Distributed Power Control Algorithm Research Articles

Resource allocation-aware efficient interference management technique for ultra-dense Femto environment

Addressing the challenge of severe inter-layer and intra-layer interference in densely deployed Femtocells, we introduce an efficient resource allocation strategy centered on clustering. Initially, we employ the Fractional Frequency Reuse (FFR) technique to mitigate interference by segmenting cells into distinct zones. Subsequently, leveraging graph theory and convex optimization, we cluster the Femto Base Stations (FBS) to enhance efficiency. An algorithm prioritizing user fairness governs sub-channel allocation among FBSs. Additionally, a distributed power control algorithm dynamically adjusts FBS power levels, further enhancing system throughput. Comparative analysis reveals that our approach surpasses traditional non-clustered methods and three other comparison methods, exhibiting superior system throughput and Signal-to-Interference Ratio (SINR). Notably, our method significantly enhances user fairness, promoting greater satisfaction among Femto User Equipment (FUE) users.

Mean Field Game-Theoretic Framework for Distributed Power Control in Hybrid NOMA

The steady expansion of the number of wireless devices and the ubiquity of the networks give rise to various interesting challenges for the future sixth generation (6G) of wireless communication systems. Particularly, the operators have to handle massive connectivity among Machine Type Devices (MTDs) and increasing demand for eMMB through limited spectrum resources. Non-Orthogonal Multiple Access (NOMA) has been spotlighted as an emerging technology to meet the above-mentioned challenges. In this paper, we consider a densely deployed network in which users are divided into NOMA coalitions. Firstly, we model the power allocation problem as a differential game. Then, we extend the formulated game using a Mean Field Game (MFG) theoretic framework by considering the effect of the collective behavior of devices. Furthermore, we derive a distributed power control algorithm that enables the users to appropriately regulate their transmit power according to brief information received from the BS. Indeed, the analysis of the proposed approach is governed by the two fundamental Hamilton- Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Numerical results are presented to analyze the equilibrium behaviors of the proposed power control algorithm and to demonstrate the effectiveness of the formulated MFG compared to existing works in the literature.

Resource allocation for D2D-assisted haptic communications

Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments, e.g., teleoperation and autonomous driving. Considering the strict transmission requirements on reliability and latency, Device-to-Device (D2D) communications is introduced to assist haptic communications. In particular, the teleoperators with poor channel quality are assisted by auxiliaries, and each auxiliary and its corresponding teleoperator constitute a D2D pair. However, the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation, especially facing the sporadic packet arrivals. First, the contention-based access scheme is applied to achieve low-latency transmission, where the resource scheduling latency is omitted and users can directly access available resources. In this context, we derive the reliability index of D2D pairs under the contention-based access scheme, i.e., closed-loop packet error probability. Then, the reliability performance is guaranteed by bidirectional power control, which aims to minimize the sum packet error probability of all D2D pairs. Potential game theory is introduced to solve the problem with low complexity. Accordingly, a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium. Experimental results demonstrate the superiority of the proposed learning algorithm.

A New Power Control Algorithm in MMSE Receiver for D2D Underlying Massive MIMO System

Device to device (D2D) underlying massive MIMO cellular network is a robust deployment which enables network to enhance its throughput. It also improves services and applications for the proximity-based wireless communication. However, an important challenge in such deployment is mutual interference. Interference, in the uplink spectrum, reusing the same resource with cellular user, is caused by D2D users. In this paper, we study a distributed power control (DPC) algorithm, using minimum mean square error (MMSE) filter in receiver, to mitigate the produced interference in this deployment scenario. For the DPC algorithm, employing the coverage probability of D2D links, an optimal power control approach is proposed, which maximizes the spectral efficiency of D2D links. Using this modeling approach, it is possible to derive closed-form analytical expressions for the coverage probabilities and ergodic spectral efficiency, which give insight into how the various network parameters interact and affect the link.‎ Also, the DPC algorithm is modeled by stochastic geometry and receiver filter is designed by estimation theory that a new structure in this robust network is an approach to improve spectral efficiency. Simulation results illustrate enhancing coverage probability performance of D2D links in term of the target (signal to interference ratio) SIR with respect to different receiver filter and other parameters which are existing in D2D links.

