Distributed Power Control Algorithm Research Articles

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.

Leveraging One-Hop Information in Massive MIMO Full-Duplex Wireless Systems

We consider a single-cell massive MIMO full-duplex wireless communication system, where the base-station (BS) is equipped with a large number of antennas. We consider the setup where the single-antenna mobile users operate in half- duplex, while each antenna at the BS is capable of full-duplex transmissions, i.e., it can transmit and receive simultaneously using the same frequency spectrum. The fundamental challenge in this system is intra-cell inter-node interference, generated by the transmissions of uplink users to the receptions at the downlink users. The key operational challenge is estimating and aggregating inter-mobile channel estimates, which can potentially overwhelm any gains from full-duplex operation. In this work, we propose a scalable and distributed scheme to optimally manage the inter-node interference by utilizing a "one- hop information architecture". In this architecture, the BS only needs to know the signal-to-interference-plus-noise ratio (SINR) from the downlink users. Each uplink user needs its own SINR, along with a weighted signal-plus-noise metric from its one-hop neighboring downlink users, which are the downlink users that it interferes with. The proposed one-hop information architecture does not require any network devices to comprehensively gather the vast inter-node interference channel knowledge, and hence significantly reduces the overhead. Based on the one-hop information architecture, we design a distributed power control algorithm and implement such architecture using overheard feedback information. We show that, in typical asymptotic regimes with many users and antennas, the proposed distributed power control scheme improves the overall network utility and reduces the transmission power of the uplink users.

Energy-Efficiency-Based Resource Allocation Framework for Cognitive Radio Networks With FBMC/OFDM

In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users' energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes.

Power Control for D2D Underlay Cellular Networks With Channel Uncertainty

Device-to-device (D2D) communications underlying the cellular infrastructure are a technology that have been proposed recently as a promising solution to enhance cellular network capabilities. It improves spectrum utilization, overall throughput, and energy efficiency while enabling new peer-to-peer and location-based applications and services. However, interference is the major challenge, since the same resources are shared by both systems. Therefore, interference management techniques are required to keep the interference under control. In this paper, in order to mitigate interference, we consider centralized and distributed power control algorithms in a one-cell random network model. Existing results on D2D underlay networks assume perfect channel state information (CSI). This assumption is usually unrealistic in practice due to the dynamic nature of wireless channels. Thus, it is of great interest to study and evaluate achievable performances under channel uncertainty. Differently from previous works, we are assuming that the CSI may be imperfect and include estimation errors. In the centralized approach, we derive the optimal powers that maximize the coverage probability and the rate of the cellular user while scheduling as many D2D links as possible. These powers are computed at the base station (BS) and then delivered to the users, and hence the name “centralized”. For the distributed method, the ON–OFF power control and the truncated channel inversion are proposed. Expressions of coverage probabilities are established in the function of D2D links intensity, pathloss exponent, and estimation error variance. Results show the important influence of CSI error on achievable performances and thus how crucial it is to consider it while designing networks and evaluating performances.

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 Robust Distributed Power Control Algorithm for Minimum Interference to Primary Users in Underlay Cognitive Radio Networks

A robust distributed optimal power control (RDPC) scheme under worst case condition is proposed to make primary users (PUs) receive minimum interference generated from all secondary users (SUs) in underlay cognitive radio networks (CRNs). The strategy considers the transmit power of each SU below the maximum allowable power of the devices and interference plus noise ratio (SINR) of each SU under the minimum threshold. Simulation illustrate thatthe RDPC can lead SUs to reduce the interference to PUs, and simultaneously the better meet quality of service (QoS) requirement of SUs in comparison with the distributed power control algorithm (DPC) and the traditional iterative water filling algorithm (IWFA) in time-varying channel environment.

On distributed power control in full duplex wireless networks

In this paper, we first consider the problem of distributed power control in a Full Duplex (FD) wireless network consisting of multiple pairs of nodes, within which each node needs to communicate with its corresponding node. We aim to find the optimal transmition power for the FD transmitters such that the network-wide capacity is maximized. Based on the high Signal-to-Interference-Plus-Noise Ratio (SINR) approximation and a more general approximation method for logarithm functions, we develop effective distributed power control algorithms with the dual decomposition approach. We also extend the work to the general FD network scenario, which can be decomposed into subproblems of isolated nodes, paths, and cycles. The corresponding power control problem is then be solved with the distributed algorithm. The proposed algorithms are validated with simulation studies.

Performance analysis of a distributed power control algorithm for shared and split spectrum femtocell networks

In this paper, we study the performance of a distributed power adjustment algorithm for shared and split spectrum allocation setups. The theoretical analysis reveals that the convergence of the power control algorithm is guaranteed under different spectrum allocation schemes and the convergence rate is exponential. The performance analysis is also carried out via simulations which demonstrate the algorithm fairness under Jain’s and Atkinkons’ fairness indices.

