This study considers a point-to-point wireless link, in which both the transmitter and receiver are equipped with multiple antennas. In addition, two nearly-passive reconfigurable metasurfaces are deployed, one in the immediate vicinity of the transmitter, and one in the immediate vicinity of the receiver. In this scenario, the system energy efficiency is optimized with respect to the transmit covariance matrix, and the reflection matrices of the two metasurfaces. A low-complexity algorithm is developed, which is converges to a first-order optimal point of the maximization problem.Moreover, closed-form expressions are derived for the metasurface matrices in the special case of single-antenna or single-stream transmission. A numerical performance analysis shows, in particular, that the considered architecture can provide significant energy efficiency gains compared to fully digital beamforming architectures.

This work addresses the problem of secrecy energy efficiency (SEE) maximization in RIS-aided wireless networks. The use of active and nearly-passive RISs are compared and their trade-off in terms of SEE is analyzed. Considering both perfect and statistical channel state information, two SEE maximization algorithms are developed to optimize the transmit powers of the mobile users, the RIS reflection coefficients, and the base station receive filters. Numerical results quantify the trade-off between active and nearly-passive RISs in terms of SEE, with active RISs yielding worse SEE values as the static power consumed by each reflecting element increases.

Hybrid Wireless Body Area Networks (HyWBANs) are revolutionizing healthcare by integrating joint sensing and communication capabilities. However, this advancement introduces critical security challenges, as attackers can exploit sensing channels to intercept sensitive medical data. This paper introduces Secrecy Energy Efficiency (SEE) as a new performance metric for hybrid radio-optical wireless networks, enabling a quantitative assessment of secure communication under power-constrained conditions. We formulate and solve optimization problems to maximize the optical secrecy rate and SEE. We extend this analysis to a joint allocation framework for Ultra Wideband (UWB) and Near-Infrared (NIR) channels. Our approach leverages Sequential Fractional Programming (SFP), which enables to tackle the non-convex SEE maximization problem by a sequence of convex problems, addressing secure transmissions' inherent non-convexity and fractional nature with intentional jamming. Based on lab-based in-body measurements through porcine tissue and on radio and optical average synthetic phantoms, numerical evaluations demonstrate that the NIR link can achieve approximately 3 bit/Hz/Joule in SEE. Further, we show that optimal power allocation significantly outperforms random allocation methods, highlighting the potential of this approach for mission-critical healthcare applications. These findings provide a robust foundation for designing next-generation, low-power medical communication systems that balance security requirements with stringent energy constraints.

This paper investigates channel-aware decision fusion empowered by massive MIMO systems and reconfigurable intelligent surfaces (RIS). By integrating both, we aim to improve goal-oriented (fusion) performance despite the unique propagation challenges introduced. Specifically, we investigate traditional favorable propagation properties in the context of RIS-aided Massive MIMO decision fusion. The above analysis is then leveraged (i) to design three sub-optimal simple fusion rules suited for the large-array regime and (ii) to devise an optimization criterion for RIS reflection coefficients based on long-term channel statistics. Simulation results confirm the appeal of the presented design.

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs) utilizing channel-aware binary-decision fusion over a shared flat-fading channel. A reconfigurable metasurface, positioned in the near-field of a limited number of receive antennas, is integrated to enable a holographic Decision Fusion (DF) system. This approach minimizes the need for multiple RF chains while leveraging the benefits of a large array. The optimal fusion rule for a fixed metasurface configuration is derived, alongside two suboptimal joint fusion rule and metasurface design strategies. These suboptimal approaches strike a balance between reduced complexity and lower system knowledge requirements, making them practical alternatives. The design objective focuses on effectively conveying the information regarding the phenomenon of interest to the FC while promoting energy-efficient data analytics aligned with the Internet of Things (IoT) paradigm. Simulation results underscore the viability of holographic DF, demonstrating its advantages even with suboptimal designs and highlighting the significant energy-efficiency gains achieved by the proposed system.

This work proposes a provably convergent and low complexity optimization algorithm for the maximization of the secrecy energy efficiency in the uplink of a wireless network aided by a reconfigurable intelligent surface (RIS), in the presence of an eavesdropper. The mobile users' transmit powers and the RIS reflection coefficients are optimized. Numerical results show the performance of the proposed methods and compare the use of active and nearly-passive RISs from an energy-efficient perspective.

This work addresses the topic of energy efficiency (EE) maximization in wireless networks aided by a reconfigurable intelligent surface (RIS), considering both active and nearly-passive RISs. Moreover, unlike available literature, the analysis considers that the RIS reflection constraints are not enforced separately on each reflecting element, but the RIS is characterized by a single reflection constraint that is jointly enforced on all the reflecting elements. This allows for a wider set of feasible RIS reflection matrices, which leads to better performance, which is theoretically analyzed. Then, two provably convergent energy efficiency maximization algorithms with polynomial complexity are developed, which optimize the reflection coefficients of the RIS, the mobile users’ transmit powers, and the linear filters at the base station. Numerical results are illustrated to quantify the performance of the proposed methods and identify the operating points in which active or nearly-passive RISs should be preferred from the energy efficiency standpoint.

