Optimal Power Allocation Scheme Research Articles

Analyzing error probability in aerial full-duplex relay systems: Exact formulations and optimization techniques

In this study, we investigate the performance of aerial full-duplex relay (AFDR) systems. In particular, we examine two practical scenarios: one where there is no direct link between the transmitter and user, and another where such a link exists. For both scenarios, we develop mathematical models to calculate the closed-form expressions of symbol error probabilities (SEPs) for AFDR systems, using a realistic channel aligned with the fifth generation (5G) and beyond yardsticks. To take care of residual self-interference (RSI) in AFDR, we propose an optimal power allocation strategy. Our numerical findings indicate that the performance in the case with a direct link is considerably higher than that in the case without this link. Additionally, we analyze thoroughly the effects of key parameters such as transmit power, RSI levels, carrier frequencies, and AFDR positions. The effect of RSI is significantly strong, and our proposed optimal power allocation method substantially improves system performance, especially in high transmit power scenarios where error floors may occur. Importantly, the optimal power level is dramatically lower than the conventional value where the optimal power cannot be found. Thus, besides reducing the SEPs, optimal power also helps to prolong the time of operation of AFDR. Monte-Carlo simulations are performed to confirm the accuracy of our derived expressions and demonstrate the efficacy of the proposed approaches in practical scenarios.

Green grant-free power allocation for ultra-dense Internet of Things: A mean-field perspective

Grant-free access, in which each Internet-of-Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant-free access with multi-packet reception capabilities, with an emphasis on ultra-low-end IoT applications with small data sizes, sporadic activity, and energy usage constraints. We propose a power allocation scheme aimed at maximizing throughput while minimizing power consumption by considering the traffic and energy constraints of IoT devices. Our approach employs a stochastic geometry framework and mean-field game theory to model and analyze the mutual interference among active IoT devices. Additionally, we utilize a Markov chain model to capture and track the queue length of IoT devices, enabling the derivation of the transmission success probability at steady-state. The simulation results illustrate the optimal power allocation strategy and evaluate the proposed approach’s performance in terms of packet transmission success probability and average delay.

Reinforcement Learning for FDA-MIMO Radar Power Allocation in Congested Spectral Environments

This paper studies the coexistence problem of frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and communication systems. The communication system and radar share the same frequency band, and the overlap of the frequency spectrum will interfere with each other's performance, which constitutes a dynamic non-cooperative coexistence problem. We model the FDA-MIMO radar interference suppression process as a Markov decision process (MDP). The interference signals received by FDA-MIMO radar are generated by the communication system, including constant interference, frequency-hopping interference, and intermittent interference. FDA-MIMO radar realizes flexible transmission waveform spectrum control through transmission power allocation. Reinforcement learning (RL) is applied to obtain the optimal power allocation strategy of FDA-MIMO radar, and it is compared with the Sense and Avoid (SSA) algorithm. The simulation results show that the FDA-MIMO radar power allocation method based on reinforcement learning can effectively suppress the communication system's interference and improve the radar's signal-to-interference-and-noise ratio (SINR) and range resolution.

Optimal resource allocation strategy for integrated sensing and communication cellular network with multiple user demands

AbstractFuture communication networks are widely considered to be able to provide both high‐speed communication service and reliable sensing service. Integrated sensing and communication (ISAC) is considered to be the key technology to realize this vision. Appropriate resource allocation strategy is essential for ensuring the quality of both communication and sensing services in terms of ISAC. Existing ISAC‐based resource allocation schemes mainly focus on the coexistence of sensing and communication. However, in multi‐user ISAC networks, the services required by users are diverse, including sensing‐only, communication‐only and dual‐function cases. To this end, a resource allocation strategy is proposed based on user quality of service (QoS). Specifically, the sum rate of cellular network is maximized via optimizing spectrum resource selection and transmit power, while satisfying the sensing QoS. The optimal solution to the entire problem is achieved by first demonstrating that the communication rate in this paper is a monotonically increasing function of sensing power, which allows to obtain the optimal power allocation scheme. Subsequently, a matching method is employed to obtain the optimal spectrum sharing scheme. Numerical results validate the effectiveness of the proposed algorithm and also unveil a flexible trade‐off in ISAC systems over the benchmark scheme.

