and energy cost can be greatly cut down. Furthermore, from our simulation results, we can see that when users are densely distributed, the optimal location of the RIS should be closer to users. When users are uniformly distributed, the optimal position of RIS is close to the central position.     

Performance enhancement in the cell-free massive MIMO SWIPT system with active eavesdropping

This paper investigates the secure transmission for simultaneous wireless information and power transfer (SWIPT) in the cell-free massive multiple-input multiple-output (MIMO) system. To develop green communication, legitimate users harvest energy by the hybrid time switching (TS) and power splitting (PS) strategy in the downlink phase, and the harvested energy can provide power to send uplink pilot sequences for the next time slot. By in-built batteries, the active eavesdropper can send the same pilots with the wiretapped user, which results in undesirable correlations between the channel estimates. Under these scenarios, we derive the closed-form expressions of average harvested energy and achievable rates, and propose an iterative power control (PC) scheme based on max–min fairness algorithm with energy and secrecy constraints (MMF-ESC). This scheme can ensure the uniform good services for all users preserving the distributed architecture advantage of cell-free networks, while meeting the requirements of energy harvested by legitimate users and network security against active eavesdroppers. Besides, continuous approximation, bisection and path tracking are jointly applied to cope with the high-complexity and non-convex optimization. Numerical results demonstrate that MMF-ESC PC scheme can effectively increase the achievable rate and the average harvested energy of each user, and decrease the eavesdropping rate below the threshold. Moreover, the results also reveal that PS strategy is superior in harvesting energy in terms of more stringent network requirements for average achievable rates or security.     

Rate analysis of ZF receiver for uplink cell-free massive MIMO systems with D2D communications

This paper proposes a transmission structure of zero forcing (ZF) receiver for uplink cell-free massive multiple-input multiple-output (MIMO) systems with device-to-device (D2D) communications, followed by a rate analysis. We assumed that D2D users (DUEs) can utilize orthogonal radio resources to improve the efficiency of the scarce utilization or repurpose the time–frequency-spectrum resources currently used by the cell-free users (CFUEs). Assuming that the imperfect channel state information (CSI) is realizable, after that, the use-and-forget bounding technique is then used to respectively obtain the closed-form expressions of the CFUEs and DUEs, which provide the lower bounds on the ergodic approximate realizable rate of both communication links. First, we calculate the minimum-mean-square error (MMSE) estimation for all channels. Then, the derived results of the achievable uplink sum rate provide us with a tool that enables us to explain how some important parameters, such as the number of access points (APs)/CFUEs, each AP/CFUE/antenna, and the density of DUEs, affect system performance, highlighting the significance of cooperation between cell-free massive MIMO and D2D communication.     

Spectral and energy efficiency for uplink massive MIMO systems with mixed-ADC architecture

In this paper, we introduce a mixed- analog-to-digital converter (ADC) architecture for massive multiple-input multiple-output (MIMO) systems and study the system’s performance mainly includes the achievable spectral efficiency and energy efficiency. In principle, the mixed-ADC architecture permits the one part of antennas at the base station (BS) are connected to speed and expensive full-resolution ADCs and the remaining part of the antennas are connected to the cheap low-resolution ADCs. By applying the general maximum-ratio combining detector, a tractable approximate expression for the achievable SE is obtained. Leveraging on the derived results, the effects of the number of BS antennas and the percent of the full-resolution ADCs on the achievable SE are investigated. Results show that the achievable SE increases with the percent of the full-resolution ADCs and the number of BS antennas. Based on a realistic power consumption model, we evaluate the energy efficiency for the considered mixed-ADC architecture. Moreover, under the certain achievable SE constraint, we maximize the energy efficiency by adjusting the number of low-resolution ADCs and the resolution bits of the corresponding ADC device. Numerical results showcase that the energy efficiency can be improved by enhancing the average transmitted power, and there exists an optimal number of resolution bits and the number of antennas to maximize the energy efficiency, which indicates that the application of mixed-ADC architecture has a great potential in future mobile communication system.     

An overview of simultaneous wireless information and power transfer in massive MIMO networks: A resource allocation perspective

Providing a stable and perpetual source of energy to charge battery-powered wireless communication devices is viewed as a major challenge in wireless communication systems. This challenge leads to the trending research area where radio frequency signals are being exploited for energy harvesting purposes. The technique for achieving this is known as simultaneous wireless information and power transfer (SWIPT). In recent studies on SWIPT, the massive Multiple-Input-Multiple-Output (MIMO) aided energy harvesting has attracted considerable attention from the research community. This can be attributed to the high energy delivery rate of massive MIMO antenna systems due to their capacity to focus transmitted signals in the direction of the intended receivers. However, SWIPT in massive MIMO networks requires an optimal design to achieve a proper balance between different conflicting network objectives. In this article, we aim to discuss various contributions to SWIPT in massive MIMO networks in order to address critical design issues. In particular, we focus on the widely adopted approach to resolving SWIPT-related issues in massive MIMO networks, that is, the resource allocation design. We also extend our discussion to studies dedicated to solving critical design challenges. In this regard, we take into consideration the energy efficiency and security aspect of the system design. Finally, we identify potential areas that can be explored for future research work.     

Joint power allocation and user assignment for licensed/unlicensed band users in massive MIMO cellular systems

In this paper, we study the joint user assignment and power allocation for the defined utility function (central cell throughput) maximization in massive Multiple Input-Multiple Output (MIMO) cellular system coexistence with Wireless Fidelity (WiFi) network. Firstly, the power allocation of problem is formulated as a convex optimization. Unfortunately, the formulated problem has not a closed-form solution. For solving the mentioned problem, it is converted to three sub-problem based on the number of lemmas that are expressed. Due to two of these problems remain difficult to solve, this two sub-problem are relaxed. The Ellipsoid algorithm is an iterative algorithm that used for solving of the relaxed problems. In the following, joint user assignment and power allocation will be addressed, in which two approaches are proposed for solving. In the first approach, we propose an iterative algorithm that user assignment problem and power allocation problem are solved in each iteration. In the second approach, at first, users are assigned to licensed and unlicensed bands, then for the obtained arrangement, the power allocation problem is solved. The simulation results showed that the proposed algorithms are significantly close to the benchmark methods.     

An effective PSO-based power allocation for target tracking in MIMO radar with widely separated antennas

In MIMO radar with widely separated antennas, the antennas are spaced far from each other and the target is seen from different angles. In this type of radars, each receiver collects all transmit signals and transmits them to the central processor unit. Power allocation is an important part of military operations. Therefore, it is a primary factor that requires to be taken into account in the designing of target tracking problems in MIMO radar. In fact, the power allocation finds an optimum strategy to allot power to transmit antennas with the goal of minimizing the target tracking errors under specified transmit power constraints. In this paper, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. For this purpose, first, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is computed. This is applied as a power allocation problem objective function. Because a complex Gaussian model is considered for target radar cross-section (RCS), this function becomes complicated. Due to the nonlinearity of this objective function, the proposed power allocation problem is nonconvex. Therefore, a particle swarm optimization (PSO) -based power allocation algorithm is proposed to solve it. In simulation experiments, the performance of the proposed algorithm in different conditions such as a different number of antennas and antenna geometry configurations is evaluated. Results prove the validity of the proposed algorithm.