This work addresses the issue of energy efficiency maximization in a multi-user network aided by a reconfigurable intelligent surface (RIS) with global reflection capabilities. Two optimization methods are proposed to optimize the mobile users’ powers, the RIS coefficients, and the linear receive filters. Both methods are provably convergent and require only the solution of convex optimization problems. The numerical results show that the proposed methods largely outperform heuristic resource allocation schemes.

This work addresses the comparison between active and passive RISs in wireless networks, with reference to the system energy efficiency (EE). Two provably convergent and computationally-friendly EE maximization algorithms are developed, which optimize the reflection coefficients of the RIS, the transmit powers, and the linear receive filters. Numerical results show the performance of the proposed methods and discuss the operating points in which active or passive RISs should be preferred from an energy-efficient perspective.

In this paper, we employ a user-centric (UC) cell-free massive MIMO (CFmMIMO) network for providing ultra reliable low latency communication (URLLC) when traditional ground users (GUs) coexist with unmanned aerial vehicles (UAVs). We study power control in both the downlink and the uplink when partial zero-forcing (PZF) transmit/receive beamforming and maximum ratio transmission/combining are utilized. We consider optimization problems where the objective is to maximize either the users’ sum URLLC rate or the minimum user’s URLLC rate. The URLLC rate function is both complicated and nonconvex rendering the considered optimization problems nonconvex. Thus, we propose two approximations for the complicated URLLC rate function and employ successive convex optimization (SCO) to tackle the considered optimization problems. Specifically, we propose the SCO with iterative concave lower bound approximation (SCO-ICBA) and the SCO with iterative interference approximation (SCO-IIA). We provide extensive simulations to evaluate SCO-ICBA and SCO-IIA and compare UC CFmMIMO deployment with traditional colocated massive MIMO (COmMIMO) systems. The obtained results reveal that employing the SCO-IIA scheme to optimize the minimum user’s rate for CFmMIMO with MRT in the downlink, and PZF reception in the uplink can provide the best corresponding URLLC rate performances.

This paper addresses the problem of co-existence between a radar and a communication system that share the same frequency band. In particular, we investigate the role of a reconfigurable intelligent surface (RIS) in improving the performance of both systems and facilitating their co-existence. We consider the optimization problem of maximizing the radar signal to interference plus noise ratio (SINR) with respect to the active transmit beamformer at the radar, the passive RIS reflection coefficients, and the transmit covariance matrices of the communication system, subject to communication outage as well as radar and communication power constraints. This problem is solved through an alternating maximization procedure, first in the ideal scenario of perfect channel state information (CSI), and then in the case of incomplete CSI, using a statistical model of the CSI uncertainty. Numerical results demonstrate the effectiveness of our approach and quantify the beneficial effect of an RIS on the co-existence of a radar and a communication system.

This work considers a multi-cell, multi-carrier massive MIMO network with carrier aggregation, and tackles the rate versus power consumption trade-off, by jointly optimizing the number of employed component carriers, active antennas, base station density, and transmit power. A provably convergent algorithm is developed together with closed-form results for the individual optimization of the considered resources. Numerical results show how carrier aggregation can effectively reduce the power consumption without sacrificing the rate performance.

This work analyzes the use of reconfigurable meta-surfaces as a more energy-efficient transceiver technology than traditional active antenna arrays. A wireless link is considered, in which both the transmitter and receiver are equipped with a single antenna that illuminates a passive meta-surface placed in the vicinity of the transmit/receive antenna. The rate and energy efficiency of this system are optimized with respect to the phase shifts applied by the transmit and receive meta-surfaces. Numerical results show that the use of passive meta-surfaces can significantly improve the system energy efficiency without reducing the system rate when compared to a similar multiple-input multiple-output (MIMO) system without meta-surface.

This work considers the problem of energy efficiency (EE) maximization in a reconfigurable intelligent surface (RIS)-aided multiple input multiple output (MIMO) communication link, subject to maximum power constraints and to additional constraints on the maximum exposure of the end-users to electromagnetic radiations. The RIS phase shifts, the transmit beamforming, the linear receive filter, and the transmit power are jointly optimized, and two provably convergent and low-complexity algorithms are developed. One algorithm applies to the general system setup, but does not guarantee global optimality. The other is provably optimal in the notable special case of isotropic electromagnetic field (EMF) exposure constraints. The numerical results show that an RIS ensures an EE of the same order of magnitude as when no EMF constraints are enforced.

