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In this study, we consider the combination of clustering and resource allocation based on game theory in ultra-dense networks that consist of multiple macrocells using massive multiple-input multiple-output and a vast number of randomly distributed drones serving as small-cell base stations. In particular, to mitigate the intercell interference, we propose a coalition game for clustering small cells, with the utility function being the ratio of signal to interference. Then, the optimization problem of resource allocation is divided into two subproblems: subchannel allocation and power allocation. We use the Hungarian method, which is efficient for solving binary optimization problems, to assign the subchannels to users in each cluster of small cells. Additionally, a centralized algorithm with low computational complexity and a distributed algorithm based on the Stackelberg game are provided to maximize the network energy efficiency (EE). The numerical results demonstrate that the game-based method outperforms the centralized method in terms of execution time in small cells and is better than traditional clustering in terms of EE. [ABSTRACT FROM AUTHOR]

The present paper investigates the trade-offs between the energy efficiency (EE) and spectral efficiency (SE) in the full-duplex (FD) multiuser multi-input multioutput (MU-MIMO) cloud radio access networks (CRANs) with simultaneous wireless information and power transfer (SWIPT). In the considered network, the central unit (CU) intends to concurrently not only transfer both energy and information toward downlink (DL) users using power splitting structures but also receive signals from uplink (UL) users. This communication is executed via FD radio units (RUs) which are distributed nearby users and connected to the CU through limited capacity fronthaul (FH) links. In order to unveil interesting trade-offs between the EE and SE metrics, we first introduce three conventional single-objective optimization problems (SOOPs) including (i) system sum rate maximization, (ii) total power minimization, and (iii) fractional energy efficiency maximization. Then, by making use of the multiobjective optimization (MOO) framework, the MOO problem (MOOP) with the objective vector of the achievable rate and power consumption is addressed. All considered problems are nonconvex with respect to designing variables comprising precoding matrices, compression matrices, and DL power splitting factors; thus, it is extremely intractable to solve these problems directly. To overcome these issues, we develop iterative algorithms by utilizing the sequential convex approximation (SCA) approach for the first two SOO problems and the SCA-based Dinkelbach method for the fractional EE problem. Regarding the MOOP, we first rewrite it as an SOOP by applying the modified weighted Tchebycheff method and, then, propose the iterative algorithm-based SCA to find its optimal Pareto set. Various numerical simulations are conducted to study the system performance and appealing EE-SE trade-offs in the considered system. [ABSTRACT FROM AUTHOR]

This paper studies the energy efficiency (EE) optimization problem in multiple-user multiple-input–multiple-output heterogeneous wireless powered communication network (HWPCN) in which the users in small cells can harvest energy from the small cell base stations in downlink and use harvested energy for uplink transmission. We consider the joint design problem of wireless energy transmission and wireless information transmission precoding matrices subject to the transmit power constraints at each BS and each user. Since the optimization problem under consideration is highly nonlinear non-convex fractional programming in design variables, it is mathematically challenging to obtain the optimal solutions. To tackle with this difficulty, we employ the difference of convex programming to obtain the concave lower bound of the achievable sum rates. Then, we apply the Dinkelbach approach to develop an efficient iterative algorithm in which the convex optimization problems are solved. Simulation results are provided to investigate the EE of the proposed algorithm in HWPCNs. [ABSTRACT FROM AUTHOR]

The present paper is concerned with a sensor network, where each sensor is modeled by either a linear or nonlinear sensing system. These sensors team up in observing either static or dynamic random targets and transmit their observations through noisy communication channels to a fusion center (FC) for locating/tracking the targets. Physically, the network is limited by energy resource. According to the available sum power budget, we develop a novel technique for power allocation to the sensor nodes that enables the FC produce the best linear estimate in terms of the mean square error (MSE). Regardless of whether the sensor measurements are linear or nonlinear, the targets are scalar or vectors, static or dynamic, the corresponding optimization problems are shown to be semidefinite programs (SDPs) of tractable optimization and thus are globally and efficiently solved by any existing SDP solver. In other words, new tractably computational algorithms of distributed Bayes filtering are derived with full multisensor diversity achieved. Intensive simulation shows that these algorithms clearly outperform previously known algorithms. [ABSTRACT FROM PUBLISHER]