Power Budget Constraints Research Articles

A Joint Communication and Computation Design for Probabilistic Semantic Communications.

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of the equivalent rate of all users, considering the total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed, where the solutions for the received beamforming matrix of the BS, the transmit power of each user, and the semantic compression ratio of each user are obtained stage by stage. The numerical results validate the effectiveness of our proposed scheme.

Decoupled Association With Rate Splitting Multiple Access in UAV-Assisted Cellular Networks Using Multi-Agent Deep Reinforcement Learning

In unmanned aerial vehicles (UAVs) assisted cellular networks, user association plays an important role in interference control and spectrum efficiency. In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in a multi-UAV assisted network in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicasting can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we propose a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation (SIC) at the receiver. To be specific, the transmitted signal is split into a common part and a private part, and the interference is partially decoded and partially treated as noise. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL under the constraints of UAV backhaul capacity and power budget. Since the formulated problem is non-convex with intricate states and an individual UAV may not know the rewards of other UAVs, we convert it into a robust partially observable Markov decision process (POMDP). Then we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. To achieve an optimal policy, we further propose an improved clip and count-based proximal policy optimization (PPO) algorithm to train actor and critic networks. Simulation results demonstrate the superiority of the proposed decoupled association strategy with RSMA and the MADRL learning algorithm.

Energy-efficient routing, power control and energy clustering for energy harvesting-enabled spectrum sharing IoT sensor networks

We investigate a spectrum sharing system that enables energy harvesting from distributed multi-antenna energy transmitter devices for primary dual-band device-to-device networks and secondary dual-band wireless sensor networks (WSNs), incorporating a multi-stage rectenna energy harvesting circuit model. We formulate a new multi-hop routing problem for network energy efficiency maximization of this system, which involves the interconnected tasks of WSN routing, controlling the transmit power of sensor nodes, and energy transmitter clustering, taking into account interference, power budget and rate constraints. To tackle these interconnected tasks effectively, we propose an integrated solution that includes both centralized and distributed routing schemes. Furthermore, the solution incorporates energy transmitter clustering schemes based on harvested power and channel gain. We provide an analysis of the computation complexity and signaling overhead for the proposed solution. Our simulation results demonstrate that the proposed routing and clustering schemes outperform their respective benchmark schemes, achieving significant performance gains.

Sub-Connected Hybrid Precoding and Trajectory Optimization Using Deep Reinforcement Learning for Energy-Efficient Millimeter-Wave UAV Communications

In this letter, a sub-connected hybrid precoding system was designed to realize energy-efficient millimeter-Wave (mmWave) unmanned aerial vehicle (UAV) communications. Considering the limited capacity of the UAV battery, the system was jointly optimized with a UAV trajectory to increase the energy efficiency of the UAV under quality-of-service (QoS) and power budget constraints. The dynamic motion of the UAV changes the channel condition between the UAV and terrestrial users over time. Hence, a joint optimization problem was formulated as a non-convex and time-sequential domain, solved using deep reinforcement learning (DRL). The performances of the proposed scheme and a fully-connected hybrid precoding scheme were compared in terms of energy efficiency and show higher results.

Joint precoding and power allocation in a FSO channel with signal-dependent noise.

To enhance the throughput of visible light communication (VLC), this paper investigates precoding and power allocation jointly in a free space optical channel with signal-dependent noise (SDN-FSO). With the presence of SDN from a light source, a more realistic multi-user multiple-input single-output (MU-MISO) VLC channel model is introduced. First, based on the entropy power inequality, a capacity bound for the SDN-FSO channel is derived under a MU-MISO scenario. Second, due to the nonnegativity of SDN-FSO input, a zero forcing constraint is introduced to alleviate multi-user interference. Third, from the perspective of resource allocation, min-max and total power budget constraints are taken into consideration. A non-convex joint optimization problem is formulated and divided into two subproblems, i.e.,the precoding subproblem and power allocation subproblem. Moreover, an iterative algorithm based on a concave and convex program and a simplified high signal-to-noise ratio approximation method are iteratively implemented in the precoding subproblem. For the power allocation subproblem, successive convex approximation is employed to ensure its optimality. Simulation results indicate that the proposed joint optimization methods can converge after about two iterations and accomplish a throughput improvement of up to 1∼3n a t s/s/H z as compared with the benchmarks for four different spacing scenarios. When channel state information is interfered with noise, the proposed methods still demonstrate their superior performance with varying degrees of noise.