Lotka-Volterra distributed power control model for OCDMA systems

Dynamic continuous Lotka-Volterra model is used to build two new distributed power control algorithms (DPCA) for optical code division multiple access systems (OCDMA) communication systems, namely DPCA-LV and DPCA-LV* algorithms. The DPCAs features are described, while comparative performance analyses are carried out involving both proposed LV-based DPCAs, the classical Sigmoid DPCA (by Uykan and Uykan), and the Matrix inversion method (MIM) by Tarhuni, including the convergence speed, the normalized mean square error (NMSE), the average consumption of energy per user and complexity measured by the floating-point operations (flops). Under conditions of estimated errors, the proposed DPCA-LV* exhibits minor discrepancy regarding the ideal power vector solution and better convergence, considering both fixed and adaptive factor convergence regarding the DPCA-LV, the conventional Sigmoid DPCA, and MIM approach. The superiority of the DPCA-LV* in terms of performance-complexity tradeoff is more remarkable under high dimension optical networks.

Distributed Dual Optimization for the Uplink of Multi-Cell NOMA

This paper studies distributed power control for the uplink of multi-cell non-orthogonal multiple access (NOMA) systems. Within a cell, the transmissions of different users are modeled as a Gaussian multiple access channel, treating inter-cell interference as additive Gaussian noise. By analyzing the geometry of the feasible power region, using successive interference cancellation at each base station is proved to be optimal in minimizing the total transmission power of all users under rate constraints. The decoding order at each base station, however, remains to be determined. If the decoding order is decided without base station cooperation, the overall control algorithm is fully distributed but is suboptimal in general. To achieve optimality, a partially distributed algorithm is designed, which requires base stations to exchange control messages. Given any feasible instance, the algorithm is proved to converge to the optimal solution. The performance of the fully distributed and partially distributed power control algorithms is compared by computer simulations. The fully distributed algorithm is nearly optimal in terms of outage probability. When a high data rate is required, the partially distributed algorithm is able to reduce the total power consumption by about 20%.

A Distributed Power Control Algorithm for Energy Efficiency Maximization in Wireless Cellular Networks

In this paper, we propose a distributed power control algorithm for addressing the global energy efficiency (GEE) maximization problem subject to satisfying a minimum target SINR for all user equipments (UEs) in wireless cellular networks. We state the problem as a multi-objective optimization problem which targets minimizing total power consumption and maximizing total throughput, simultaneously, while a minimum target SINR is guaranteed for all UEs. We propose an iterative scheme executed in the UEs to control their transmit power using individual channel state information (CSI) such that the GEE is maximized in a distributed manner. We prove the convergence of the proposed iterative algorithm to its corresponding unique fixed point also shown by our numerical results. Additionally, simulation results demonstrate that our proposed scheme outperforms other algorithms in the literature and performs like the centralized algorithm executed in the base station and maximizes the GEE using the global CSI.

Application of deep neural network and deep reinforcement learning in wireless communication.

ObjectiveTo explore the application of deep neural networks (DNNs) and deep reinforcement learning (DRL) in wireless communication and accelerate the development of the wireless communication industry.MethodThis study proposes a simple cognitive radio scenario consisting of only one primary user and one secondary user. The secondary user attempts to share spectrum resources with the primary user. An intelligent power algorithm model based on DNNs and DRL is constructed. Then, the MATLAB platform is utilized to simulate the model.ResultsIn the performance analysis of the algorithm model under different strategies, it is found that the second power control strategy is more conservative than the first. In the loss function, the second power control strategy has experienced more iterations than the first. In terms of success rate, the second power control strategy has more iterations than the first. In the average number of transmissions, they show the same changing trend, but the success rate can reach 1. In comparison with the traditional distributed clustering and power control (DCPC) algorithm, it is obvious that the convergence rate of the algorithm in this research is higher. The proposed DQN algorithm based on DRL only needs several steps to achieve convergence, which verifies its effectiveness.ConclusionBy applying DNNs and DRL to model algorithms constructed in wireless scenarios, the success rate is higher and the convergence rate is faster, which can provide experimental basis for the improvement of later wireless communication networks.