The potential of intelligent reflecting surfaces (IRSs) is investigated as a promising technique for enhancing the energy efficiency of wireless networks. Specifically, the IRS enables passive beamsteering by employing many low-cost individually controllable reflect elements. The resulting change of the channel state, however, not only increases the signal quality but also the interference at the users. To counteract this negative side effect, we employ rate splitting (RS), which inherently is able to mitigate the impact of interference. We facilitate practical implementation by considering a Cloud Radio Access Network (C-RAN) at the cost of finite fronthaul-link capacities, which necessitate the allocation of sensible user-centric clusters to ensure energy-efficient transmissions. Dynamic methods for RS and the user clustering are proposed to account for the interdependencies of the individual techniques. Numerical results show that the dynamic RS method establishes synergistic benefits between RS and the IRS. Additionally, the dynamic user clustering and the IRS cooperate synergistically, reflected by increased individual gains for the dynamic clustering. Interestingly, with an increasing fronthaul capacity, the gain of the dynamic user clustering decreases, while the gain of the dynamic RS method increases. Around the resulting intersection, both methods affect the system concurrently, improving the energy efficiency drastically.

The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which the reflecting elements only employ random phase rotations without requiring any CSI. The only CSI then needed is at the base station (BS) of the overall channel to implement the beamforming transmission scheme. Under this framework, we derive the sum-rate scaling laws in the large number of users regime for the IRS-assisted multiple-input single-output (MISO) broadcast channel, with optimal dirty paper coding (DPC) scheme and the lower-complexity random beamforming (RBF) and deterministic beamforming (DBF) schemes at the BS. The random rotations scheme increases the sum-rate by exploiting multi-user diversity, but also compromises the gain to some extent due to correlation. Finally, energy efficiency maximization problems in terms of the number of BS antennas, IRS elements and transmit power are solved using the derived scaling laws. Simulation results show the proposed scheme to improve the sum-rate, with performance becoming close to that under coherent beamforming for a large number of users.

Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to report the optimized phase shifts to the surface. This issue has not been addressed by previous works, which do not explicitly consider the overhead during the resource allocation phase. This work aims at filling this gap, by developing an overhead-aware resource allocation framework for wireless networks where reconfigurable intelligent surfaces are used to improve the communication performance. An overhead model is proposed and incorporated in the expressions of the system rate and energy efficiency, which are then optimized with respect to the phase shifts of the reconfigurable intelligent surface, the transmit and receive filters, the power and bandwidth used for the communication and feedback phases. The bi-objective maximization of the rate and energy efficiency is investigated, too. The proposed framework characterizes the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.

This work deals with the use of artificial neural networks for energy efficiency optimization. Unlike previous works, it addresses the question of how frequently should the neural network be re-trained in order to optimize the long-term energy efficiency of a wireless network. This question is motivated by the fundamental trade-off between frequently updating the configuration of the neural network in response to changes in the propagation channel statistics, and the energy consumption of the training process. In order to shed light on this tradeoff, this work develops energy consumption models that quantity the energy consumption due to the training and use of a neural network. Moreover, the long-term energy efficiency performance of power control based on neural networks is compared to state-of-the-art methods based only on the use of optimization theory.

Reconfigurable Intelligent Surfaces (RISs) are recently attracting a wide interest due to their capability of tuning wireless propagation environments in order to increase the system performance of wireless networks. In this paper, a multi-user single-cell wireless network assisted by a RIS is studied. First of all, for the special case of a single-user system, three possible approaches are shown in order to optimize the Signal-to-Noise Ratio with respect to the beamformer used at the base station and to the RIS phase shifts. Then, for a multi-user system, assuming channel-matched beamforming, the geometric mean of the downlink Signal-to-Interference plus Noise Ratios across users is maximized with respect to the base stations transmit powers and RIS phase shifts configurations. Numerical results show that the proposed procedures are effective and greatly improve the performance of the system.

Future wireless networks will be as pervasive as the air we breathe, not only connecting us but embracing us through a web of systems that support personal and societal well-being. That is, the ubiquity, speed and low latency of such networks will allow currently disparate devices and services to become a distributed intelligent communications, sensing, and computing platform.

Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength. Compared with other transmission technologies, e.g., phased arrays, multi-antenna transmitters, and relays, RISs require the largest number of scattering elements, but each of them needs to be backed by the fewest and least costly components. Also, no power amplifiers are usually needed. For these reasons, RISs constitute a promising software-defined architecture that can be realized at reduced cost, size, weight, and power (C-SWaP design), and are regarded as an enabling technology for realizing the emerging concept of smart radio environments (SREs). In this paper, we (i) introduce the emerging research field of RIS-empowered SREs; (ii) overview the most suitable applications of RISs in wireless networks; (iii) present an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs; (iv) provide a comprehensive overview of the current state of research; and (v) discuss the most important research issues to tackle. Owing to the interdisciplinary essence of RIS-empowered SREs, finally, we put forth the need of reconciling and reuniting C. E. Shannon's mathematical theory of communication with G. Green's and J. C. Maxwell's mathematical theories of electromagnetism for appropriately modeling, analyzing, optimizing, and deploying future wireless networks empowered by RISs.