Power Control With QoS Guarantees: A Differentiable Projection-Based Unsupervised Learning Framework

Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users’ quality-of-service (QoS) constraints, guaranteeing constraint satisfaction becomes a fundamental challenge. In this paper, we propose a novel unsupervised learning framework to solve the classical power control problem in a multi-user interference channel, where the objective is to maximize the network sum-rate under users’ minimum data rate or QoS requirements and power budget constraints. Utilizing a differentiable projection function, two novel deep learning (DL) solutions are pursued. The first is called Deep Implicit Projection Network (DIPNet), and the second is called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a differentiable convex optimization layer to implicitly define a projection function. On the other hand, DEPNet uses an explicitly-defined projection function, which has an iterative nature and relies on a differentiable correction process. DIPNet requires convex constraints; whereas, the DEPNet does not require convexity and has a reduced computational complexity. To enhance the sum-rate performance of the proposed models even further, Frank-Wolfe algorithm (FW) has been applied to the output of the proposed models. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate but also achieve zero constraint violation probability, compared to the existing DNNs. The proposed solutions outperform the classic optimization methods in terms of computation time complexity.

Polar Coded Integrated Data and Energy Networking: A Deep Neural Network Assisted End-to-End Design

Wireless sensors are everywhere. To address their energy supply, we proposed an end-to-end design for polar-coded integrated data and energy networking (IDEN), where the conventional signal processing modules, such as modulation/demodulation and channel decoding, are replaced by deep neural networks (DNNs). Moreover, the input-output relationship of an energy harvester (EH) is also modelled by a DNN. By jointly optimising both the transmitter and the receiver as an autoencoder (AE), we minimize the bit-error-rate (BER) and maximize the harvested energy of the IDEN system, while satisfying the transmit power budget constraint determined by the normalization layer in the transmitter. Our simulation results demonstrate that the DNN aided end-to-end design conceived outperforms its conventional model-based counterpart both in terms of the harvested energy and the BER.

Energy-Efficient Beamforming and Resource Optimization for AmBSC-Assisted Cooperative NOMA IoT Networks

In this manuscript, we present an energy-efficient alternating optimization framework based on the multi-antenna ambient backscatter communication (AmBSC) assisted cooperative non-orthogonal multiple access (NOMA) for next-generation (NG) internet-of-things (IoT) enabled communication networks. Specifically, the energy-efficiency maximization is achieved for the considered AmBSC-enabled multi-cluster cooperative IoT NOMA system by optimizing the active-beamforming vector and power-allocation coefficients (PAC) of IoT NOMA users at the transmitter, as well as passive-beamforming vector at the multi-antenna assisted backscatter node. Usually, increasing the number of IoT NOMA users in each cluster results in inter-cluster interference (ICI) (among different clusters) and intra-cluster interference (among IoT NOMA users). To combat the impact of ICI, we exploit a zero-forcing (ZF) based active-beamforming, as well as an efficient clustering technique at the source node. Further, the effect of intra-cluster interference is mitigated by exploiting an efficient power-allocation policy that determines the PAC of IoT NOMA users under the quality-of-service (QoS), cooperation, SIC decoding, and power-budget constraints. Moreover, the considered non-convex passive-beamforming problem is transformed into a standard semi-definite programming (SDP) problem by exploiting the successive-convex approximation (SCA) approximation, as well as the difference of convex (DC) programming, where Rank-1 solution of passive-beamforming is obtained based on the penalty-based method. Furthermore, the numerical analysis of simulation results demonstrates that the proposed energy-efficiency maximization algorithm exhibits an efficient performance by achieving convergence within only a few iterations.

Fronthaul latency and capacity constrained cost-effective and energy-efficient 5G C-RAN deployment

