Distributed Power Allocation Scheme Research Articles

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.

Security performance analysis for cell-free massive multiple-input multiple-output system with multi-antenna access points deployment in presence of active eavesdropping

This article investigates the influence of multiple-antenna deployment at access points on physical layer security under cell-free massive multiple-input multiple-output systems. To embody the concept of network multiple-input multiple-output, cell-free massive multiple-input multiple-output employs a great deal of geographically distributed access points, which jointly serve multiple users in the same frequency-time resource. Assuming a malicious active eavesdropper having knowledge of instantaneous channel state information, we derive the asymptotic closed-form expression of the achievable secrecy rate, which is used to evaluate network security performance. A distributed power allocation scheme is developed based on channel fading degrees of different users. All of access points distribute a larger proportion of local transmission power to the users with better channel conditions. We verified that the security performance of the multi-antenna access points outperforms that of the single one. Compared to equal power allocation, the proposed power control algorithm can further boost the network security performance.

Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks

In wireless caching networks, the design of the content delivery method must consider random user requests, caching states, network topology, and interference management. In this article, we establish a general framework for content delivery in wireless caching networks without stringent assumptions that restrict the network structure and interference model. Based on the framework, we propose a dynamic and distributed link scheduling and power allocation scheme for content delivery that is assisted by belief-propagation (BP) algorithms. The proposed scheme achieves three critical purposes of wireless caching networks: 1) limiting the delay of user request satisfactions, 2) maintaining the power efficiency of caching nodes, and 3) managing interference among users. In addition, we address the intrinsic problem of the BP algorithm in our network model, proposing a matching algorithm for one-to-one link scheduling. Simulation results show that the proposed scheme provides almost the same delay performance as the optimal scheme found through an exhaustive search at the expense of a little additional power consumption and does not require a clustering method and orthogonal resources in a large-scale D2D network.

Energy efficient power control for device to device communication in 5G networks

Next generation cellular networks require high capacity, enhanced energy efficiency and guaranteed quality of service (QoS). In order to meet these targets, device-to device (D2D) communication is being considered for future 5th generation especially for certain applications that require the proximity gain, the reuse gain, and the hop gain. In this paper, we investigate energy efficient power control for the uplink of an OFDMA (orthogonal frequency-division multiple access) single-cell communication system composed of both regular cellular users and device to device (D2D) pairs. Firstly, we analyze and mathematically model the actual requirements forD2D communications and traditional cellular links in terms of minimum rate and maximum power requirement. Secondly, we use fractional programming in order to transform the original problem into an equivalent concave one and we use the non-cooperative Game theory in order to characterize the equilibrium. Then, the solution of the game is given as a water-filling power allocation. Furthermore, we implement a distributed power allocation scheme using three ways: a) Fractional programming techniques b) Closed form expression (the novelty is the use of wright omega function). c) Inverse water filling. Finally, simulations in both static and dynamic channel setting are presented to illustrate the improved performance in term of EE, SE (spectral efficiency) and time of execution of the iterative algorithm (Dinkelbach) than the closed form algorithms.

Statistical Channel Knowledge-Based Distributed Power Allocation for Analog Network Coding in High SNR

A novel distributed power allocation scheme for analog network coding (ANC) in high signal-to-noise ratio (SNR) is proposed by using statistical channel knowledge at the transmitters only. The scheme considers nonorthogonal amplified-and- forward (NAF) protocol. In the derivation, a general Rayleigh fading multiple hop network is accurately approximated by a simpler two hop network, and the closed-formed expression for the outage probability of the simplified two hop network is obtained and optimized with respect to the power allocation. Numerical results show that the derived power allocation scheme has significant performance gain over the equal power scheme. In some cases, it approaches the optimal scheme.

Bidirectional wireless information and power transfer for decode‐and‐forward relay systems

In this study, the authors investigate the bidirectional wireless information and power transfer (BWIPT) in decode-and-forward (DF) relay systems, where the bidirectional relay can decode and forward information from the user to the access point (AP), and assist the wireless power transfer from the AP to the user. The relay employs the power splitting (PS) protocol to coordinate the received signal energy for information transmission and energy harvesting. By converting the multi-relay system information rate maximisation problem into a convex optimisation problem, the distributed power allocation scheme is obtained to maximise the information rate. Particularly, for single-relay systems, the authors derive the closed-form expression of the optimal PS factor, which can maximise the information rate. Simulation results show that the BWIPT for DF relay systems outperforms the BWIPT for amplify-and-forward relay systems.

