Power Allocation In Wireless Networks Research Articles

Autonomous bonobo optimization algorithm for power allocation in wireless networks

Bonobo optimizer (BO) is a recent metaheuristic algorithm inspired by the social behavior of bonobos. BO maintains a robust search strategy based on two distinct phases that involve interesting mechanisms, such as the fission–fusion method for selection and an efficient process for producing new candidate solutions. Despite its remarkable capacity, BO presents a critical flaw that corresponds to an inappropriate balance between exploration and exploitation. Under such conditions, suboptimal or even poor solutions can be obtained. This paper proposes a modified BO algorithm in which the trajectory of each search agent is modified dynamically through the adaptation of the main parameters of the algorithm. With this new mechanism, the algorithm allows for the exploration and exploitation of different regions of the solution space and determines the global optimum in a faster manner. The performance of the proposed algorithm is evaluated by considering two scenarios. One is the optimization of 23 benchmark functions and the second is the problem of power allocation in wireless networks. The results show that the proposed scheme enhances the performance of the basic BO method by increasing the robustness and providing a better solution quality.

Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air

Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links. We first apply the message passing neural network (MPNN), a unified framework of GNN, to solve the problem. We show that the signaling overhead grows quadratically as the network size increases. Inspired from the over-the-air computation (AirComp), we then propose an Air-MPNN framework, where the messages from neighboring nodes are represented by the transmit power of pilots and can be aggregated efficiently by evaluating the total interference power. The signaling overhead of Air-MPNN grows linearly as the network size increases, and we prove that Air-MPNN is permutation invariant. To further reduce the signaling overhead, we propose the Air message passing recurrent neural network (Air-MPRNN), where each node utilizes the graph embedding and local state in the previous frame to update the graph embedding in the current frame. Since existing communication systems send a pilot during each frame, Air-MPRNN can be integrated into the existing standards by adjusting pilot power. Simulation results validate the scalability of the proposed frameworks, and show that they outperform the existing power allocation algorithms in terms of sum-rate for various system parameters.

A stochastic geometric programming approach for power allocation in wireless networks

A stochastic geometric programming approach for power allocation in wireless networks

Graph-Based Algorithm Unfolding for Energy-Aware Power Allocation in Wireless Networks

We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non-convex nature of the problem, the proposed method consists of modular structures inspired by a classical iterative suboptimal approach and enhanced with learnable components. More precisely, we propose a deep unfolding of the successive concave approximation (SCA) method. In our unfolded SCA (USCA) framework, the originally preset parameters are now learnable via graph convolutional neural networks (GCNs) that directly exploit multi-user channel state information as the underlying graph adjacency matrix. We show the permutation equivariance of the proposed architecture, which is a desirable property for models applied to wireless network data. The USCA framework is trained through a stochastic gradient descent approach using a progressive training strategy. The unsupervised loss is carefully devised to feature the monotonic property of the objective under maximum power constraints. Comprehensive numerical results demonstrate its generalizability across different network topologies of varying size, density, and channel distribution. Thorough comparisons illustrate the improved performance and robustness of USCA over state-of-the-art benchmarks.

Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in wireless networks. Existing techniques typically find near-optimal power allocations by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a distributively executed dynamic power allocation scheme is developed based on model-free deep reinforcement learning. Each transmitter collects CSI and quality of service (QoS) information from several neighbors and adapts its own transmit power accordingly. The objective is to maximize a weighted sum-rate utility function, which can be particularized to achieve maximum sum-rate or proportionally fair scheduling. Both random variations and delays in the CSI are inherently addressed using deep Q-learning. For a typical network architecture, the proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents. The proposed scheme is especially suitable for practical scenarios where the system model is inaccurate and CSI delay is non-negligible.

An iterative heuristic approach for channel and power allocation in wireless networks

A large number of access points (APs) may be required to provide network coverage in a given environment. However, a non-optimized deployment of these APs can reduce the network performance significantly due to the excessive interference. The optimization of the channel and transmit power is an efficient method to reduce the interference and therefore guarantee a minimally acceptable performance. In this paper, we propose a heuristic algorithm to find a proper channel and transmit power configuration for all APs within a network. The algorithm evaluates the network, using theoretical models, and employs several techniques to optimize the channel and the transmit power to increase the network performance. We also estimate the complexity of the proposed algorithm comparing it with an exhaustive search approach. The results show that the proposed algorithm can arrive at a solution very close to the optimal with a much reduced computational complexity.

Optimizing power allocation in wireless networks: Are the implicit constraints really redundant?

The widely considered power constraints on optimizing power allocation in wireless networks, e.g., non-negative individual power and limited sum of all the individual power, imply the constraints where each individual power is not greater than the limited sum. However, the related implicit constraints are generally regarded as redundant for algorithm design in most current studies. In this paper, we explore the question “Are the implicit constraints really redundant?” in the optimization of power allocation especially when using iterative methods (e.g., subgradient method) that have slow convergence speeds. Using the water-filling problem as an illustration, we first derive the structural properties of the optimal solutions based on Karush–Kuhn–Tucker conditions. Then we propose a non-iterative closed-form optimal method and use iterative methods (i.e., bisection method and subgradient method) to solve the problem. Our theoretical analysis shows that the implicit constraints are not redundant, and particularly, their consideration can effectively speed up the convergence of the subgradient method and reduce its sensitivity to the chosen step size. Numerical results for the water-filling problem and another existing power allocation problem demonstrate the effectiveness of considering the implicit constraints.