The third-generation partnership project (3GPP) has identified different new functional split points, defined as options 1–7, between the baseband unit (BBU) and the remote unit (RU) for the 5G centralized radio access networks (C-RANs). The functional splits facilitate lower fronthaul line rate and latency requirements while imposing technical complexity and cost-efficiency trade-offs. Among all the functional split options, the standardization groups such as 3GPP, Open RAN (O-RAN), and small cell forum have selected option-7 as the most suitable choice for the 5G C-RAN deployment that can meet the operator’s requirements. One of the biggest challenges for designing option-7 functional split-based C-RAN is the capacity and latency requirement fulfillment as specified by 3GPP, while minimizing the deployment cost. In this regard, we propose an optimization model for cost-effective and energy-efficient option-7 functional split-based C-RAN architecture using wavelength division multiplexed passive optical network (WDM-PON) in the fronthaul. For the given locations of the next-generation core (NGC) and the set of RUs, the proposed optimization model explores the most suitable locations for the placement of BBU pools and arrayed waveguide gratings (AWGs). It provides complete interconnection details among NGC, BBU pools, AWGs, and RUs. It also provides the optimal number of digital units in each selected BBU pool, the splitting ratio of each AWG, and maximum fronthaul reach, satisfying the fronthaul latency, capacity, and power budget constraints. To the best of our knowledge, for the first time, we estimate the worst-case queuing delay for the upstream traffic flow using the G/G/1 queuing model and investigate its impact on fronthaul latency and deployment cost for different RU configurations. We compare the performance of the proposed option-7 functional split-based C-RAN with the traditionally used option-8 functional split-based C-RAN in terms of the deployment cost and fronthaul latency improvement.

Joint Trajectories and Resource Allocation Design for Multi-UAV-Assisted Wireless Power Transfer with Nonlinear Energy Harvesting

In this work, we explore a multi-UAV-assisted wireless power transfer (WPT) network, where multiple UAVs are deployed to provide WPT services to multiple ground devices (GDs) in order to extend their lifespan. To enhance the WPT efficiency while considering fairness, we investigate the joint trajectories and transmit power design. For fairness-aware consideration, our objective is to maximize the harvested energy of the GD with the worst condition, taking into account UAV mobility, anti-collision, and power budget constraints. Unlike previous works that focus on the simplified linear energy harvesting (EH) model, a more accurate multi-source nonlinear EH model is, for the first time, adopted to formulate the problem. Given the highly non-convex nature of the original problem due to the presence of coupled variables, we leverage the convexity of the multi-source nonlinear EH model and introduce a convex approximation method, which enables us to construct a tightly convex problem in each iteration for the original joint design problem, thereby obtaining a high-quality solution. Finally, we present numerical results to showcase the convergence of our algorithm and validate the performance advantages of the proposed multi-UAV WPT scheme with a nonlinear EH model versus benchmarks.

Active Reconfigurable Intelligent Surface-Assisted MISO Integrated Sensing and Communication Systems for Secure Operation

Spectrum congestion has motivated the recent introduction of integrated sensing and communication (ISAC) systems where radar and communications functionalities co-exist and share the same spectrum. This is accompanied by extensive research in the area of reconfigurable intelligent surfaces (RIS), thanks to their ability to improve the ISAC systems. In this paper, the physical layer security of a multiuser multiple-input single-output (MU-MISO) ISAC system is investigated when the system is subject to eavesdropping by a malicious unmanned aerial vehicle (UAV). In particular, we propose using an active RIS to maximize the achievable secrecy rate of the system through jointly designing the radar receive beamformers, the active RIS reflection coefficients matrix and the transmit beamformers at the dual-function base station of the ISAC system. This is done while taking into account a minimum radar detection signal-to-noise ratio (SNR) and total system power budget constraints. The resulting non-convex optimization problem is tackled by exploiting fractional programming (FP) and majorization-minimization (MM) techniques to achieve a solution. Our numerical results show the superiority of active RIS to achieve the goal when compared with passive RIS or with an ISAC without an RIS. Results also show that passive RIS could still be a viable solution to achieve this goal but at the expense of a much larger needed size especially when lower power budgets are available.

Subchannel and power optimization for sum rate maximization in downlink multicarrier NOMA networks

In a multicarrier NOMA system, the subchannel allocation (SA) and power allocation (PA) are intricately linked and essential for improving system throughput. Also, for the successful execution of successive interference cancellations (SIC) at the receiver, a minimum power gap is required among users. As a result, this research comes up with optimization of the SA and PA to maximize the sum rate of the NOMA system while sticking to the minimum power gap constraint in addition to minimum user rate, maximum number of users in a subchannel and power budget constraints for downlink transmission in multicarrier NOMA networks. To ensure that the formulated problem can be solved in polynomial time, we propose solving it in two stages; SA followed by PA. To obtain SA, we investigate four algorithms: Greedy, WSA, WCA, and WCF. For PA, we propose a low-complexity algorithm. We compare the performance of the proposed method with benchmark method that does not consider the minimum power gap constraint. We conclude that employing WCF algorithm with the PA algorithm gives the best sum rate performance.