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System Containing Wind-Storage Combined Unit

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System Containing Wind-Storage Combined Unit

Reconfigurable Intelligent Surfaces-Aided Relaying in Correlated Rice Fading: Performance Analysis and Design Optimization

In this paper, we investigate the spatial correlation for a practical RIS-assisted communication system operating over Rician fading. Specifically, we derive a unified analytical framework for computing the outage probability and the achiev- able diversity order for arbitrary number of reflecting elements and over arbitrarily correlated Rice fading. We also investigate the impact of spatial correlation on a hybrid architecture that amalgamates both a practical RIS and a decode and forward relay. We then attain closed-form expressions for the outage probability and show that the activation of the relay boosts the outage performance by providing and additional diversity path. The tractable results empower the asymptotic outage analyses, which are conducted not only to reveal helpful insights into understanding the impact of spatial correlation, but also enable optimal power allocation strategies to enhance the system perfor- mance under both transmission modes. Numerical results validate the tightness and effectiveness of the closed-form expressions. In particular, the harmfulness of spatial correlation and the benefit of line-of-sight links are revealed and optimal power allocation is shown to yield impressive coding gains over equal power allocation.

Secure Vehicular Communications With Varying QoS and Environments: A Unified Cross-Layer Policy-Adaptation Approach

With the ubiquitous connectivity in internet of vehicles, secure transmission is of vital importance for intelligent vehicle-to-everything (V2X) communications. However, in order to effectively guarantee the secrecy performance, once the short-term statistics of channel quality are low, which frequently happens because of the high mobility of vehicles, the throughput performance can be weakened by using the existing either cryptography techniques or physical-layer security (PLS) technique. To improve the throughput of secure data transmission, we propose the concept of statistical security, which utilizes the time-sensitive characteristics of information, such as the position of mobile vehicles, in V2X communications. Specifically, the time-sensitive information has its own outdated rate. As long as the information leakage rate is less than the information outdated rate, the eavesdropper still cannot obtain any useful information. Then, we use a queue system to characterize the eavesdropping process, where the queue-length violation probability indicates the level of secure transmission. Under this statistical security model, we investigate the throughput maximization problem and give the optimal power allocation scheme for the legitimate vehicular users. Furthermore, since the service’s security QoS requirements and wireless environments are both varying in V2X communications, we propose a unified cross-layer policy-adaption approach to guarantee the varying security QoS requirements meanwhile improving the throughput. Simulation results verify that our proposed approach significantly outperforms the existing baseline schemes.

Energy-Efficient Federated Learning Over Cell-Free IoT Networks: Modeling and Optimization

To leverage massive distributed data and computation resources in the Internet-of-Things (IoT) networks, federated learning (FL) is considered to be a promising technique with benefits of improved data privacy and communication efficiency. Meanwhile, cell-free massive multiple-input multiple-output (cell-free massive MIMO) is a promising technology to enable the IoT networks to support FL. By deploying the access points (APs) closer to the IoT devices, path loss attenuation can be reduced. However, the performance of FL is still constrained by the limited power resources of IoT devices. To address this issue, we design an energy-efficient FL scheme over cell-free IoT networks by formulating an optimization problem to minimize the total energy consumption of the IoT devices participating in the FL process. To solve the intractable problem in hand, by exploiting its unique structure, we decompose it into three sub-problems that facilitate the development of the proposed scheme. First, we derive the optimal central processing unit (CPU) operating frequency for IoT devices. Then, we design an optimal power allocation scheme to mitigate the straggler effect. Next, nonlinear programming method is adopted to obtain a sub-optimal solution for the reformulated problem. Finally, a three-stage algorithm is proposed for energy consumption minimization by considering these sub-problems. Simulation results demonstrate the close-to-optimal performance of the proposed algorithm for energy savings compared with three baseline algorithms and the capability to support large numbers of IoT device access by mitigating the straggler effect.