NOMA-Based D2D-Enabled Traffic Offloading for 5G and Beyond Networks Employing Licensed and Unlicensed Access

As the versatile applications emerge, traffic offloading is an urgent issue to improve the performance for the fifth generation (5G) and beyond networks. We focus on the scenario where a device is enabled to transmit to more than one device simultaneously. The device-to-device (D2D) enabled traffic offloading scheme is studied by employing non-orthogonal multiple access (NOMA) and unlicensed access technologies. Our target is to maximize the capacity of the D2D network by optimizing subchannel assignment and power control while guaranteeing the capacity of NOMA-based cellular links and the WiFi system. The formulated problem is a non-convex mixed integer programming problem, which is hard to solve within a rational time. The problem is decomposed into subchannel assignment and power control subproblems. A matching based licensed subchannel allocation algorithm and an unlicensed subchannel access mechanism are proposed. Furthermore, we propose a centralized power control algorithm and a distributed power control algorithm based on global and local information, respectively. Besides, the unlicensed resource management scheme based on Stackelberg game is proposed to achieve the near-optimal utility of both D2D links and the WiFi system. The simulations illustrate that the proposed scheme can increase the throughput of D2D networks efficiently compared with other works.

Multi-sink distributed power control algorithm for Cyber-physical-systems in coal mine tunnels

A coal mine tunnel usually has a long-strip shape. Traditional single-sink cyber-physical-systems (CPS) cannot ensure that sensor nodes far from the entrance of a coal mine tunnel promptly and accurately transmit abnormal data from deep in the tunnel to the ground control center via the sink at the tunnel entrance. At the same time, it causes the “hot spot” problem easily around the Sink and limits the lifetime of the whole network. In order to solve these problems, this paper proposes a Multi-sink distributed power control algorithm (MSDPC-SRMS) suitable for a coal mine tunnel. This algorithm uses the Multiple Sink network structure and the idea of non-uniform cluster, combines the Multi-sink network with the clustering Voronoi scoping routing algorithm. It allocates the optimal transmission range and power for each sink, uses each sink as the cluster head and the network clustering is performed. The network topology is optimized on the basis of a good network coverage rate. Simulation results compared with CNP strategy show that the new algorithm exhibits superior connectivity, power consumption validity, clustering interference, and network performance, can effectively reduce the overall power consumption and prolong the lifetime of the network, thus ensure the monitoring data transmitted to the monitoring center rapidly and quickly.

Adaptive PID Scheme for OCDMA Next Generation PON Based on Heuristic Swarm Optimization

In this paper, an adaptive proportional–integral-derivative (PID) distributed power control algorithm (DPCA) for next generation passive optical networks (NG-PON) is investigated. The adaptive tuning procedures aiming to calibrate the PID gains (proportional, integral, and derivative) based on heuristic particle swarm optimization (PSO) is deployed to design the PID block control. For the DPCA-PID-PSO, important aspects of PID gains, including tuning procedure, algorithm convergence, algorithm performance under time-delay, and channel error estimation, as well as the computational complexity are investigated to evaluate the effectiveness and efficiency of the proposed scheme. Our numerical results have revealed that the proposed DPCA-PID-PSO scheme is effective and consistently robust to solve the power allocation problem with adequate velocity of the convergence and the quality of the solutions for different cost functions considering the time-delay and channel error estimation deviations.

