Power Allocation Framework Research Articles

Optimization of Interference Management andPower Control in (Noma) With (Hetnets) Through Sectoring Techniques

The development of heterogeneous networks(HetNets), in the dynamic field of wireless communications still depends critically on the improvement of power control and interference management. An original sectoring technique to address the problems with Non-orthogonal Multiple Access (NOMA) systems such power allocation and interference management is presented in this study. It attempts to greatly lower inter-user and co-tier interference, increasing network efficiency and user fairness, by combining a strategic sectoring approach, round-robin channel allocation, and an inverse route loss power allocation framework. In the present work, the Bit Error Rate (BER) performance in a simulated two-tier NOMA-enabled HetNet is analysed using Monte Carlo simulation. The findings show that, especially at higher signal-to-noise ratios (SNR), sectoring considerably lowers BER by around 50% compared to the non-sectoring situation. The results of this work emphasize the need of sectoring in optimizing the efficiency of future wireless network infrastructures and have wide-ranging implications for the deployment of 5G networks and beyond. The findings back up the idea of combining sector-based designs to increase spectrum and energy efficiency, hence increasing the reliability and resilience of future communication systems

Optimization Framework for User Clustering, Beamforming Design and Power Allocation in MIMO-UFMC Systems

Optimization Framework for User Clustering, Beamforming Design and Power Allocation in MIMO-UFMC Systems

Low-Coupling Policy Optimization Framework for Power Allocation in Ultra-Dense Small-Cell Networks

Low-Coupling Policy Optimization Framework for Power Allocation in Ultra-Dense Small-Cell Networks

Joint Optimization Framework for User Clustering, Downlink Beamforming, and Power Allocation in MIMO NOMA Systems

In this paper, we investigate the application of downlink beamforming along with non-orthogonal multiple access (NOMA) in a multi-user multiple-input multiple-output (MIMO) system. The joint optimization framework for user clustering, downlink beamforming and power allocation scheme is formulated as a novel mixed-integer non-linear program (MINLP), where the aim is to minimize the total transmission power while satisfying quality-of-service (QoS), user clustering and power constraints. Owing to the non-convexity and combinatorial nature of the problem, obtaining an optimal solution is challenging. To tackle this issue, we first develop an algorithm based on branch-and-bound (BB), whereby the feasible space is successively partitioned and searched by means of lower and upper bounds on the objective function. While this algorithm is shown to return an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -optimal solution within a finite number of iterations, it entails high computational complexity. Considering this limitation, we then reformulate the original problem into a more tractable form and conceive a low-complexity algorithm for its solution based on the penalty dual-decomposition technique. The proposed joint design algorithms for MIMO NOMA are evaluated by means of simulations over mmWave channels. Results show significant improvements in terms of total transmit power and spectral efficiency compared to benchmark approaches.

An MEC-Enabled Framework for Task Offloading and Power Allocation in NOMA Enhanced ABS-Assisted VANETs

This letter investigates the application of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) in air base station (ABS)-assisted vehicular ad hoc networks (VANETs). A tethered ABS acts as the server to provide MEC services for vehicles. Vehicles adopt NOMA to offload their tasks to the ABS. For the considered network, we formulate an average task processing ratio maximization problem by jointly optimizing the offloading decision, computation resource allocation, and power allocation. For tackling this non-convex problem, we first obtain the offloading decision and the computation resource allocation with the aid of the relax-and-round method and the convex problem solver. Then, the optimal transmission power allocation of vehicles is derived by considering the successive interference cancellation decoding threshold. Finally, the simulation results show that the proposed algorithm has a significant performance improvement in the average task processing ratio compared with the current works in different urban scenarios.

Optimal LED Power Allocation Framework for a Location-Assisted Indoor Visible Light Communication System

The widespread deployment of white light-emitting6 diodes (LEDs) for illumination provides a unique opportunity to create a flexible, accurate, and ubiquitous indoor communication and positioning system. In this paper, we exploit the location information obtained via LEDs to improve the communication performance of an indoor visible light communication (VLC) system. Specifically, we propose an optimal LED power management framework to maximize the average data rate across the room while satisfying the bit error rate (BER) and illumination constraint across the room. The maximum allowed localization error, as a function of the number of blockages and the LED irradiance angle, have been calculated. In addition, the closed-form expression for the BER is derived for the proposed optimal LED power allocation scheme. We have also formulated an optimization problem that will maximize the power savings among the LEDs with respect to the number of blockages and permissible error in localization. It has been shown that, by employing the proposed optimal LED power allocation will results in a significant amount of power-saving, which is approximately 40% for 4 LEDs configuration and 70% 26 for 8 LEDs configuration as compared to equal power allocation. Further, the maximum allowed localization error is found out to be 28 approximately 7 cm and 18 cm with 4 and 8 LEDs, respectively, to 29 achieve the maximum achievable data rate. Finally, it is shown that 30 the dimming range of up to 70% can be achieved for the proposed system.