Recently, the so-called cell-free (CF) massive multiple-input multiple-output (MIMO) architecture has been introduced, wherein a very large number of distributed access points (APs) simultaneously and jointly serve a much smaller number of mobile stations (MSs). The paper extends the CF approach to the case in which both the APs and the MSs are equipped with multiple antennas, proposing a beamfoming scheme that, relying on the zero-forcing strategy, does not require channel estimation at the MSs. We contrast the originally proposed formulation of CF massive MIMO with a user-centric (UC) approach wherein each MS is served only by a limited number of APs. Exploiting the framework of successive lower-bound maximization, the paper also proposes and analyzes power allocation strategies aimed at either sum-rate maximization or minimum-rate maximization, both for the uplink and downlink. Results show that the UC approach, which requires smaller backhaul overhead and is more scalable that the CF deployment, also achieves generally better performance than the CF approach for the vast majority of the users, especially on the uplink.

In this paper, we propose a deep learning based approach to design online power control policies for large EH networks, which are often intractable stochastic control problems. In the proposed approach, for a given EH network, the optimal on-line power control rule is learned by training a deep neural network (DNN), using the solution of offline policy design problem. Under the proposed scheme, in a given time slot, the transmit power is obtained by feeding the current system state to the trained DNN. Our results illustrate that the DNN based online power control scheme outperforms a Markov decision process based policy. In general, the proposed deep learning based approach can be used to find solutions to large intractable stochastic control problems.

One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transceivers, there exist inevitably nonlinear hardware components, like signal amplifiers and various analog filters, which complicates the calibration task. To deal with this challenge, we design a deep neural network for channel calibration between the UL and DownLink (DL) directions. During the initial training phase, the deep neural network is trained from both UL and DL channel measurements. We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlinear relationships between the UL and DL channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited.

The goal in this work is to design online power control policies for large energy harvesting (EH) networks where, due to large energy overhead involved in the exchange of state information among the nodes, it is infeasible to use a centralized policy. Furthermore, typical applications of EH networks concern the scenario where the statistical information, about both the EH process and the wireless channel, is not available. In order to address these challenges, we propose a mean-field multiagent deep reinforcement learning framework. The proposed approach enables the nodes to learn online power control policies in a fully distributed fashion, i.e., it does not require the nodes to exchange the information about their states. Using the underlying structure of the problem, we analytically establish the convergence of the proposed scheme. In particular, we show that the policies obtained using the proposed approach converge to the `stationary' Nash equilibrium. Our simulation results illustrate the efficacy of the power control policies, learned through the proposed approach. In particular, the mean-field multi-agent reinforcement learning scheme achieves a performance close to the state-of-the-art centralized policies which operate using the information about the state of whole network.

This paper deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that the data-driven approaches should not replace, but rather complement, traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks, such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case studies wherein the use of deep learning proves extremely useful for network design. For each case study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement the data-driven approaches. Finally, concluding remarks describe those that, in our opinion, are the major directions for future research in this field.

This work develops a beamforming framework for energy efficiency optimization in MIMO multi-user systems with confidentiality constraints. Two channel models are considered, namely, a broadcast channel with confidential messages (corresponding to single-cell downlink) and an interference channel with confidential messages (corresponding to multi-cell downlink), in which multiple messages are transmitted, and it must be ensured that only the intended receiver is able to perform data decoding, thus treating non-intended receivers as potential eavesdroppers. In this multi-user scenario, the new metric global secrecy energy efficiency is introduced and optimized. Moreover, the coupling among the secrecy energy efficiencies of different users is analyzed by providing an efficient way of computing the system secrecy energy efficiency Pareto boundary. Both contributions are achieved by developing an optimization framework which suitably combines fractional programming theory and sequential optimization theory. The proposed framework is provably convergent, enjoys affordable complexity, and fulfills first-order optimality properties. Finally, the closed form conditions are provided to support smart user selection algorithms for downlink transmission.

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.

This paper investigates the use of a Passive Intelligent Mirrors (PIM) to operate a multi-user MISO downlink communication. The transmit powers and the mirror reflection coefficients are designed for sum-rate maximization subject to individual QoS guarantees to the mobile users. The resulting problem is non-convex, and is tackled by combining alternating maximization with the majorization-minimization method. Numerical results show the merits of the proposed approach, and in particular that the use of PIM increases the system throughput by at least 40%, without requiring any additional energy consumption.

