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In this letter, we investigate user-centric access point (AP) selection for cell-free massive multiple-input multiple-output (MIMO) systems. In contrast to existing works, we study this problem from the perspective of game theory and model user-centric AP service cluster formation as a local altruistic game, which is proven to be an exact potential game (EPG) and to have at least one pure Nash equilibrium (NE). Then, an improved maximum non-neighbor-set-based concurrent spatial adaptive play (MNSB-C-SAP) algorithm is proposed to achieve the NE. Simulation results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and verify the superiority of the game-theoretic AP selection method compared with some existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an unmanned aerial vehicle (UAV) communication system, where the trajectories of multi-UAVs are designed for the data collection mission of IoT nodes. We aim at minimizing the mission time with constraints of UAV’s maximum speed and acceleration, the collision avoidance, and communication interference among UAVs. We propose a three-step approach to solve this problem, which is based on the K-means algorithm, and Deep Reinforcement Learning (DRL) with a distributed manner and a centralized manner. The mutual influences like collision avoidance and interference among UAVs are explicitly expressed in our algorithm. Numerical results show the advantage of our proposed approach. [ABSTRACT FROM AUTHOR]

To meet the demand of green communications of ultradense mobile devices (MDs), an energy-efficient mechanism, jointly considering the computation offloading and resource management, is designed to minimize the network-wide weighted energy consumption under the delay constraints of MDs for ultradense heterogeneous networks. Such a mechanism tightly integrates with the adjustment of computation capability and transmission power of MDs. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we design an effective algorithm to solve it. Specifically, by utilizing the powerful global searching capability of genetic algorithm (GA) and the accurate local searching capability of particle swarm optimization (PSO), the adaptive GA with diversity-guided is firstly used for coarse-grained search, and then adaptive PSO is utilized for fine-grained search. After that, some detailed analyses on the convergence, computation complexity and parallel implement are provided for algorithms. Simulation results show that the designed algorithm can achieve a lower network energy consumption than other offloading algorithms in general. At the same time, the numerical simulation also reveals that the designed algorithm may be more suitable for ultradense networks than existing algorithms. [ABSTRACT FROM AUTHOR]

In this article, we propose a cognitive relaying (CR) scheme with both the secondary source (SS) and the secondary relay (SR) capable of harvesting wireless energy. Along with the data transmission of primary system, a power beacon actively transfers wireless power to SS and SR. The interference from the primary data transmission can increase the amount of harvested energy at SS and SR. After harvesting and accumulating energy for a number of successive time blocks, the CR will be performed in two continuous time blocks. In CR-Block-One, SS will transmit the secondary data to SR by exhausting its available energy. In CR-Block-Two, SR will forward the secondary data to SD if it has correctly decoded the data from SS; otherwise, it will stay silent. We properly model the energy accumulation process of SS and SR by defining a group of discrete energy levels. We analyze the approximate throughputs of both systems by considering various transmit powers of SS and SR. The optimal number of time blocks used for the wireless power transfer is determined through maximizing the secondary throughput while slightly degrading the primary performance. Numerical results show that the secondary data transmission can be well accommodated. [ABSTRACT FROM AUTHOR]

Direct-conversion receivers (DCRs) have been adopted in wideband communication systems owing to their simple structure and low cost, however, their operation is affected by amplitude and phase mismatches between their analog inphase (I) and quadrature (Q) branches, as well as the discrepancy of low-pass filter coefficients between these two channels. In this paper, a blind adaptive frequency-dependent I/Q imbalance compensator is proposed, which exploits the complex properness (second-order circularity) of ideal constellation mappings to provide more enhanced insight into the problem setting within the proposed compensator. This serves as a basis for a novel full second-order performance assessment framework, which is established through a joint consideration of the weight error covariance and complementary covariance in both the transient and steady-state stages. This conjoint analysis is further shown to facilitate accurate quantification of the overall mirror-frequency interference attenuation capability of the proposed compensator. Simulation results in an orthogonal frequency division multiplexing (OFDM) transmission system demonstrate the excellent performance of the proposed compensator. [ABSTRACT FROM AUTHOR]