Communication on SWIPT and EH Using Electromagnetic Behaviour for Power Allocation in Wireless Networks

This review paper focuses on the basic relations between wireless power transfer, wireless information transfer and combined phenomenon of simultaneous wireless information and power transfer. The authors reviewed and discussed electromagnetic fields behaviour (EMB) for enhancing the power allocation strategies (PAS) in energy harvesting (EH) wireless communication systems. Further, this paper presents relations between Friis transmission equation and Maxwell’s equations to be used in propagation models for reduction in specific absorption rate (SAR). This paper provides a review of various methods and concepts reported in earlier works. This paper also reviews Poynting vector and power densities along with boundary conditions for antennas and human body. Finally, this paper explores the usage of electromagnetic behaviour for the possible enhancement in power saving methods for electromagnetic behaviour centered-wireless energy harvesting (EMBC-WEH). At the same time, possibilities of PAS for reduction in SAR are discussed.

Optimal scheduling and power allocation in wireless networks with heavy traffic: the infinite time horizon case

ABSTRACTThis paper addresses the problem of joint transmit power allocation and time-slot scheduling in a wireless communication system with time-varying traffic. The system is handled by a single base station transmitting over time-varying channels. The operating time horizon is divided into time slots; a fixed amount of power is available at each time slot. The mobile users share each time slot and the power available at this time slot with the objective of minimising their expected total delay. This is reformulated, via a heavy traffic approximation, as the optimal control of a reflected diffusion in the positive orthant. We address this optimisation problem using the dynamic programming principle and establish Bellman's equation; here the main feature consists in how to deal with the reflection of the diffusion process on the boundary of its state space. A closed-form solution for the optimal control problem is provided. The proposed solution relies on, among other issues, the knowledge of the intensity of the driving process in the diffusion. Accordingly, we compute this intensity given a model for the multidimensional channel: a purely discontinuous process with known characteristics.

Event‐triggered feedback for power allocation in wireless networks

Transmission power control constitutes a core feature required to achieve a desired quality of service in wireless networks. A practical implementation calls on the intuition of using limited resources, only when are strictly required. Thus, the appeal of an event-triggered scheme arises naturally over more traditional, but less efficient, periodic time-triggered control solutions. Motivated by these ideas, the authors present here a power allocation strategy based on an event-triggered transmission (ETT) to reduce the feedback signalling in the network. The power allocation under ETT is based on a nominal control that guarantees internal stability and performance. Hence, if the feedback signalling does not change significantly from the previous sample, no feedback transmission is assigned to reduce bandwidth usage. Two schemes are employed to define the ETT while preserving closed-loop stability: a piecewise quadratic Lyapunov argument and a perturbed linear system approach. These two approaches establish a trade-off between complexity and conservativeness. The power allocation under ETT was thoroughly evaluated under two nominal control schemes to establish a relation between performance and feedback reduction.

Optimal scheduling and power allocation in wireless networks with heavy traffic

This paper addresses the problem of joint transmit power allocation and time slot scheduling in a wireless communication system with time varying traffic. The system is handled by a single base station transmitting over time varying channels. This may be the case in practice of a hybrid TDMA-CDMA (Time Division Multiple Access-Code Division Multiple Access) system. The operating time horizon is divided into time slots; a fixed amount of power is available at each time slot. The users share each time slot and the power available at this time slot with the objective of minimizing the expected total queue length. The problem is reformulated, via a heavy traffic approximation, as the optimal control of a reflected diffusion in the positive orthant. We establish a closed form solution for the obtained control problem. The main feature that makes it possible is an astute choice of some auxiliary weighting matrices in the cost rate. The proposed solution relies also on the knowledge of the covariance matrix of the non-standard multi-dimensional Wiener process which is the driving process in the reflected diffusion. We then compute this covariance matrix given the stationary distribution of the multi-dimensional channel process. Further stochastic analysis is provided: the cost variance, and the Fokker–Planck equation for the distribution density of the queue length.

A General Mechanism Design Methodology for Social Utility Maximization with Linear Constraints

Social utility maximization refers to the process of allocating resources in a way that maximizes the sum of agents' utilities, under the system constraints. Such allocation arises in several problems in the general area of communications, including unicast (and multicast multi-rate) service on the Internet, as well as in applications with (local) public goods, such as power allocation in wireless networks, spectrum allocation, etc. Mechanisms that implement such allocations in Nash equilibrium have also been studied but either they do not possess the full implementation property, or are given in a case-by-case fashion, thus obscuring fundamental understanding of these problems. In this paper we propose a unified methodology for creating mechanisms that fully implement, in Nash equilibria, social utility maximizing functions arising in various contexts where the constraints are convex. Two additional design goals are the focus of this paper: a) the size of the message space scaling linearly with the number of agents (even if agents' types are entire valuation functions), b) allocation being feasible on and off equilibrium.