A Robust Power Allocation Scheme in Ad-Hoc Cognitive Radio Networks

We investigate the secondary users (SUs) power allocation in ad-hoc cognitive radio networks, considering an underlay paradigm where SUs can access the licensed spectrum owned by the primary users (PUs), if the total power to SUs and the interference power threshold from PUs and the signal-to-interference-noise-ratio (SINR) requirements of SUs are guaranteed. While prevalent works about distributed resources allocation algorithms are mainly focused on power allocation under the consideration of many practical limitations, which may not be the case in practice. We assume that SUs also are needed to satisfy heterogenous constraints with different positions by taking the fairness of SUs into account. Moreover, we develop a weighted robust distributed power allocation scheme to guarantees the fairness of SUs. Techniques from robust optimization theory are used to study an objective formulation with infinite numbers of constraints. The optimal solution is obtained by using Lagrange dual method, and the Lagrange multiplier is solved by the sub-gradient update algorithm. The feasibility and availability of our proposed power allocation scheme is confirmed by numerical results.

Distributed Power Allocation for D2D Communications Underlaying/Overlaying OFDMA Cellular Networks

The implementation of device-to-device (D2D) underlaying or overlaying preexisting cellular networks has received much attention due to the potential of enhancing the total cell throughput, reducing the power consumption, and increasing the instantaneous data rate. In this paper, we propose a distributed power allocation scheme for D2D OFDMA communications and, in particular, we consider the two operating modes amenable to a distributed implementation: dedicated and reuse modes. The proposed schemes address the problem of maximizing the users’ sum rate subject to power constraints, which is known to be nonconvex and, as such, extremely difficult to be solved exactly. We propose here a fresh approach to this well-known problem, capitalizing on the fact that the power allocation problem can be modeled as a potential game. Exploiting the potential games property of converging under better response dynamics, we propose two fully distributed iterative algorithms, one for each operation mode considered, where each user updates sequentially and autonomously its power allocation. Numerical results, computed for several different user scenarios, show that the proposed methods, which converge to one of the local maxima of the objective function, exhibit performance close to the maximum achievable optimum and outperform other schemes presented in the literature.

Power control based on the Stackelberg game in two-tier femtocell networks

In this paper, the resource allocation strategy is investigated for a spectrum sharing two-tier femtocell networks, in which a central macrocell is underlaid with distributed femtocells. The spectral radius is introduced to address the conditions that any feasible set of users’ signal-to-interference-plus-noise ratio requirements should satisfy in femtocell networks. To develop power allocation scheme with the derived conditions, a Stackelberg game is formulated, which aims at the utility maximization both of the macrocell user and femtocell users. The distributed power control algorithm is given to reduce the cross-tier interference between the macrocell and femtocell with same channel. At last, admission control algorithm is proposed, aiming to exploit the network resource effectively. Numerical results show that the proposed resource allocation schemes are effective in reducing power consumption and more suitable in the densely deployed scenario of the femtocell networks. Meanwhile, it also presents that the distributed power allocation scheme combined with admission control can protect the performance of all active femtocell users in a robust manner.

A Distributed Power Allocation Scheme for Base Stations Powered by Retailers with Heterogeneous Renewable Energy Sources

Owing to the intermittent power generation of renewable energy sources (RESs), future wireless cellular networks are required to reliably aggregate...

Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints

This work proposes a distributed power allocation scheme for maximizing energy efficiency in the uplink of OFDMA-based HetNets where a macro-tier is augmented with small cell access points. Each user equipment (UE) in the network is modeled as a rational agent that engages in a non-cooperative game and allocates its available transmit power over the set of assigned subcarriers to maximize its individual utility (defined as the user's throughput per Watt of transmit power) subject to a target rate requirement. In this framework, the relevant solution concept is that of Debreu equilibrium, a generalization of the concept of Nash equilibrium. Using techniques from fractional programming, we provide a characterization of equilibrial power allocation profiles. In particular, Debreu equilibria are found to be the fixed points of a water-filling best response operator whose water level is a function of rate constraints and circuit power. Moreover, we also describe a set of sufficient conditions for the existence and uniqueness of Debreu equilibria exploiting the contraction properties of the best response operator. This analysis provides the necessary tools to derive a power allocation scheme that steers the network to equilibrium in an iterative and distributed manner without the need for any centralized processing. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters.

