This paper explores the energy efficiency (EE) of integrated sensing and communication (ISAC) systems employing massive multiple-input multiple-output (mMIMO) techniques to leverage spatial beamforming gains for both communication and sensing. We focus on an mMIMO-ISAC system operating in an orthogonal frequency-division multiplexing setting with a uniform planar array, zero-forcing downlink transmission, and mono-static radar sensing to exploit multi-carrier channel diversity. By deriving closed-form expressions for the achievable communication rate and Cramér-Rao bounds (CRBs), we are able to determine the overall EE in closed-form. A power allocation problem is then formulated to maximize the system’s EE by balancing communication and sensing efficiency while satisfying communication rate requirements and CRB constraints. Through a detailed analysis of CRB properties, we reformulate the problem into a more manageable form and leverage Dinkelbach’s and successive convex approximation (SCA) techniques to develop an efficient iterative algorithm. A novel initialization strategy is also proposed to ensure high-quality feasible starting points for the iterative optimization process. Extensive simulations demonstrate the significant performance improvement of the proposed approach over baseline approaches. Results further reveal that as communication spectral efficiency rises, the influence of sensing EE on the overall system EE becomes more pronounced, even in sensing-dominated scenarios. Specifically, in the high ω regime of 2 × 10−3, we observe a 16.7% reduction in overall EE when spectral efficiency increases from 4 to 8 bps/Hz, despite the system being sensing-dominated.

In this paper, we address dynamic network selection problems of mobile users in an intelligent reflecting surface (IRS)-enabled wireless network. In particular, the users dynamically select different service providers (SPs) and network services over time. The network services are composed of adjustable resources of IRS and transmit power. To formulate the SP and network service selection, we adopt an evolutionary game in which the users are able to adapt their network selections depending on the utilities that they achieve. For this, the replicator dynamics is used to model the service selection adaptation of the users. To allow the users to take their past service experiences into account their decisions, we further adopt an enhanced version of the evolutionary game, namely fractional evolutionary game, to study the SP and network service selection. The fractional evolutionary game incorporates the memory effect that captures the users’ memory on their decisions. We theoretically prove that both the game approaches have a unique equilibrium. Finally, we provide numerical results to demonstrate the effectiveness of our proposed game approaches. In particular, we have reveal some important finding, for instance, with the memory effect, the users can achieve the utility higher than that without the memory effect.

In this paper, we consider an integrated data and energy network and D2D communication coexistence (DED2D) system. The DED2D system allows a base station (BS) to transfer data to information-demanded users (IUs) and energy to energy-demanded users (EUs), i.e., using a time-fraction-based information and energy transfer (TFIET) scheme. Furthermore, the DED2D system enables D2D communications to share spectrum with the BS. Therefore, the DED2D system addresses the growth of energy and spectrum demands of the next generation networks. However, the interference caused by the D2D communications and propagation loss of wireless links can significantly degrade the data throughput of IUs. To deal with the issues, we propose to deploy an intelligent reflecting surface (IRS) in the DED2D system. Then, we formulate an optimization problem that aims to optimize the information beamformer for the IUs, energy beamformer for EUs, time fractions of the TFIET, transmit power of D2D transmitters, and reflection coefficients of the IRS to maximize IUs’ worse throughput while satisfying the harvested energy requirement of EUs and D2D rate threshold. The max-min throughput optimization problem is computationally intractable, and we develop an alternating descent algorithm to resolve it with low computational complexity. The simulation results demonstrate the effectiveness of the proposed algorithm.

Improper Gaussian signaling (IGS) has shown its capability of improving the rate of interference-limited networks by exploiting the additional degrees of freedom in signal processing. This article considers a system of a multiple-input single-output (MISO) cellular network coexisting with device-to-device (D2D) communication, where the former employs proper Gaussian signaling (PGS) but the latter employs IGS to improve D2D's rate and also to mitigate interference to the former. Both non-orthogonal and orthogonal bandwidth sharing between cellular users (CUs) and D2D pairs are considered. The problems of joint bandwidth allocation and signal beamforming to maximize the minimum CUs' rate subject to the transmit power budget and D2D's rate threshold are addressed, which pose critical computational challenges. Path-following algorithms of low complexity are developed for computational solutions. Two distinct scenarios, i.e., unmanned aerial vehicle (UAV)-enabled networks, and MISO cellular networks are simulated to give insight into the superiority of using IGS over PGS. Our results reveal that in UAV-enabled networks, orthogonal sharing produces a higher D2D's rate, while non-orthogonal sharing offers better CUs' rate under practical levels of D2D's rate. In MISO systems, IGS is a game-changer, which enables the orthogonal sharing to uniformly outperform the non-orthogonal sharing in terms of CUs' rate.

Caching and multicast have surged as effective tools to alleviate the heavy load from the backhaul links while enabling content-centric delivery in communication networks. The main focus of work in this area has been on the cache placements to manage the network delay and backhaul transmission cost. An important issue of optimizing the cost efficiency in content delivery has not been addressed. This paper tackles this issue by proposing collaborative multicast beamforming in cache-enabled ultra-dense networks. The objective is to maximize the cost efficiency, which is defined as the ratio of the content throughput to the sum of power consumption and backhaul cost, in providing quality-of-service for content delivery. Zero-forcing beamforming and generalized zero-forcing beamforming are employed to force the multi-content interference to zero or mitigate it while amplifying the desired signals for users. These problems of collaborative multicast beamforming design are computationally difficult. Path-following algorithms, which invoke a simple convex quadratic program at each iteration, are developed for their solution. Numerical results are provided to demonstrate the computational efficiency of the proposed algorithms and also give insights into the impact of caching on the cost efficiency.

In this study, the performance of energy harvesting systems in the presence of unreliable backhaul links is investigated over independent but not necessarily identically distributed Nakagami‐ m fading channels. In particular, the energy‐constrained relay uses the amount of harvested energy from the best small‐cell transmitter based on time switching‐based relaying protocol to process for the next hop transmission. The authors derived the closed‐form expressions of the outage probability and the effective throughput with two distinct transmission schemes: (i) delay‐limited transmission, and (ii) delay‐tolerance transmission are attained. In order to assess the impacts of unreliable backhaul links, they thus obtain the asymptotic expression of the outage probability in high signal‐to‐noise ratio (SNR) regime. The numerical results are conducted to analyse the effects of energy harvesting fraction time, energy efficiency, backhaul reliability, and the fading parameters on the system performance. The authors' results show that under the unreliable backhaul links the outage probability yields the error‐floor in the high SNR regime which demonstrates the significant impact of the backhaul unreliability.

In this paper, we investigate the impacts of unreliable backhaul connections on cognitive heterogeneous networks with best relay selection. Since spectrum sharing is employed, the transmit powers of the small-cell transmitter and relays are constrained by the peak interference at the primary user, as well as their maximum transmit powers. The closed-form expressions of the outage probability, ergodic capacity and symbol error rate are derived along with the asymptotic performance to get full insights. Our results show that the backhaul reliability is a limiting factor of the system performance.