Deep Reinforcement Learning for Channel Selection and Power Allocation in D2D Communications

Device-to-device (D2D) communication is regarded as a key technical component of the fifth-generation (5G), D2D communication usually reuses spectrum resources with cellular users (CUs). To mitigate interference to cellular links and improve spectrum efficiency, this paper investigates a sum-rate maximization problem in the underlay of D2D communication. Particularly, a joint channel selection and power allocation framework based on multi-agent deep reinforcement learning is proposed, named Double Deep Q-Network (DDQN). It can adeptly select the channel and allocate power in a dynamic environment. The proposed scheme only requires local information and some outdated nonlocal information, which reduces signaling overheads significantly. Simulation results show that the proposed scheme can improve the D2D sum rate and ensure quality-of-service (QoS) of CUs compared with other benchmarks.

Game Theoretic Countermeasure Analysis for Multistatic Radars and Multiple Jammers

AbstractRadar and jammer can be considered as two players in the countermeasure game of electronic warfare (EW). A game framework for joint beamforming and power allocation is investigated for multistatic radars and multiple jammers in this paper. Under a certain target detection criterion, the major aim of every radar is to reduce its power consumption and suppress various jammings. Conversely, the multi‐agent jammers determine their transmitting power strategies to interfere with the multistatic radar network, which is based on the transmitting power of the radars. According to the countermeasure game, both existence and uniqueness are strictly confirmed about the Nash Equilibrium (NE) of the proposed game. Besides, the receive beamformer weight vector can be obtained by minimum variance distortionless response (MVDR). And the corresponding transmit beamformer weight vector is also obtained. An algorithm for joint beamforming and power allocation is proposed, which is iteratively convergent. Finally, the convergence and validity for the proposed algorithm and the interference rejection capability of the multistatic radars are supported by the numerical results.

Multi‐agent deep reinforcement learning‐based energy efficient power allocation in downlink MIMO‐NOMA systems

NOMA and MIMO are considered to be the promising technologies to meet huge access demands and high data rate requirements of 5G wireless networks. In this paper, the power allocation problem in a downlink MIMO-NOMA system to maximize the energy efficiency while ensuring the quality-of-service of all users is investigated. Two deep reinforcement learning-based frameworks are proposed to solve this non-convex and dynamic optimization problem, referred to as the multi-agent DDPG/TD3-based power allocation framework. In particular, with current channel conditions as input, every single agent of two multi-agent frameworks dynamically outputs the optimum power allocation policy for all users in every cluster by DDPG/TD3 algorithm, and the additional actor network is also added to the conventional multi-agent model in order to adjust power volumes allocated to clusters to improve overall performance of the system. Finally, both frameworks adjust the entire power allocation policy by updating the weights of neural networks according to the feedback of the system. Simulation results show that the proposed multi-agent deep reinforcement learning based power allocation frameworks can significantly improve the energy efficiency of the MIMO-NOMA system under various transmit power limitations and minimum data rates compared with other approaches, including the performance comparison over MIMO-OMA.

Energy-Efficient Resource Allocation for Ultra-Dense Licensed and Unlicensed Dual-Access Small Cell Networks

In this study, an energy-efficient self-organized framework for sub-channel allocation and power allocation is presented for ultra-dense small cell networks, which can operate in both licensed and unlicensed bands. In order to protect legacy WiFi devices (operating in unlicensed bands), we consider the Long-Term Evolution (LTE) operation in unlicensed bands based on Carrier Sense Adaptive Transmission (CSAT), in which 'ON' and 'OFF' duty cycle approach is utilized. On the other hand, there are severe interference management problems among small cells (operating in licensed and unlicensed bands) and between macro cells and small cells (operating in licensed bands) due to co-channel and ultra-dense deployment of small cells. This article proposes a self-organized optimization framework for the allocation of sub-channels and power levels by exploiting a non-cooperative game with the objective to maximize the energy efficiency of dual-access small cells without creating harmful impact on coexisting network entities including macro cell users, small cell users, and legacy WiFi devices. Simulation results show that the proposed scheme outperforms (6 and 11 percent) and (8 and 18 percent) the round-robin and the spectrum-efficient schemes, respectively, for two different small cell scenarios. In addition, it is shown that for less channel state information (CSI) estimation errors ς = 0.02, the maximum performance degradation of the proposed scheme is reasonably small (5.5 percent) as compared to the perfect CSI.