Distributed power allocation with a novel signaling in dense OFDMA small cell networks

A distributed power allocation scheme was presented to maximize the system capacity in dense small cell networks. A new signaling called inter-cell-signal to interference plus noise ratio (ISINR) as well as its modification was defined to show the algebraic properties of the system capacity. With the help of ISINR, we have an easy way to identify the local monotonicity of the system capacity. Then on each subchannel in iteration, we divide the small cell evolved node B's (SeNBs) into different subsets. For the first subset, the sum rate is convex with respect to the power domain and the power optimally was allocated. On the other hand, for the second subset, the sum rate is monotone decreasing and the SeNBs would abandon the subchannel in this iteration. The two strategies are applied iteratively to improve the system capacity. Simulations show that the proposed scheme can achieve much larger system capacity than the conventional ones. The scheme can achieve a promising tradeoff between performance and signaling overhead.

A distributed power allocation scheme in green cognitive radio ad hoc networks

For the realization of green communications in cognitive radio ad hoc networks (CRAHNs), self- adaptive and efficient power allocation for secondary users (SUs) is essential. With the distributed and time- varying network topology, it needs to consider how to optimize the throughput and power consuming, avoid the interference to primary users (PUs) and other SUs, and pay attention to the convergence and fairness of the algorithm. In this study, this problem is modeled as a constraint optimization problem. Each SU would adjust its power and corresponding strategy with the goal of maximizing its throughput. By studying the interactions between SUs in power allocation and strategy selection, we introduce best-response dynamics game theory and prove the existence of Nash equilibrium (NE) point for performance analysis. We further design a fully distributed algorithm to make the SUs formulate their strategy based on their utility functions, the strategy and number of neighbors in local area. Compared with the water-filling (WF) algorithm, the proposed scheme can significantly increase convergent speed and average throughput, and decrease the power consuming of SUs.

Adaptive multiobjective optimisation for energy efficient interference coordination in multicell networks

In this paper, the authors investigate the distributed power allocation for the multicell orthogonal frequency division multiple access networks by taking both the energy efficiency and the intercell interference (ICI) mitigation into account. A performance metric termed as throughput contribution is exploited to measure how the ICI is effectively coordinated. To achieve a distributed power allocation scheme for each base station (BS), the throughput contribution of each BS to the network is first given based on a pricing mechanism. Different from the existing works, a biobjective problem is formulated based on the multiobjective optimisation theory, which aims at maximising the throughput contribution of the BS to the network and minimising its total power consumption at the same time. By using the method of the Pascoletti and Serafini scalarisation, the relationship between the varying parameters and the minimal solutions is revealed. Furthermore, to exploit the relationship an algorithm is proposed based on which all the solutions on the boundary of the efficient set can be achieved by adaptively adjusting the involved parameters. With the obtained solution set, the decision maker has more choices in the power allocation schemes in terms of both the energy consumption and the throughput. Finally, the performance of the algorithm is assessed by the simulation results.

Robust power allocation in two-tier heterogeneous networks

This paper focuses on a robust distributed power allocation scheme for downlink two-tier heterogeneous networks (HetNets). The objective is to maximize the network aggregate utility of femtocell users (FUEs) which factors the fairness between users under the constraint of not causing serious interference to existing macrocell users (MUEs). Being impractical for different nodes to cooperate in HetNets to obtain precise estimated values of the channel gains between them, it is a challenge to guarantee the performance of the power allocation algorithm by using existing methods. This work makes a step forward in the direction of conquering this challenge by taking into account channel uncertainty, and the robust counterpart of nominal problem, without channel uncertainty, is framed by using worst-case robust optimization theory. To make the robust counterpart computationally tractable, we exploit its convexity and derive that the channel uncertainties between a FUE and nearby femtocell base stations (FBSs) fall into water-filling form being related to the received power from interference sources. Based on the inherent relationship between channel uncertainty and received power, we design a distributed algorithm which merely needs to solve a deterministic problem. The algorithm is devised based on alternating direction method of multipliers (ADMM) with a fast convergence speed. The simulation results demonstrate the robustness of our proposed approach, and the corresponding cost of robustness is investigated.

Analysis of distributed power control under constant and time-varying delays

This work studies the distributed power control algorithm proposed in 1993 by Foschini-Miljanic, standardised for universal mobile telecommunication systems. Continuous and discrete time versions of this algorithm are analysed. First, the stability of the distributed power allocation schemes was studied, where sufficient conditions to guarantee stability and convergence to a desired quality of service were provided. In this study, the channel gains are assumed to be slowly time-varying or piece-wise constant. For closed-loop control, a proportional controller is then employed under integral action in order to achieve good tracking despite time-varying and unknown channel gains. Next, the effects of constant and time-varying time delays in the closed-loop structure are studied. Explicit stability regions for the control gains in the Foschini-Miljanic scheme are derived for both the continuous and discrete-time versions of the algorithm, under constant and time-varying delays. For time-varying scenario, the resulting stability regions do not impose limitations on the rate change of the time-varying profiles. A comprehensive evaluation using simulations is performed to validate the analytical derivations described in the paper.