Energy-Efficient Backscatter-Assisted Coded Cooperative NOMA for B5G Wireless Communications

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the transmit power of the source, power allocation coefficients (PAC), and power of the relay node under imperfect successive interference cancellation (SIC) decoding. A three-stage low-complexity energy-efficient alternating optimization algorithm is introduced which optimizes the transmit power, PAC, and relay power by considering the quality of service (QoS), power budget, and cooperation constraints. Subsequently, a joint channel coding framework is introduced to enhance the performance of far user which has no direct communication link with the base station (BS) and has bad channel conditions. In the destination node, the far user data is jointly decoded using a Sum-product algorithm (SPA) based joint iterative decoder realized by jointly-designed Quasi-cyclic Low-density parity-check (QC-LDPC) codes. Simulation results evince that the proposed backscatter-enabled cooperative NOMA system outperforms its counterpart by providing an efficient performance in terms of energy efficiency. Also, proposed jointly-designed QC-LDPC codes provide an excellent bit-error-rate (BER) performance by jointly decoding the far user data for considered BSC cooperative NOMA system with only a few decoding iterations.

Energy-Efficiency Optimization for D2D Communications Underlaying UAV-Assisted Industrial IoT Networks With SWIPT

The Industrial Internet of Things (IIoT) has been viewed as a typical application for the fifth generation (5G) mobile networks. This article investigates the energy efficiency (EE) optimization problem for the Device-to-Device (D2D) communications underlaying unmanned aerial vehicles (UAVs)-assisted IIoT networks with simultaneous wireless information and power transfer (SWIPT). We aim to maximize the EE of the system while satisfying the constraints of transmission rate and transmission power budget. However, the designed EE optimization problem is nonconvex involving joint optimization of the UAV’s location, beam pattern, power control, and time scheduling, which is difficult to tackle directly. To solve this problem, we present a joint UAV location and resource allocation algorithm to decouple the original problem into several subproblems and solve them sequentially. Specifically, we first apply the Dinkelbach method to transform the fraction problem to a subtractive-form one and propose a mulitiobjective evolutionary algorithm based on decomposition (MOEA/D)-based algorithm to optimize the beam pattern. We then optimize UAV’s location and power control using the successive convex optimization techniques. Finally, after solving the above variables, the original problem can be transformed into a single-variable problem with respect to the charging time, which is linear and can be tackled directly. Numerical results verify that significant EE gain can be obtained by our proposed algorithm as compared to the benchmark schemes.

Energy-efficient beamforming optimization for MISO communication based on reconfigurable intelligent surface

Reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiency (EE) by reconfiguring the wireless propagation environment. In this paper, we propose a base station (BS) beamforming and RIS phase shift optimization technique that maximizes the EE of a RIS-aided multiple-input–single-output system. In particular, considering the system circuits’ energy consumption, an EE maximization problem is formulated by jointly optimizing the active beamforming at the BS and the passive beamforming at the RIS, under the constraints of each user’ rate requirement, the BS’s maximal transmit power budget and unit-modulus constraint of the RIS phase shifts. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. To overcome this obstacle, we divide the problem into active and passive beamforming optimization subproblems. For the first subproblem, the active beamforming is given by the maximum ratio transmission optimal strategy. For the second subproblem, the optimal phase shift matrix at the RIS is obtained by exploiting sine cosine algorithm (SCA). Moreover, for this case where each reflection element’s working state is controlled by a circuit switch, each reflection element’s switch value is optimized with the aid of particle swarm optimization algorithm. Finally, numerical results verify the effectiveness of our proposed algorithm compared to other algorithms.

Spectral-Energy Efficiency and Power Allocation in Full-Duplex Networks: The Effects of Hardware Impairment and Nakagami-$m$ Fading Channels

Full-duplex (FD) systems have emerged as game-changers for the future of wireless communication thanks to their ability to increase spectral efficiency (SE) and energy efficiency (EE). In this article, we study the effect of hardware impairments (HWIs) on Multiple-input Multiple-output (MIMO) FD systems. We derived closed-form expressions for the lower bounds of the average UL and DL achievable rates. We formulate different power allocation optimization problems to maximize the average FD SE and EE, while satisfying the quality of service (QoS) and power budget constraints. Moreover, we consider the max-min objective functions to assure fairness between users. These problems are solved using different optimization techniques, including the Dinkelbach approach, transformation, and the Karush–Kuhn–Tucker (KKT) conditions. We also refine the SE algorithm and present a simpler solution. Finally, we assume that all fading channels follow Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> distributions, where other scenarios can be considered special cases. Extensive simulations were performed to validate the presented analysis.