Cooperative PSK constellation design and power allocation for massive MIMO uplink communications

This paper considers a massive multiple-input multiple-output (MIMO) wireless uplink communication system over Rayleigh fading channels. In this system, two single-antenna nodes timely upload data to a base station (BS) with a large number of antennas on the same time-frequency resources. The small scale fading coefficients keep constant during two consecutive time slots, after which they change into new independent values. Assuming only the large-scale fading coefficients are available, we construct a maximum likelihood (ML) detector without the instantaneous small scale fading coefficients at the BS. The key idea is to utilize the absolutely additively uniquely decomposable constellation pair with a phase-shift keying (PSK) modulation scheme. Specifically, we use a rotated PSK constellation for one of the nodes to ensure the unique identification of transmitted symbols in a noise-free case. In a noisy case, we propose a power allocation framework for the proposed modulation scheme to improve the ML detector's average symbol error performance. Finally, simulations results verify the effectiveness of our proposed scheme.

Power Allocation in CoMP-Empowered C-NOMA Networks

In this letter, the dynamic power allocation problem of a cellular network consisting of two adjacent and coordinating cells is investigated. The joint transmission coordinated multipoint (JT-CoMP) between the two-cell is introduced to assist users experiencing high inter-cell interference, where each cell invokes cooperative non orthogonal multiple access (C-NOMA) to serve its associated devices. Both effects of imperfect successive interference cancellation (SIC) and imperfect channel estimation are considered within the proposed scheme. A power allocation framework is formulated as an optimization problem with the objective of maximizing the network sum-rate while guaranteeing a certain quality-of-service (QoS) for each user. The formulated optimization problem is neither concave nor quasi-concave, which is difficult to be solved directly unless using heuristic methods, which comes with the expense of high computational complexity. To overcome this issue, a near-optimal closed-form expressions for the power allocation are derived. The simulation results show that our purposed scheme achieves an average sum-rate that is 3% less than the one of the optimal power control but it can save up to 99% in the computational time. In addition, the superiority of the proposed CoMP C-NOMA scheme is demonstrated when compared to the well known C-NOMA scheme.

Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications

This article proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.

Efficient Power-Splitting and Resource Allocation for Cellular V2X Communications

The research efforts on cellular vehicle-to-everything (V2X) communications are gaining momentum with each passing year. It is considered as a paradigm-altering approach to connect a large number of vehicles with minimal cost of deployment and maintenance. This article aims to further push the state-of-the-art of cellular V2X communications by providing an optimization framework for wireless charging, power allocation, and resource block assignment. Specifically, we design a network model where roadside objects use wireless power from RF signals of electric vehicles for charging and information processing. Moreover, due to the resource-constraint nature of cellular V2X, the power allocation and resource block assignment are performed to efficiently use the resources. The proposed optimization framework shows an improvement in terms of the overall energy efficiency of the network when compared with the baseline technique. The performance gains of the proposed solution clearly demonstrate its feasibility and utility for cellular V2X communications.

NOMA-Based Pervasive Edge Computing: Secure Power Allocation for IoV

Nowadays, intelligent transportation industry is becoming a hot spot in Internet of vehicles (IoV). However, owing to the existence of numerous intelligent terminals, communication security becomes a pressing problem. On the other hand, pervasive edge computing (PEC), as a pivotal technology, can significantly improve the performance of the system compared to the traditional cloud computing. In this article, we propose a nonorthogonal multiple access (NOMA)-based PEC power allocation framework in IoV, aiming at minimizing the system latency in the presence of eavesdroppers. Besides, queuing models, imperfect channel state information, and vehicles' speeds are all considered. Since the formulated problem is complicated, we consider its lower bound and derive the suboptimal closed-form expressions of the power allocation coefficients. Furthermore, a Frank-and-Wold algorithm is proposed to achieve the optimum total power. Simulation results illustrate the superior performance of the proposed NOMA scheme.

Efficient power allocation for NOMA-enabled IoT networks in 6G era

Due to unrivaled effectiveness, non-orthogonal multiple access (NOMA) has risen as a promising multiple access scheme for the Internet of things (IoT). In this paper, we provide a new power allocation technique for improving the energy and spectral efficiency of NOMA-enabled IoT devices. The power allocation is performed without compromising the quality of service (QoS) requirements of the network. By considering the transmit power, QoS and successive interference cancellation (SIC) constraints, we use the sequential quadratic programming (SQP) technique to solve the non-convex problem. To assess the performance of our scheme, we compare the proposed SQP-based approach with the conventional KKT-based optimization method. We provide Monte Carlo simulation results to assess our proposed power allocation framework and illustrate the performance improvements against orthogonal multiple access (OMA) scheme. The results uncover that the proposed SQP-based power optimization design substantially improves the performance of the NOMA-enabled IoT network.