This work develops a learning-based framework for energy-efficient power control in multi-carrier wireless networks. The problem is formulated as the maximization of the network global energy efficiency, defined as the ratio between the network sum-rate and the total consumed power, and is tackled by a novel approach which merges tools from learning, non-cooperative game theory, and fractional programming theory. The proposed algorithm is provably convergent, enjoys near-optimal performance, while requiring a much lower complexity than previous alternatives.

The problem of radio resource allocation for global energy efficiency (GEE) maximization in mmWaves large-scale multiple-input multiple-output (MIMO) systems using hybrid-beamforming with spatial modulation is addressed. The theoretical properties of the optimization problem at hand are analyzed and two provably convergent optimization algorithms with affordable complexity are proposed. The former achieves the global optimum, while the latter trades off optimality with a lower computational complexity. Nevertheless, numerical results show that both algorithms attain global optimality in practical scenarios.

This letter aims at introducing the framework of polynomial optimization theory to solve fractional polynomial problems (FPPs). Unlike other widely used optimization frameworks, the proposed one applies to a larger class of FPPs, not necessarily defined by concave and/or convex functions. An iterative algorithm that is provably convergent and enjoys asymptotic optimality properties is proposed. Numerical results are used to validate its accuracy in the nonasymptotic regime when applied to the energy efficiency maximization in multiuser multiple-input multiple-output communication systems.

This paper aims at developing a power control framework to jointly optimize energy efficiency (measured in bit/joule) and delay in wireless networks. A multi-objective approach is taken dealing with both performance metrics, while ensuring a minimum quality-of-service to each user in the network. Each user in the network is modeled as a rational agent that engages in a generalized non-cooperative game. Feasibility conditions are derived for the existence of each player's best response, and used to show that if these conditions are met, the game best response dynamics will converge to a unique Nash equilibrium. Based on these results, a convergent power control algorithm is derived, which can be implemented in a fully decentralized fashion. Next, a centralized power control algorithm is proposed, which also serves as a benchmark for the proposed decentralized solution. Due to the non-convexity of the centralized problem, the tool of maximum block improvement is used, to tradeoff complexity with optimality.

Energy-efficient resource allocation in multiple-antenna wiretap channels is investigated, subject to maximum power and minimum secrecy capacity/rate constraints. Two energy-efficient metrics are optimized, namely the secrecy energy efficiency, defined as the ratio between the system secrecy capacity and the consumed power, and the secret-key energy efficiency, defined as the ratio between the system secret-key capacity and the consumed power. If the legitimate receiver and the eavesdropper have a single antenna, and the transmitter has multiple antennas, the global solution can be expressed by a simple formula that requires negligible complexity to be computed. Instead, if all nodes have multiple-antennas, provably convergent and computationally-friendly iterative algorithms are provided, which are able to determine the global maximum of the secret-key energy efficiency and candidate solutions of the secrecy energy efficiency maximization problem. Numerical results assess the performance of the proposed methods.

This paper proposes computationally efficient algorithms to maximize the energy efficiency in multicarrier wireless interference networks, by a suitable allocation of the system radio resources, namely, the transmit powers and subcarrier assignment. The problem is formulated as the maximization of the system global energy efficiency subject to both maximum power and minimum rate constraints. This leads to a challenging nonconvex fractional problem, which is tackled through an interplay of fractional programming, learning, and game theory. The proposed algorithmic framework is provably convergent and has a complexity linear in both the number of users and subcarriers, whereas other available solutions can only guarantee a polynomial complexity in the number of users and subcarriers. Numerical results show that the proposed method performs similarly as other, more complex, algorithms.

In this paper, we analyze and optimize the energy efficiency of downlink cellular networks. With the aid of tools from stochastic geometry, we introduce a new closed-form analytical expression of the potential spectral efficiency (bit/sec/m2). In the interference-limited regime for data transmission, unlike currently available mathematical frameworks, the proposed analytical formulation depends on the transmit power and deployment density of the base stations. This is obtained by generalizing the definition of coverage probability and by accounting for the sensitivity of the receiver not only during the decoding of information data, but during the cell association phase as well. Based on the new formulation of the potential spectral efficiency, the energy efficiency (bit/Joule) is given in a tractable closed-form formula. An optimization problem is formulated and is comprehensively studied. It is mathematically proved, in particular, that the energy efficiency is a unimodal and strictly pseudo-concave function in the transmit power, given the density of the base stations, and in the density of the base stations, given the transmit power. Under these assumptions, therefore, a unique transmit power and density of the base stations exist, which maximize the energy efficiency. Numerical results are illustrated in order to confirm the obtained findings and to prove the usefulness of the proposed framework for optimizing the network planning and deployment of cellular networks from the energy efficiency standpoint.