Optimal Routing and Power Allocation for Wireless Networks with Imperfect Full-Duplex Nodes

The joint routing and power allocation problem in a wireless full-duplex network with imperfect interference cancellation and a single source destination pair is solved. Our simplified interference model focuses on the effects of full-duplex by including residual self-interference and one hop interference while other interfering signals are assumed to be negligible. First, the optimal power allocation for a fixed route is identified. Then a modification of Dijkstra's algorithm is used to find the joint route and power allocation to maximize throughput. Due to interference, the algorithm proposed has a non decomposable objective function; however, it is efficiently solved for every candidate route. Our solution in a full interference model, that does not ignore any interference, is at most a constant bound away from the optimal solution in the full interference model. Through simulations we evaluate several scenarios and show the behavior of the solution in both interference models.

Distributed and Autonomous Resource and Power Allocation for Wireless Networks

In this paper, a distributed and autonomous technique for resource and power allocation in orthogonal frequency division multiple access (OFDMA) femto-cellular networks is presented. Here, resource blocks (RBs) and their corresponding transmit powers are assigned to the user(s) in each cell individually without explicit coordination between femto-base stations (FBSs). The “allocatability” of each resource is determined utilising only locally available information of the following quantities: the required rate of the user; the quality (i.e., strength) of the desired signal; the frequency-selective fading on each RB; and the level of interference incident on each RB. Using a fuzzy logic system, the time-averaged values of each of these inputs are combined to determine which RBs are most suitable to be allocated in a particular cell, i.e., which resources can be allocated such that the user requested rate(s) in that cell are satisfied. Fuzzy logic presents a completely novel, low-complexity methodology for inter-cell interference coordination (ICIC). A comprehensive study of this system in a femtocell environment is performed, yielding system performance improvements in terms of throughput, energy efficiency and coverage over state-of-the-art ICIC techniques.

Fast Global Optimal Power Allocation in Wireless Networks by Local D.C. Programming

Power allocations in an interference-limited wireless network for global maximization of the weighted sum throughput or global optimization of the minimum weighted rate among network links are not only important but also very hard optimization problems due to their nonconvexity nature. Recently developed methods are either unable to locate the global optimal solutions or prohibitively complex for practical applications. This paper exploits the d.c. (difference of two convex functions/sets) structure of either the objective function or constraints of these global optimization problems to develop efficient iterative algorithms with very low complexity. Numerical results demonstrate that the developed algorithms are able to locate the global optimal solutions by only a few iterations and they are superior to the previously-proposed methods in both performance and computation complexity.

Performance analysis of random uniform power allocation for wireless networks in Rayleigh fading channels

AbstractWhen there is no feedback channels in wireless sensor networks or when the sensors are deaf (cannot listen to the feedback channels), the conventional and practical method for power allocation is to use fixed power transmission. This method has many limitations such as near–far problems where the relatively far away sensors are blocked by near sensors and also the unnecessarily high power consumption for good channel sensors. One recent proposal to overcome such limitations is to use uniform random power allocation for terminals. In that work the system performance is analysed for static channels (i.e. snapshot assumption), where it shows that there is a considerable obtained gain when using uniform random power allocation over fixed power. An important question is about the performance of such transmission power strategy in case of a more realistic channel scenario such as Rayleigh fading channels. This paper answers this question. Detailed mathematical analysis as well as Monte‐Carlo simulations for the system performance in Rayleigh fading channels are given in this paper. We show that the performance of uniform power allocation is much worse in case of fading channels than in static channels. Almost there is no gain of using uniform random power over fixed power in terms of average probability of outage. However, uniform random power may considerably reduce the total power consumption at small degradation of the average performance. This can be critical feature in energy constrained networks. Copyright © 2009 John Wiley & Sons, Ltd.

Decode-and-Forward Cooperative Diversity with Power Allocation in Wireless Networks

We study power allocation for the decode-and-forward cooperative diversity protocol in a wireless network under the assumption that only mean channel gains are available at the transmitters. In a Rayleigh fading channel with uniformly distributed node locations, we aim to find the power allocation that minimizes the outage probability under a short-term power constraint, wherein the total power for all nodes is less than a prescribed value during each two-stage transmission. Due to the computational and implementation complexity of the optimal solution, we derived a simple near-optimal solution. In this near-optimal scheme, a fixed fraction of the total power is allocated to the source node in stage I. In stage II, the remaining power is split equally among a set of selected nodes if the selected set is not empty, and otherwise is allocated to the source node. A node is selected if it can decode the message from the source and its mean channel gain to the destination is above a threshold. In this scheme, each node only needs to know its own mean channel gain to the destination and the number of selected nodes. Simulation results show that the proposed scheme achieves an outage probability close to that for the optimal scheme obtained by numerical search, and achieves significant performance gain over other schemes in the literature