In this paper, we address the downlink resource (subcarriers and power jointly) allocation problem for energy harvesting-based device-to-device communication in a railway carriage communication network to improve the energy efficiency (EE) of the system. The considered problem is formulated as maximizing the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, we first propose a new performance metric for evaluating the EE and optimize its lower bound. However, there exists an intractable issue of mixing the integer nature into the feasible region. To this end, we decompose the optimization problem into two subproblems by fixing the subcarrier and power allocations alternately. These two subproblems are formulated as two exact potential games, and the optimal properties of their solutions are analyzed. Accordingly, we respectively design a virtual distributed learning algorithm for the power control to find the optimum solution, i.e., Nash equilibrium (NE) point, based on the derived conditions of the uniqueness of NE, which can effectively accelerate convergence, and a fully distributed Max-logit algorithm for the subcarrier allocations to obtain the best NE with an arbitrarily high probability in which only local information needs to be exchanged. Through the alternation of two algorithms and iterative operation, the optimal solution to the problem is achieved. Finally, numerical results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

With the increase of access point (AP) density and the exponential growth of mobile devices supported by ultradense networks (UDNs), user-centric overlapped clustering relying on AP cooperation is becoming a promising design principle for guaranteeing the quality of service (QoS) required by each user equipment. This ambitious goal can be achieved by the joint design of user-centric overlapped clustering and resource allocation. In this context, both the traffic-load balancing and the limited availability of orthogonal resource blocks (RBs) have to be carefully considered in UDNs. To tackle these challenges, we formulate a joint user-centric overlapped clustering and resource allocation problem with the goal of maximizing the system's spectral efficiency. To efficiently solve this problem, it is decoupled into two subproblems, which can be solved independently with the aid of our graph–theoretical framework. We first develop a novel distributed three-stage user-centric clustering solution, which is aware of the APs’ traffic-loads. Then, based on the overlapped clusters constructed, we propose a novel two-stage graph-based user-centric resource allocation scheme for mitigating the resultant inter-cluster interference, despite the limited availability of orthogonal RBs. Our numerical results confirm the benefits of the proposed framework and show that it outperforms the benchmark solutions in terms of both its per area aggregated user rate and user rate. [ABSTRACT FROM PUBLISHER]

Instead of achievable rate in the conventional association, we utilize the long-term rate to design two types of association strategies for load balancing in heterogeneous cellular networks (HCNs). In the first type, we try to balance the network loads by maximizing the weighted sum of long-term rates. Based on this type, we introduce a power control (PC) to mitigate the network interference and reduce the energy consumption in the second type. Since the long-term rate of any user associated with some base stations (BSs) mainly depends on the achievable rate received by this user from the selected BS and the load of this BS, it can be used as a key factor of association design for load balancing in HCNs. To solve the first association problem, we design a one-layer iterative algorithm by converting the original sum-of-ratio form into a parameterized polynomial one. By combining it with a PC algorithm, we give a two-layer iterative solution for the second problem. Specifically, the outer layer performs a user association using the algorithm mentioned in the first type, and the inner layer updates the transmit power of each BS using a power update function. At last, we give some convergence and complexity analyses for these proposed algorithms. As shown in numerical results, the proposed strategies have a superior performance than the conventional association, and the second association strategy achieves a higher load balancing gain and energy efficiency than conventional scheme and other offloading strategies. [ABSTRACT FROM AUTHOR]

In this letter, we design an association strategy to maximize the weighted sum energy efficiency (EE) for massive multiple-input and multiple-output (MIMO)-enabled heterogeneous cellular networks. Considering that the final formulated problem is in a sum-of-ratio form, we first need to transform it into a parametric nonfractional form, by which we can achieve its solution through a two-layer iterative algorithm. The outer layer searches the EE parameters and the multipliers associated with signal-interference-plus-noise ratio constraints using Newton-like method, and the inner layer optimizes the association indices using Lagrange multiplier method. Then, we give some convergence and complexity analyses for the proposed algorithm. Numerical results show that the proposed scheme significantly outperforms the existing one on the system throughput and network EE under a certain condition. In addition, we also investigate the impacts of the number of massive antennas and the transmit power of each pico base station on the association performance. [ABSTRACT FROM PUBLISHER]