Energy efficiency in cognitive cooperative amplify and forward networks

The emphasis on improving energy efficiency has gradually replaced that on network throughput or data rate alone in the past, because the former not only concerns about network throughput or data rate, but also concentrate on energy consumption according to the goal of green and low carbon nowadays. In the context of increasingly clamoring environmental protection, this paper delivers the energy efficiency problem in amplify and forward cooperative relay networks, and then extends this problem to the cognitive radio scenarios. Traditional Dinkelbach parametrized method needs to transform the original problem into a series of nonlinear iterative problems. On the surface, this method simplifies the original energy efficiency optimization problem, but it often requires multiple iterations, and the number of iterations cannot be predicted in advance. In order to get rid of this dilemma, this paper directly starts with the partial derivative of the original problem, and obtains a closed-form solution of power distribution based on the Lambert function, instead of the water-filling form in traditional method. Then this Lambert result is extended to the cognitive radio cooperative relay system under the dual constraints of power budget and interference threshold. Finally, the performance of energy efficiency is confirmed by a large number of simulation experiments for distinct parameter settings.

Energy-Efficient Power Allocation for Multiuser Distributed MISO Systems With Beamforming and Imperfect CSI

In this article, we study the energy-efficient power allocation (PA) for multiuser distributed multiple-input-single-output (D-MISO) system with beamforming (BF) and imperfect channel state information (CSI). Given a target bit error rate, the system energy efficiency (EE) with imperfect CSI is deduced. Subject to the power budget constraints, a constrained optimization problem is first formulated to maximize EE of single-user D-MISO. The optimal BF is shown to be aligned with the channel direction that renders the joint optimization problem to be a PA problem. A near-optimal PA scheme is proposed to tackle this problem by using the concave–convex procedure method and fractional programming (FP) and block coordinate descent (BCD) method, and it can provide nearly optimal performance close to that of optimal exhaustive search scheme with less complexity. On this basis, a suboptimal PA scheme based on the FP and BCD method is developed to further decrease the complexity and maintain similar performance to near-optimal scheme, and it is suitable for the system with more distributed antennas. With these results, two PA schemes are, respectively, proposed for multiuser D-MISO with imperfect CSI to maximize EE. The first scheme can obtain better performance with relatively higher complexity, and the second scheme can gain the performance close to the first scheme with lower complexity, and only smaller performance loss is found. Interestingly, these two schemes have the same optimal performance for perfect CSI, and include the existing ones under perfect CSI as special cases. Simulation results verify the effectiveness of the developed schemes.

Strong homogeneous clutter and random target scattering analysis for closed-form Cramer Rao bound and optimal power allocations in MIMO radars

This paper considers the effects of strong homogeneous clutter and the target's random scattering on radar measurements. The signal model is improved and closed-form Cramer-Rao lower bounds (CRLBs) are calculated for delay and Doppler frequency estimations in attendance of practical clutter, and target's scatterings models. The calculated closed-form CRLBs are simplified to extract the effect of assigned powers to the transmitters for using power allocation (PA) strategies. The maximum likelihood estimation error variances (MLEEVs) are derived for calculated CRLBs' verifications. Four power allocation strategies are defined as mathematical optimization problems that consider the power budget constraints and measurement error statistics as the objective function or constraints. Simulation results verify the proposed simplified closed-form CRLBs accuracies by comparing with exact CRLBs and MLE estimators' performance. It is shown that proposed power allocations not only preserve required performance but also optimize objectives.

Outage Constrained Robust Secure Beamforming in Integrated Sensing and Communication Systems

In this letter, we consider the secure multi-user downlink communication scheme against eavesdroppers in Integrated Sensing and Communication (ISAC) systems, where the ISAC base station (BS) transmits ISAC waveform to serve communication users and sense targets, and artificial noise (AN) is employed to guarantee the transmission secrecy. In particular, we study the robust ISAC beamforming design under the assumption of imperfect legitimate users’ and eavesdroppers’ channel state information (CSI). Specifically, we aim to minimize the squared error between designed and desired beampatterns under the probabilistic outage constraints concerning the secrecy performance as well as power budget constraint. To deal with the non-convex problem, we utilize the semidefinite relaxation (SDR) technique and Bernstein-type inequality to convert the problem to a tractable form. Numerical results show that the proposed robust beamforming design algorithm is able to approach the performance under perfect CSI, and illustrates the trade-off between sensing and secrecy performances.