We consider the global energy efficiency (GEE) maximization problem for the general non-regenerative MIMO relay network, under the maximum power constraints for each user and each relay. The problem is reformulated through the fractional optimization technique, and the mean square error receiver filter is considered. By applying the alternating minimization method, we simplify the problem into several convex quadratic constrained quadratic programming subproblems, and solve the subproblems by the feasible shrinkage method combined with the sequential quadratic programming method. Because the result highly depends on the initialization, we design a deterministic initialization by introducing an auxiliary power minimization problem. Simulation results show that our proposed algorithm can achieve more than 10 times higher GEE than the previous works which are not tailored for GEE maximization.

Recently, the so-called cell-free Massive MIMO architecture has been introduced, wherein a very large number of distributed access points (APs) simultaneously and jointly serve a much smaller number of mobile stations (MSs). A variant of the cell-free technique is the user-centric approach, wherein each AP just decodes the MSs that it receives with the largest power. This paper considers both the cell-free and user-centric approaches, and, using an interplay of sequential optimization and alternating optimization, derives downlink power-control algorithms aimed at maximizing either the minimum users' SINR (to ensure fairness), or the system sum-rate. Numerical results show the effectiveness of the proposed algorithms, as well as that the user-centric approach generally outperforms the CF one.

This work investigates the feasibility of opportunistic target detection in mmWaves vehicular networks employing the IEEE 802.11ad standard. The beacon signals transmitted during the network discovery phase are exploited to detect the presence of possible obstacles in the surrounding environment and to estimate their position and velocity. The detection problem is formulated as a composite hypothesis test and two detectors are derived and investigated.

In this paper, we investigate the global energy efficiency of a bidirectional amplify-and-forward relay MIMO system. It is assumed that the relay serves two end-users, each requiring a minimum target rate. Two algorithms are proposed, a suboptimal one with lower complexity, and an optimal one with slightly higher complexity. We present numerical results that compare the two algorithms and exhibit several optimality properties concerning the global energy efficiency function.

Along with spectral efficiency (SE), energy efficiency (EE) is a key performance metric for the design of 5G and beyond 5G (B5G) wireless networks. At the same time, infrastructure sharing among multiple operators has also emerged as a new trend in wireless communication networks. This paper presents an optimization framework for EE and SE maximization in a network, where radio resources are shared among multiple operators. We define a heterogeneous service level agreement (SLA) framework for a shared network, in which the constraints of different operators are handled by two different multi-objective optimization approaches namely the utility profile and scalarization methods. Pareto-optimal solutions are obtained by merging these approaches with the theory of generalized fractional programming. The approach applies to both noise-limited and interference-limited systems, with single-carrier or multi-carrier transmission. Extensive numerical results illustrate the effect of the operator specific SLA requirements on the global spectral and EE. Three network scenarios are considered in the numerical results, each one corresponding to a different SLA, with different operator-specific EE and SE constraints.

State-of-the-art algorithms for energy-efficient power allocation in wireless networks are based on fractional programming theory, and allow to find the global maximum of the energy efficiency only in noise-limited scenarios. In interference-limited scenarios, several sub-optimal solutions have been proposed, but an efficient framework to globally maximize energy-efficient metrics is lacking. The goal of this work is to fill this gap by making use of fractional programming theory jointly with monotonic optimization. The resulting optimization framework is useful for at least two main reasons. First, it sheds light on the ultimate energy-efficiency performance of wireless networks. Second, it provides the means to benchmark the energy efficiency of state-of-the-art, but sub-optimal, solutions.

This paper studies the problem of resource allocation in overlay systems. A multiple-input single-output (MISO) primary link shares the spectrum with a multiple-input multiple-output (MIMO) secondary link, which in return acts as an amplify-and-forward (AF) relay, forwarding the primary message. The considered problem is the maximization of the secondary energy efficiency (EE) subject to a primary rate requirement. The resulting optimization problem is a fractional program which can not be tackled by traditional fractional optimization methods. Two algorithms are proposed, based on an interplay between fractional programming and sequential optimization theory, which trade-off performance and complexity. Numerical results demonstrate the merits of the proposed algorithms both in terms of energy-efficient performance and complexity.

After about a decade of intense research, spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.

This paper deals with the problem of distributed resource allocation in multiple-input multiple-output multi-carrier multiple-access channel networks. The assignment between users and subcarriers is allocated together with the users' transmit powers for energy efficiency maximization, by means of a novel approach which merges the popular Dinkelbach's algorithm with the frameworks of distributed auction theory and stable matching. Two distributed algorithms are presented, which can be implemented in a fully decentralized way. The former is guaranteed to converge to the global optimum of the system energy efficiency, up to a threshold which can be set in advance, while the latter enjoys weaker optimality properties, but has an even lower computational complexity. Additionally, we develop a novel energy consumption model which explicitly accounts for the energy consumption due to feedback transmissions. Employing this new model, it is shown that the proposed distributed algorithms can even outperform centralized resource allocations which require a larger feedback energy consumption.