Unified User Association and Contract-Theoretic Resource Orchestration in NOMA Heterogeneous Wireless Networks

Lately, the deployment of heterogeneous wireless networks has emerged, as part of the 5G vision, to cope with the users' exaggerated service demands. In this context, the application of Non-Orthogonal Multiple Access (NOMA) technique constitutes a promising solution to facilitate a balance between spectral efficiency and system complexity. In this article, we consider the problem of joint user association and uplink power allocation, in heterogeneous 5G wireless networks, employing NOMA technology. The coupled problem is treated under an incomplete information scenario, where the Base Stations (BSs) have statistical only knowledge of the users' channel conditions. To deal with the incompleteness of Channel State Information (CSI), a Contract Theory (CT) based approach is introduced. A Reinforcement Learning (RL) based methodology, capitalizing on the provided feedback from the communication environment is initially adopted, in order to achieve the users to BS association in an iterative and distributed manner. The problem of uplink power allocation is subsequently formulated as a contract between each BS and its corresponding users. The optimal power is thus obtained as the solution of the optimization of each BS's utility function, while ensuring the optimality of the utility function of each associated user, given the unique communications characteristics and type of each user. Detailed numerical evaluation of the performance of the proposed unified user association and power allocation framework is provided, via modeling and simulation, illustrating its operation, features and benefits, under densely deployed heterogeneous environments.

Distributed Cooperative Economic Optimization Strategy of a Regional Energy Network Based on Energy Cell–Tissue Architecture

The conventional centralized control method can effectively solve the influence of system power imbalance on the optimal dispatching of a power network and stabilize the power fluctuation of tie lines when the control precision is relatively high. However, when there are disturbances and uncertainties in the system, the robustness is insufficient. The distributed control method in economic optimization scheduling has the advantages of simple communication and high reliability and is well adapted to the energy dispersion characteristics of a distributed generator within the regional energy network. However, the control accuracy is slightly less than that of the centralized control method, and when the distributed power encounters a random input or exit, the corresponding sparse network matrix needs to be updated every time, complicating the use of this system for economic operation. In addition, when message loss or communication noise occurs in the communication path, the robustness of distributed control is greatly reduced. To address these issues, this paper proposes a novel power allocation framework to solve the economic optimization problem of a regional energy network that requires only local information exchange among neighboring energy cells and tissues. Correspondingly, to meet the supply–demand balance, a distributed cooperative approach based on the equal incremental principle is used to impart decentralized autonomy to the whole system. In addition, the concept of virtual consistency variables is introduced to manage topological change caused by power excursions in the energy cell and to implement plug-and-play capabilities. Relative to the conventional collaborative control method, the case studies demonstrate the scalability, plug-and-play capability, and robustness of decentralized autonomous control strategies in terms of communication delay, communication failure, and message loss, among other aspects.

Collaborative detection and power allocation framework for target tracking in multiple radar system

In reality, multiple radar system (MRS) may have some limited resource, such as the data computation capacity of the fusion center and the transmit energy of each radar. To better exploit its limited system resource, a collaborative detection and power allocation (CDPA) scheme is developed for the application of target tracking in clutter. The basis of the CDPA scheme is to use optimization technique to control the false alarm rate (FAR) and transmit power of each radar in view of the aforementioned resource constraints, while achieving better target state estimation accuracy. The Bayesian Cramér–Rao lower bound is derived, relaxed, and subsequently utilized, as the optimization criterion for the CDPA strategy. The resulting nonlinear and nonconvex optimization problem consists of two adaptable vectors, one for FAR selection and the other for power allocation. By introducing an auxiliary vector, a fast two-step solution technique is presented to jointly select the FAR and distribute the transmit power. Simulation results demonstrate that, with given data computation capability and system total power budget, the CDPA scheme can evidently expand the detection range, increase the resource utilization efficiency of the MRS, and improve the target tracking accuracy.

Control of NCSs with energy efficient channel assignment and power allocation

Abstract This paper investigates the simultaneous stabilization of a collection of battery-powered networked control systems (NCSs) with medium access constraint and communication energy limitation. Each time the channels can only accommodate a limited number of systems for communication and the successful packet arrival rate at the controller depends on the power used by the sensor. Then in order to satisfy the medium access constraint, reduce the communication energy cost and stabilize the collection of NCSs, one needs to decide when to schedule which plant to communicate by which channel and by which power level. Based on the average dwell time technique, sufficient conditions for the exponential mean square stability of a single plant are given at first. Then, based on the stability conditions, a period scheduling policy is proposed to assign the channel accessing sequence and power level such that all the plants are simultaneously stabilized. Finally, a new framework for channel assignment, power allocation and controller design is constructed, which can guarantee a desired decay rate for each system and a minimal energy consumption for the group of systems. The effectiveness of the methodology is demonstrated with numerical simulations.