Incentive learning-based energy management for hybrid energy storage system in electric vehicles

Deep reinforcement learning has emerged as a promising candidate for online optimal energy management of multi-energy storage vehicles. However, how to ensure the adaptability and optimality of the reinforcement learning agent under realistic driving conditions is still the main bottleneck. To enable the reinforcement learning agent to efficiently learn the optimal power allocation strategies under diverse driving conditions, this paper proposes an incentive learning-based energy management strategy for battery-supercapacitor electric vehicles to minimize the battery capacity loss cost and power loss cost. First, an incentive reward function based on supercapacitor state-of-charge and vehicle acceleration is proposed for proximal policy optimization-based energy management strategy, which can stimulate the agent to learn for optimal power allocation policy under high load power conditions quickly. Second, a random sampling-based velocity transfer probability surface is constructed for pre-training to guarantee strategy optimality under unfamiliar driving cycles. Third, the generalized advantage estimation and layer normalization of neural networks are incorporated to improve the learning convergence. Results show that the proposed method can reduce the above costs by 5.8%–13.8% and 11.7%–38.8% compared with existing deep reinforcement learning methods under the pre-training driving cycle and test driving cycles, respectively, which yields closer results to offline dynamic programming.

Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay Networks With CCI

Hybrid satellite-terrestrial relay networks (HSTRNs) which exploit relay cooperative technology in the integrated system of satellites and terrestrial base stations (BSs) are expected to offer enhanced transmission performance benefited from the spatial gain of independent fading paths. To enable multi-user accessing and resource sharing among users, non-orthogonal multiple access (NOMA) technique can be applied in HSTRNs. In this paper, we consider a NOMA-based HSTRN where a satellite transmits signals to a relay base station (RBS) and a destination ground station (DGS), and RBS also serves a cellular user (CU) and DGS simultaneously by leveraging NOMA scheme. We assume that the satellite shares spectrum with a number of cellular small BSs (SBSs), hence SBSs will cause co-channel interference (CCI) to DGS, RBS and CU. We examine the transmission characteristics of the satellite and terrestrial links, and derive the closed-form expressions of the outage probability (OP) and average symbol error rate (ASER) of DGS and CU, respectively. Specially, we consider the effect of perfect and imperfect successive interference cancellation (SIC) on the ASER performance of CU. Then, considering the stochastic characteristics of satellite links and CCI, we analyze the mean energy efficiency (MEE) of the satellite and propose an optimal power allocation scheme which maximizes the MEE of the satellite and achieves system performance optimization. Simulation results demonstrate the effectiveness of the theoretical analysis and the proposed power allocation scheme.

Beamsteering-Aware Power Allocation for Cache-Assisted NOMA mmWave Vehicular Networks

Cache-enabled networks with multiple access (NOMA) integration have been shown to decrease wireless network traffic congestion and content delivery latency. This work investigates optimal power control in cache-assisted NOMA millimeter-wave (mmWave) vehicular networks, where mmWave channels experience double-Nakagami fading and the mmWave beamforming is subjected to beamsteering errors. We aim to optimize vehicular quality of service while maintaining fairness among vehicles, through the maximization of successful signal decoding probability for paired vehicles. A comprehensive analysis is carried out to understand the decoding success probabilities under various caching scenarios, leading to the development of optimal power allocation strategies for diverse caching conditions. Moreover, an optimal power allocation is proposed for the single-antenna case, for exploiting the cached data as side information to cancel interference. The robustness of our proposed scheme against variations in beamforming orientation is assessed by studying the influence of beamsteering errors. Numerical results demonstrate the effectiveness of the proposed cache-assisted NOMA scheme in enhancing cache utility and NOMA efficiency, while underscoring the performance gains achievable with larger cache sizes.

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

SummaryThe design of energy management strategy (EMS) plays a vital role in the power performance and economy of battery–ultracapacitor for electric vehicles. A reinforcement learning (RL)‐based EMS is proposed to obtain an optimal power allocation strategy for battery–ultracapacitor electric vehicle, and its robustness is verified at different temperatures. First of all, the dynamic characteristic experiments of the battery and ultracapacitor were performed at 10°C, 25°C, and 40°C to obtain mechanism characteristics at different temperatures. Secondly, a genetic algorithm is selected to identify the parameters of the battery and ultracapacitor model. Next, the RL‐based strategy takes the minimum energy loss of the hybrid energy storage system as the reward function and solves the optimal policy based on Markov theory. The simulation results show that the economy of the RL‐based strategy correspondingly improved by 3.05%, 3.20%, and 3.15% at different temperatures in comparison with the fuzzy‐based strategy, and the economic gap between the RL‐based strategy and the DP‐based strategy is further narrowed down to 7.30%, 3.88%, and 8.40% at different temperatures, respectively. Finally, the proposed strategy is validated under different driving conditions, which indicate that the RL‐based strategy can effectively reduce energy consumption and has good robustness at different temperatures.