This paper considers the problem of energy efficiency maximization in the uplink of a cluster of multiple-antenna coordinated access points. A framework for energy efficiency optimization is developed in which the signal-to-interference-plus-noise ratio takes a more general expression than existing alternatives so as to encompass most 5G candidate technologies. Two energy efficiency optimization problems are formulated, also considering quality-of-service (QoS) constraints: 1) network global energy efficiency maximization; 2) worst-case energy-efficient design. These fractional, non-convex problems are tackled by means of fractional programming coupled with sequential convex optimization, and two low-complexity resource allocation algorithms are designed, which are guaranteed to converge to local optima of the non-convex problems. Numerical results show that the proposed algorithm can efficiently balance between the goals of maximizing the energy efficiency and meeting the QoS constraints. Moreover, it is shown that a small sum-rate reduction allows large energy savings.

Throughput and energy efficiency in 3-way relay channels are studied in this paper. Unlike previous contributions, we consider a circular message exchange. First, an outer bound and achievable sum rate expressions for different relaying protocols are derived for 3-way relay channels. The sum capacity is characterized for certain SNR regimes. Next, leveraging the derived achievable sum rate expressions, cooperative and competitive maximization of the energy efficiency are considered. For the cooperative case, both low-complexity and globally optimal algorithms for joint power allocation at the users and at the relay are designed so as to maximize the system global energy efficiency. For the competitive case, a game theoretic approach is taken, and it is shown that the best response dynamics is guaranteed to converge to a Nash equilibrium. A power consumption model for mmWave board-to-board communications is developed, and numerical results are provided to corroborate and provide insight on the theoretical findings.

This paper addresses the problem of energy-efficient resource allocation in the downlink of a cellular orthogonal frequency division multiple access system. Three definitions of energy efficiency are considered for system design, accounting for both the radiated and the circuit power. User scheduling and power allocation are optimized across a cluster of coordinated base stations with a constraint on the maximum transmit power (either per subcarrier or per base station). The asymptotic noise-limited regime is discussed as a special case. Results show that the maximization of the energy efficiency is approximately equivalent to the maximization of the spectral efficiency for small values of the maximum transmit power, while there is a wide range of values of the maximum transmit power for which a moderate reduction of the data rate provides large savings in terms of dissipated energy. In addition, the performance gap among the considered resource allocation strategies is reduced as the out-of-cluster interference increases.

Green resource allocation in an amplify-and-forward (AF) relay-assisted MIMO system is considered, consisting of one source, one AF relay, and one destination, in which the relay-to-destination channel is only statistically known to the source and relay. The source covariance matrix and the relay AF matrix are optimized so as to maximize the system energy efficiency (EE), defined as the ratio of the system ergodic achievable rate over the total consumed power. The resulting optimization problem is a challenging non-convex problem, which is tackled employing fractional programming in conjunction with the alternating maximization algorithm. In addition, the regime of single-stream transmission is investigated and a sufficient condition for its optimality is derived.

In this paper, the issue of energy-aware competitive resource allocation in an amplify-and-forward (AF) relay-assisted multiple-antenna interference network is considered. A non-cooperative game-theoretic approach is taken and the EE of each communication link is defined as the ratio of the achievable rate over the consumed power. The proposed resource allocation algorithm jointly allocates the relay AF, the users' transmit powers and receive filters. The resulting resource allocation algorithm is shown to converge to a unique fixed point and to have limited feedback and computational requirements. Numerical results are provided to illustrate the performance gain with respect to resource allocation policies that do not perform a joint allocation of the network resources.

Energy-efficient resource allocation in a single-user, amplify-and-forward (AF), relay-assisted, multiple-input-multiple-output (MIMO) system is considered in this paper. Previous results in this area assume that perfect CSI is available for at least one of the source-relay and relay-destination channels. Instead, the case in which statistical CSI is available for both the source-relay and relay-destination channel is tackled in this letter. Using fractional programming theory and the alternating maximization algorithm, low-complexity source and relay precoding is performed, subject to quality-of-service (QoS) and power constraints.

This paper considers the problem of energy-efficient communication in the downlink of a cellular OFDMA network. User scheduling and power allocation are jointly optimized across a cluster of coordinated base stations so as to maximize the weighted sum of the energy efficiencies on the available resource slots under a per-subcarrier power constraint. The asymptotic noise-limited regime is also discussed as a special case. Numerical results show that there is a wide range of operating regimes where a moderate reduction of the data rate can provide a large saving in terms of dissipated energy.