Optimized Power Control Scheme for Global Throughput of Cognitive Satellite-Terrestrial Networks Based on Non-Cooperative Game

In this paper, we investigate the power control problem for spectrum sharing in the cognitive satellite-terrestrial networks (CSTNs), aiming to maximize the throughput of global networks while meeting the requirements of signal to interference plus noise ratio (SINR) in the satellite links. Since the power control problem for global networks involves a mass of information collection or exchange, it is not conducive to centralized control in CSTNs. So we solve it by constructing a non-cooperative game with limited information exchange. First, we theoretically prove that the Nash equilibrium (NE) of the proposed game is consistent with the stationary point of the global throughput maximization problem. Then, we design a distributed power control (DPC) algorithm to obtain the NE of the game with guaranteeing the SINR demand of the satellite links. Considering that the precise channel state information (CSI) is often difficult to be obtained in actual communication scenarios, we propose a modified scheme of the DPC algorithm for the case of imperfect CSI. The modified scheme not only ensures that the SINR requirements of the satellite links can be met but also approximately converges to an NE of the problem with a little performance loss. Finally, the numerical results demonstrate that our scheme outperforms the existing typical algorithms in terms of the throughput of the global networks as well as the number of iterations.

Distributed power control in mobile wireless sensor networks

Controlling the transmission power level in a mobile wireless sensor network is an essential process to improve the energy efficiency and quality of service (QoS) at each wireless node. To achieve this goal, we propose to evaluate the QoS by an estimation of the signal-to-interference noise ratio (SINR) according to the IEEE 802.15.4 standard. For this purpose, the received signal strength indicator per packet (RSSIp), and general received signal strength indicator (RSSIg) metrics are considered in this research work followed by a filtering stage to reduce the variability in the SINR estimation. In addition, this research work aims to evaluate different dynamic power control algorithms through an experimental test-bed, where the wireless nodes are in motion. A comparative analysis is presented of five distributed power control algorithms: fixed-step, Foschini-Miljanic, proportional-integral-derivative control, variable structure control, and water-filling control. Moreover, a new distributed power control technique is proposed for mobile wireless sensor networks: modified fixed-step (MFS). All the power allocation algorithms use the tracking error between the estimated and objective SINRs as the driving mechanism to adjust the transmission power. In general, the results show that a dynamic power allocation scheme not only enables the efficient use of the battery, extending its lifetime, but also it is able to achieve the desired QoS despite multiple interference and mobility of the nodes in the network. Furthermore, the results of the experimental evaluation indicate that the modified fixed-step algorithm has the best performance as a function of the reference tracking of the QoS and power consumption.

Efficient joint power and admission control in underlay cognitive networks using Benders’ decomposition method

In this paper, the joint power and admission control (JPAC) problem in cognitive radio networks is studied. This problem is decomposed into two subproblems, by utilizing the Benders’ decomposition theory, which are efficiently addressed. Specifically, the former is a simple linear optimization problem which a closed-form expression for its optimal solution is derived, and the latter is solved via an iterative distributed power control algorithm. Furthermore, we use the outcomes of the decomposed two subproblems to propose a sequential searching JPAC algorithm with a novel removal metric whereas minimal number of SUs are sequentially removed. In an infeasible system, where the minimum target signal-to-interference-plus-noise-ratios (SINRs) of all primary and secondary users are not simultaneously reachable, our proposed JPAC algorithm guarantees protecting all primary users while the maximal number of SUs are admitted and supported with their target SINRs. Not only our proposed algorithm does converge to an equilibrium, but also outperforms existing algorithms in terms of average outage ratio and average aggregate power, as demonstrated through the extensive simulations.

An approach of robust power control for cognitive radio networks based on chance constraints

The changing and fluctuations of channel gains are inevitable in wireless communication. In this paper, a robust power control scheme for cognitive radio networks is proposed with consideration of uncertain channel gains. With the uncertainty, an optimal power control problem is formulated, which keeps the outage probability both of cognitive and primary users below the given threshold and maximizes the sum-utility of cognitive users. The chance-constraint robust approach is applied to transform the uncertain parameters into the determining setting, which is convexity of the outage probability constraints. In order to make the optimization problem solve facilely, the optimization problem is transformed to a convex problem by a suitable relaxation and the exponential transformations. The distributed power control algorithm based on Lagrange dual decomposition is proposed further. Numerical results show the convergence and effectiveness of the proposed chance-constraint robust power control algorithm. The sum of utility is improved and the energy consumption is reduced compared with some existing algorithms.