QoS-aware secure transmission design for VLC with semi-grant-free NOMA scheme.

This work aims to enhance the physical layer security (PLS) of non-orthogonal multiple access (NOMA) aided indoor visible light communication (VLC) system with semi-grant-free (SGF) transmission scheme, in which a grant-free (GF) user shares the same resource block with a grant-based (GB) user whose quality of service (QoS) must be strictly guaranteed. Besides, the GF user is also provided with an acceptable QoS experience, which is closely aligned with the practical application. Both active and passive eavesdropping attacks are discussed in this work, where users' random distributions are taken into account. Specifically, to maximize the secrecy rate of the GB user in the presence of an active eavesdropper, the optimal power allocation policy is obtained in exact closed-form and the user fairness is then assessed by Jain's fairness index. Moreover, the secrecy outage performance of the GB user is analyzed in the presence of the passive eavesdropping attack. Both exact and asymptotic theoretical expressions for the secrecy outage probability (SOP) of the GB user are derived, respectively. Furthermore, the effective secrecy throughput (EST) is investigated on the basis of the derived SOP expression. Through simulations, it is found that the PLS of this VLC system can be significantly improved by the proposed optimal power allocation scheme. The radius of the protected zone, the outage target rate for the GF user, and the secrecy target rate for the GB user would have pronounced impacts on the PLS and user fairness performance of this SGF-NOMA assisted indoor VLC system. The maximum EST will increase with the increasing transmit power and it is hardly influenced by the target rate for the GF user. This work will benefit the design of indoor VLC system.

Dynamic User Resource Allocation for Downlink Multicarrier NOMA with an Actor–Critic Method

Future wireless communication systems require higher performance requirements. Based on this, we study the combinatorial optimization problem of power allocation and dynamic user pairing in a downlink multicarrier non-orthogonal multiple-access (NOMA) system scenario, aiming at maximizing the user sum rate of the overall system. Due to the complex coupling of variables, it is difficult and time-consuming to obtain an optimal solution, making engineering impractical. To circumvent the difficulties and obtain a sub-optimal solution, we decompose this optimization problem into two sub-problems. First, a closed-form expression for the optimal power allocation scheme is obtained for a given subchannel allocation. Then, we provide the optimal user-pairing scheme using the actor–critic (AC) algorithm. As a promising approach to solving the exhaustive problem, deep-reinforcement learning (DRL) possesses higher learning ability and better self-adaptive capability than traditional optimization methods. Simulation results have demonstrated that our method has significant advantages over traditional methods and other deep-learning algorithms, and effectively improves the communication performance of NOMA transmission to some extent.

Probabilistic optimal power dispatch in a droop controlled islanded microgrid in presence of renewable energy sources and PHEV load demand

This paper proposes a stochastic optimal power allocation strategy for a droop controlled islanded microgrid (DCIMG) with a medium X/R ratio in presence of combined heat and power-based distributed generators (DGs), renewable generators, switched capacitor banks, plug-in hybrid electric vehicle (PHEV) loads, voltage dependent electric loads and heat loads. Optimal values of stochastic power demand due to PHEV charging and discharging load are determined based on the goals of achieving target state-of-charge of PHEV batteries and effective peak shaving during peak load periods, respectively. Active power of dispatchable DGs is optimally allocated depending on the simultaneous fulfillment of an economic objective, an environmental objective along with three network related objectives. Hong's 2m+1 point estimate method (PEM) and Hong's 2m+1 PEM coupled with Nataf transformation (NT) are employed to handle the uncorrelated uncertainties accompanied with PHEV load demand and the correlated uncertainties associated with spatially correlated renewable generation and load demand. Active and reactive power static droop coefficients, and frequency and voltage reference settings of the dispatchable DG units are considered as the control variables for optimal dispatching of active power. A modified hybrid particle swarm and grey wolf optimizer embedded in fuzzy framework is used to solve the constrained multi-objective optimization problem. The efficacy of the proposed framework is validated on a 33-node droop controlled islanded microgrid test system.