In this paper, the issue of competitive, energy- efficient power control in a relay-assisted interference channel is considered. The energy efficiency is measured in bit/Joule and is defined as the ratio of a SINR-based function, divided by the sum of the transmit power plus the circuit power consumed to operate the device. Taking also into account the direct path between transmitters and receivers, a non-cooperative power control game is devised. The proposed game is shown to always admit a Nash equilibrium and, based on its best-response dynamics, a power control algorithm that can be implemented in a fully distributed way is provided. Finally, numerical results are provided to show the merits of the proposed algorithms.

Competitive power control for energy efficiency maximization in wireless interference networks is addressed, for the scenarios in which the users' SINR can be expressed as either (a) γ = (αp)/(φp + ω), or (b) γ = (αp + βp2)/(φp + ω), with p the user's transmit power. The considered SINR expressions naturally arise in relay-assisted systems. The energy efficiency is measured in bit/Joule and is defined as the ratio of a proper function of the SINR, divided by the consumed power. Unlike most previous related works, in the definition of the consumed power, not only the transmit power, but also the circuit power needed to operate the devices is accounted for. A non-cooperative game theoretic approach is employed and distributed power control algorithms are proposed. For both SINR expressions (a) and (b), it is shown that the competitive power allocation problem always admits a Nash equilibrium. Moreover, for the SINR (a), the equilibrium is also shown to be unique and the best-response dynamic is guaranteed to converge to such unique equilibrium. For the two-user case, the efficient computation of the Pareto frontier of the considered game is addressed, and, for benchmarking purposes, a social optimum solution with fairness constraint is derived.

In this paper, the issue of non-cooperative, energy-efficient power control in a relay-assisted interference channel is considered. The energy efficiency of a given terminal is defined as the ratio between the throughput of that terminal and the consumed power. As far as the computation of the consumed power is concerned, not only the transmit power, but also the power dissipated in terminal's electronic circuitry to operate the device is considered. A non-cooperative power control game is devised, which admits a unique Nash equilibrium, and whose best-response-dynamics is guaranteed to converge to the unique equilibrium. A cooperative power control algorithm is also devised, which is used as a benchmark for the non-cooperative game. Finally, numerical results are provided to show the merits of the proposed algorithms.

Non-cooperative resource allocation in amplify-and-forward relay-assisted DS/CDMA networks is tackled in this paper. The relay allocates its amplify-and-forward matrix for achievable sum-rate maximization, whereas the mobile transmitters selfishly allocate their spreading codes for individual achievable rate maximization. Convergence of the proposed algorithm is proved and its performance contrasted against a centralized approach. Moreover, the proposed approach is extended to the case in which the direct path between transmitters and receiver is taken into account.

The problem of non-cooperative resource allocation in an amplify-and-forward relay-assisted DS/CDMA system is addressed. The relay designs its amplify-and-forward matrix for achievable sum-rate maximization, whereas the multiple access users pursue individual achievable rate maximization. The interaction between the relay and the multiple access users has been modeled as a Stackelberg game, with the relay as the leader and the multiple access users as followers. Numerical results are provided to show the merits of the proposed algorithm.

In this work the problem of non-cooperative resource optimization in the uplink of a relay-assisted MIMO MAC system with partial CSI at the transmitter is addressed. Each multiple access user pursues individual rate maximization, whereas the relay designs its amplify-and-forward matrix in order to optimize the system's sum-rate. From a game-theoretic perspective, the resource allocation process is modeled as a two-level Stackelberg game, with the relay as the leader, and the multiple access users as followers. For any choice of the relay matrix the individual rate maximizing transmit covariance matrices are derived, and then optimum relay matrix design is carried out. Finally, numerical results are provided to give insights on the proposed algorithms.

The issue of noncooperative transceiver optimization in the uplink of a multiuser wireless code division multiple access data network with widely linear detection at the receiver is considered. While previous work in this area has focused on a simple real signal model, in this paper, a baseband complex representation of the data is used so as to properly take into account the I and Q components of the received signal. For the case in which the received signal is improper, a widely linear reception structure, processing separately the data and their complex conjugates, is considered. Several noncooperative resource allocation games are considered for this new scenario, and the performance gains granted by the use of widely linear detection are assessed through theoretical analysis. Numerical results confirm the validity of the theoretical findings and show that exploiting the improper nature of the data in noncooperative resource allocation brings remarkable performance improvements in multiuser wireless systems.

The problems of detecting the presence of a new user and of estimating the delays of its multipath replicas in a direct-sequence/code-division-multiple-access (DS/CDMA) system are investigated. Despite previous works, we consider a doubly-dispersive fading channel model and we propose a new code-aided detection algorithm which relies on the application of a powerful statistical tool known as the method of sieves. The proposed detector is blind and bounded constant false alarm rate. As a byproduct of the detection stage, a new blind procedure to estimate the multipath channel delays of the detected user is also derived.