An enhanced distributed power control algorithm for mobile femtocells under limited dynamic range and its convergence

This paper presents a distributed power control algorithm for wireless backhaul links of mobile femtocells by using the pilot’s information. Taking into account the limited dynamic range of transmitted powers, the SINR balancing of mobile (vehicular) femto base stations in their home macro base station and the load balancing among the macrocells are achieved by the proposed approach at the cost of exchanging some limited information among both macro and vehicular femto base stations. The algorithm is very beneficial especially in a high load heterogeneous network. Monte Carlo simulation results denote that the mobile femtocells can be uniformly assigned to the macrocells and the SINR balancing is achievable via the proposed scheme.

Distributed Power Control Algorithm in Orthogonal Frequency Division Multiple Cognitive Radio Systems for Fading Channel

In order to guarantee the quality of service (QoS) for primary users (PUs) and secondary users (SUs) in fading channel, a distributed power control algorithm is proposed based on convex optimization theory in underlay OFDM cognitive radio networks (CRNs). Our purpose obtains the maximum transmit data rate of each SU at all subcarriers under three constraints of the maximum allowable transmission power, the minimum signal to interference plus ratio (SINR) of each SU and the maximum allowable interference generated from SUs to PU at each subcarrier. Simulation results show that the performance of the proposed algorithms (m2) are superior to the geometric programming algorithm (m1) in fading channel environment.

Power Allocation for Downlink NOMA Heterogeneous Networks

Non-orthogonal multiple access (NOMA) and heterogeneous network (HetNet) are two significant and promising enabling techniques to further improve overall system performance for next-generation mobile communication systems. In this paper, we develop a novel NOMA HetNet through applying NOMA technique to both macrocell and small-cell of conventional HetNet, which improves the spectral efficiency whereas results in a more complex interference environment. To tackle this complicated interference problem and maximize the overall throughput of this NOMA HetNet, meanwhile ensure the desired quality of service (QoS) of each user, we mathematically formulate a power allocation problem which proves to be an NP-hard problem. Then, to deal with this optimization problem, we propose a users scheduling scheme and an iterative distributed power control algorithm. The simulation results demonstrate that compared with the conventional orthogonal multiple access (OMA) HetNet systems and single-tier NOMA networks, the combination of OMA technique and HetNet with the proposed algorithm can greatly improve the system performance in terms of spectral efficiency and outage performance.

On the Stability of Distributed Power Control Algorithms Under Imperfect Estimation of Channel and Interference

The distributed power control algorithms available in this paper assume perfect estimation of interference power at the receivers and often, the availability of perfect local channel state information (CSI). However, perfect estimation of interference power or acquisition of channel information is impractical. In this paper, we consider a generic class of distributed power control algorithms and analyze the resulting performance loss when faced with errors in CSI and/or interference level estimation. We approximate such power control algorithms with linearized state space dynamics wherein the errors in the estimation of received interference or the CSI, can be modeled as additive observation noise. Based on the proposed model, we derive lower bounds on the norm of the errors in the transmit power vector and receive signal-to-interference-plus-noise ratio (SINR) vector. Our analysis suggests that when the target SINR vector approaches the Pareto-optimal bound, the errors in the transmit vectors could grow without bound. As a consequence, to maintain a performance loss within an acceptable range, either much longer estimation times or lower target SINRs are required. Our numerical results confirm the validity of our analysis and the validity of the proposed bounds.

Comparative analysis of distributed power control algorithms in CDMA

This paper presents comparative analysis of various algorithms of distributed power control used in Code Division Multiple Access (CDMA) systems. These algorithms include Distributed Balancing power control algorithm (DB), Modified Distributed Balancing power control algorithm (MDB), Fully Distributed Power Control algorithm (FDPC), Distributed Power Control algorithm (DPC), Distributed Constrained Power Control algorithm (DCPC), Unconstrained Second-Order Power Control algorithm (USOPC), Constrained Second-Order Power Control algorithm (CSOPC), and Fixed Step Distributed Power Control algorithm (FSDPC). These algorithms are compared based on the rate of convergence to the target signal-to-interference ratio, the rate of convergence of the power, and the rate of convergence of utilities. Simulation results show that the FDPC is the best choice according to these parameters.