Joint Impacts of Non-Ideal System Limitations on the Performance of NOMA-Based SatCom Networks

With the increasing shortage of satellite spectrum resources, non-orthogonal multiple access (NOMA) scheme is applied to satellite communication (SatCom) networks, which is regarded as one of the key technologies to improve spectrum efficiency. In this paper, we investigate the joint impact of non-ideal limitations, i.e., channel estimation errors, co-channel interference and imperfect successive interference cancellation on the performance of NOMA-based SatCom networks. To evaluate the performance improvement under practical scenarios, we obtain a closed-form expression for the outage probability (OP) in the presence of non-ideal limitations. To investigate the effect of non-ideal factors at high signal-to-noise ratios, an asymptotic analysis for the OP is also presented. Besides, an optimal power allocation scheme based on the non-ideal limitations is proposed to minimize the OP of the system. Finally, Monte Carlo simulations corroborate the correctness of the presented theoretical results.

Channel capacity and power allocation of MIMO visible light communication system

In this paper, the channel capacity of the multiple-input multiple-output (MIMO) visible light communication (VLC) system is investigated under the peak, average optical and electrical power constraints. Finding the channel capacity of MIMO VLC is shown to be a mixed integer programming problem. To address this open problem, we propose an inexact gradient projection method to find the channel capacity-achieving discrete input distribution and the channel capacity of MIMO VLC. Also we derive both upper and lower bounds of the capacity of MIMO VLC with the closed-form expressions. Furthermore, by considering practical discrete constellation inputs, we develop the optimal power allocation scheme to maximize transmission rate of MIMO VLC system. Simulation results show that more discrete points are needed to achieve the channel capacity as SNR increases. Both the upper and lower bounds of channel capacity are tight at low SNR region. In addition, comparing the equal power allocation, the proposed power allocation scheme can significantly increase the rate for the low-order modulation inputs.

On the application of uplink/downlink decoupled access in heterogeneous mobile edge computing

Mobile edge computing (MEC) is a key player in low latency 5G networks with the task to resolve the conflict between computationally-intensive mobile applications and resource-limited mobile devices (MDs). As such, there has been intense interest in this topic, especially in multi-user single-server and homogeneous multi-server scenarios. However, the research in the heterogeneous multi-server scenario is limited, where the servers are located at small base-stations (SBSs), macro base-stations (MBSs), or the cloud with different computing and communication capabilities. On the other hand, computational-tasks offloading is limited by the type of MD-BS association with almost all previous works focusing on offloading the MD’s computational tasks to the MEC servers/cloudlets at its serving BS. However, in multi-BS association, or downlink/uplink decoupled (DUDe) scenarios, an MD can be served by multiple BSs and hence has multiple offloading choices. Motivated by this, we proposed a joint BS association and subchannel allocation algorithm based on a student-project allocation (SPA) matching approach to minimize the network sum-latency, which breaks the constraint that one MD must connect to the same BS in the UL and DL, and jointly consider the communication and computational disparity of SBS and MBS cloudlets in heterogeneous MEC networks. Moreover, an optimal power allocation scheme is proposed to optimize the system performance subject to the predefined quality of service constraints. Our results show that the proposed scheme is superior to benchmark techniques in enabling the effective use of computational and communication resources in heterogeneous MEC networks.

Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks

The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps.

Distinctive analysis of power and frequency allocation for Device to Device (D2D) communication

Wireless communication makes an impeccable advancement from 1 G to 5 G and beyond. Simultaneously, modern technologies like Internet of Things (IoT) and Vehicular Communications also emerged. Improved connectivity between many different devices is provided by 5 G, which maximises data throughput and reduces interference. Non-Orthogonal Multiple Access (NOMA) schemes or Orthogonal Multiple Access techniques (OMA) suit 5 G. It is a NOMA-based device-to-device communication by correlating it with the Orthogonal Frequency Division Multiple Access (OFDMA). NOMA is also utilized to propose an optimal power allocation scheme and duly evaluated. The aim of such a scheme is to achieve a minimized outage and a maximized sum rate. A NOMA based Greedy Interference Algorithm is proposed for interference management by exquisite frequency allocation. The results generated from a comprehensive evaluation justify the algorithm utilization. Simulation results infer that the algorithms proposed improves system throughput and enhance energy efficiency.