This letter studies the cooperative precoding design for a coexisting wireless energy transfer (WET) and cognitive radio (CR) system, where the WET system share the same spectrum with the CR system. Different from the traditional wireless networks, interference here is regarded as a useful rather than harmful resource. Specifically, we address the transmit covariance design to minimize the total transmit power at the energy transmitter and the secondary transmitter while satisfying secrecy rate, energy harvesting, and interference temperature constraints. We propose an iterative algorithm to tackle the formulated nonconvex optimization problem, and prove that it could converge to a Karush–Kuhn–Tucker point of the original problem. Simulation results are finally provided to illustrate the effectiveness of our proposed algorithm. [ABSTRACT FROM PUBLISHER]

The cellular communications scenario involving the coexistence of the one-hop direct transmission and the two-hop relaying is studied in this paper. In contrast to conventional cellular systems featuring orthogonal information flows served by decoupled channel resources via, e.g., time division, we propose a novel protocol in which two information flows are served simultaneously via the shared channel resource, thus constituting a spectral-efficient solution for cellular communications. On the other hand, however, an inevitable issue associated with the proposed protocol is the interflow interference, which may lead to serious deterioration on both information flows. To tackle this issue, we develop an overhearing-based protocol that utilizes the overheard interference as useful side information in the receiver design. Specifically, depending on the interference levels, an adaptive linear minimum mean squared error (MMSE) and nonlinear MMSE successive interference cancellation (SIC) receiver exploiting the overheard interference at the direct mobile terminal is developed. To balance between the two information flows, we develop the asymptotically optimum superposition coding at the base station (BS) in the high-power regime. Furthermore, the optimum relay beamforming matrix maximizing the bottleneck of the achievable rates of the two information flows is developed subject to a finite power constraint. Finally, simulations demonstrate a remarkable throughput gain over the conventional cellular systems. [ABSTRACT FROM AUTHOR]

In this paper, an overhearing protocol is proposed for two-way cooperative multiantenna relaying systems, where the relays equipped with multiple antennas collaborate to relay signals between the base station (BS) and two user equipment units (UEs). In the proposed overhearing protocol, the UE in the uplink transmission phase transmits only in the first time slot, i.e., it remains silent in the second time slot, whereas the previous overhearing protocol assumes that the UE transmits also in the second time slot. Therefore, the proposed overhearing protocol is more power efficient. The precoding matrix at each cooperative relay is optimized in the sense of minimizing the weighted mean squared error (WMSE). Simulation results show that the proposed scheme shows not only lower mean squared error but also higher achievable sum rate than existing cooperative relaying schemes. [ABSTRACT FROM AUTHOR]

The conventional two-way relaying (TWR) protocol requires that the transmitter (receiver) in one direction must be the receiver (transmitter) in the other direction, a limitation precluding the application of the TWR for sophisticated real-world wireless networks. In this paper, we study a more general multicell system consisting of a downlink (DL) traffic in one cell and an uplink (UL) traffic in an adjacent cell, with a multiantenna relay located in the cell edge and shared by both cells. For the coexistence of DL and UL transmissions, we propose exploiting the overheard signals from the adjacent cell (commonly known as the intercell interference) to improve the quality of signal reception in both cells. To reduce the power consumption to suit for green networks, we design the optimum relay precoder to minimize the total power at the relay yet satisfying the rate constraints for both the DL and UL traffic flows. The original precoder design is a nonconvex problem that is difficult to solve. To make the problem tractable, we transform the nonconvex problem to an equivalent quadratically constrained quadratic program (QCQP), which is then solved by the semidefinite relaxation (SDR) technique. Finally, simulations validate the effectiveness of our proposed protocol together with the optimized relay precoder. [ABSTRACT FROM PUBLISHER]