A novel distributed power allocation scheme for coordinated multicell systems

Coordination between base stations (BSs) is a promising solution for cellular wireless systems to mitigate intercell interference, improving system fairness, and increasing capacity in the years to come. The aim of this manuscript is to propose a new distributed power allocation scheme for the downlink of distributed precoded multicell MISO-OFDM systems. By treating the multicell system as a superposition of single cell systems we define the average virtual bit error rate (BER) of one single-cell system, allowing us to compute the power allocation in a distributed manner at each BS. The precoders are designed in two phases: first the precoder vectors are computed in a distributed manner at each BS considering two criteria, distributed zero-forcing and virtual signal-to-interference noise ratio; then the system is optimized through distributed power allocation with per-BS power constraint. The proposed power allocation scheme minimizes the average virtual BER over all user terminals and the available subcarriers. Both the precoder vectors and the power allocation are computed by assuming that the BSs have only knowledge of local channel state information. The performance of the proposed scheme is compared against other power allocation schemes that have recently been proposed for precoded multicell systems based on LTE specifications. The results also show that although our power allocation scheme is based on the minimization of the virtual uncoded BER, it also has significant gains in coded systems.

Finite Horizon Adaptive Optimal Distributed Power Allocation for Enhanced Cognitive Radio Network in the Presence of Channel Uncertainties

In this paper, novel enhanced Cognitive Radio Network is considered by using power control where secondary users are allowed to use wireless resources of the primary users when primary users are deactivated, but also allow secondary users to coexist with primary users while primary users are activated by managing interference caused from secondary users to primary users. Therefore, a novel finite horizon adaptive optimal distributed power allocation scheme is proposed by incorporating the effect of channel uncertainties for enhanced cognitive radio network in the presence of wireless channel uncertainties under two cases. In Case 1, proposed scheme can force the Signal-to-interference (SIR) of the secondary users to converge to a higher target value for increasing network throughput when primary users' are not communicating within finite horizon. Once primary users are activated as in the Case 2, proposed scheme can not only force the SIR of primary users to converge to a higher target SIR, but also force the SIR of secondary users to converge to a lower value for regulating their interference to Pus during finite time period. In order to mitigate the attenuation of SIR due to channel uncertainties the proposed novel finite horizon adaptive optimal distributed power allocation allows the SIR of both primary users' and secondary users' to converge to a desired target SIR while minimizing the energy consumption within finite horizon. Simulation results illustrate that this novel finite horizon adaptive optimal distributed power allocation scheme can converge much faster and cost less energy than others by adapting to the channel variations optimally.

A Distributed Power Allocation Scheme for Sum-Rate Maximization on Cognitive GMACs

This paper considers a distributed power allocation scheme for sum-rate-maximization under cognitive Gaussian multiple access channels (GMACs), where primary users and secondary users may communicate under mutual interference with the Gaussian noise. Formulating the problem as a standard nonconvex quadratically constrained quadratic problem (QCQP) provides a simple distributed method to find a solution using iterative Jacobian method instead of using centralized schemes. A totally asynchronous distributed power allocation for sum-rate maximization on cognitive GMACs is suggested. Simulation results show that this distributed algorithm for power allocation converges to a fixed point and the solution achieves almost the same performance as the exhaustive search.

Perturbation analysis for spectrum sharing in cognitive radio networks

A primary ad-hoc network working in parallel with a secondary ad-hoc network is considered. The main challenge in operating cognitive ad-hoc networks is the lack of a centralized controller performing resource allocation for different users in the network. In this paper, a distributed power allocation scheme is considered for secondary users and its performance is analyzed when time average channel gains are substituted for instantaneous channel gains. In this way, it is not necessary to exchange instantaneous channel information; however, users' allocated power will be perturbed. It is of interest to analyze mathematically this perturbation and to show how it affects the network performance. In particular, an upper bound on perturbation of each user's allocated power, rate, and interference caused to a primary receivers by the secondary users is obtained. Then, it is shown that how this perturbation affects the transmission rate and the probability of interference constraint violation by the secondary users.