Uplink Power Allocation for Distributed Interference Cancellation With Channel Estimation Error

A distributed interference cancellation scheme for cellular networks is considered, in which a subset of receivers forward their decoded messages to another subset of receivers. The messages can then be used to cancel interference with estimated cross-channel gains. A distributed power control algorithm is presented that harnesses the gain of interference cancellation and that considers channel estimation error. The algorithm is based on the exchange of interference prices, but in this case, the local power updates are not concave due to nonlinear terms introduced by channel estimation errors. A partial cancellation scheme is then presented, where the power updates are derived in closed form. We show that the algorithm converges to a local optimum of the weighted sum-rate maximization problem. Two methods for sorting users into the two groups that, respectively, forward and receive the decoded messages are presented. The first is sequential and is shown to converge to a local optimum. The second is a simple heuristic that is based on a stochastic geometric approach. Numerical results show that power control and adaptive partitioning of the users can add substantial gains to interference cancellation.

Power Allocation Scheme for OCDMA NG-PON With Proportional–Integral–Derivative Algorithms

Distributed power control algorithms (DPCAs) based on the Foschini/Miljanic (FM) and Verhulst (V) models and a proportional–integral–derivative (PID) algorithm have been investigated in this work for next-generation passive optical networks (NG-PONs). The DPCAs of FM and V that are represented by the classical control theory as an integral control present limitations in the accuracy of signal-to-noise-plus-interference ratio (SNIR) estimates in weak-signal environments. Furthermore, in this work the development of DPCA-PID-FM and DPCA-PID-V based on PID schemes originated as DPCA-FM and DPCA-V, respectively, has been conducted to overcome the SNIR estimation limitation. The main results have shown that DPCA-FM reaches convergence with a lower number of iterations than DPCA-V for situations of a good SNIR. However, under weak-signal scenarios, DPCA-V exhibits a smaller discrepancy from the optimum power vector solution and better convergence than DPCA-FM. Also, the proportional and differential actions taken in DPCA-V to originate DPCA-PID-V do not present a noticeable impact on the tendency and the velocity of the convergence compared with DPCA-V. On the other hand, DPCA-PID-FM changes the trend in convergence from DPCA-FM. Finally, the increase in channel error estimation causes an error of convergence for all DPCAs studied in this work. The main advantage of DPCAs based on PID is the possibility of utilizing adaptive tuning procedures to tune the PID gains.

An optimal power control algorithm for STDMA MAC protocols in multihop wireless networks

Multihop spatial time division multiple access (STDMA) medium access control (MAC) protocols constitute an important building block of wireless networks. There are not many practical power control algorithms that can optimally tradeoff power consumption against transmission rates with a reasonable computational complexity. In this paper, we introduce an energy-efficient distributed power control algorithm for STDMA MAC protocols. The motivation for this study is two fold, namely, maximizing the spatial reuse of the system resources and maximizing power efficiency. We develop a mathematical formulation for maximizing spatial reuse and power efficiency under discrete SINR and rate constrains. After proving that power is a convex function of data rates in our problem, we demonstrate that our problem in simultaneous transmission environments can be reduced to a linear programming (LP) problem. Then, we solve this LP problem using dynamic programming (DP). Finally, based on our proposed solution, we propose a low complexity optimal power control (OPC) algorithm which can be generically embedded within any existing STDMA MAC protocol. Through analytical and experimental studies, we show that our power control algorithm cannot only significantly improve the throughput, power consumption, and delay performance of STDMA MAC protocols compared to their baseline alternatives, but also outperform existing STDMA algorithms.

Delay and estimation uncertainty in distributed power control algorithm for optical CDMA networks

In this work, the performance of a distributed power control algorithm (DPCA) considering time-delay and estimation uncertainty has been investigated. Control theory concepts have been deployed aiming to derive the Foschini–Miljanic DPCA as well as to analyse their stability properties under time-varying delays and estimation uncertainty applicable to optical networks. Although delays introduce transient response for initial iterations, our numerical results have shown a suitable convergence of the transmitted power obtained with the DPCA under strong delay conditions. Furthermore, the DPCA was able to converge also under high level of uncertainty on the estimated SNIR, despite the solution quality decreasing.

다이나믹 펨토셀 네트워크에서 커버리지와 데이터 부하 균형을 고려한 기지국의 파워 조절 분산 알고리즘

셀룰러 시스템을 기반으로 하는 펨토셀 네트워크는 실내 환경에서 대용량 데이터 전송을 위한 기술로 주목 받고 있다. 본 연구에서는 다수의 펨토 기지국을 배치하여 서비스를 제공하는 실내 환경에서 커버리지 문제와 데이터 전송 부하 문제를 동시에 고려하는 기지국의 파워 조절 분산 방법을 제안한다. 사용자의 데이터 사용량은 시간과 장소에 따라 지속적으로 변화하기 때문에 기지국의 데이터 전송 부하를 효과적으로 분배하는 동적 파워 조절 방법이 요구된다. 또한, 대규모 시설에 설치되어 있는 펨토셀 네트워크 시스템을 효율적으로 운영하기 위해서는 기지국의 파워 조절을 위한 분산 알고리즘이 필요하다. 본 논문에서는 기지국 사이의 데이터 전송 부하를 효과적으로 분배하는 분산 알고리즘을 제시한다. 또한, 일부 기지국에서 발행한 장애를 대응하는 방법과 새로운 기지국을 추가하는 방법을 제시한다. 모의 실험을 통해서 제안한 알고리즘의 성능을 시험하였다.

Distributed Energy-Efficient Power Control for Macro–Femto Networks

Femtocell deployment provides a cost- and energy-saving solution for cellular operators. Although energy efficiency is imperative for green communications, most existing works on femtocells focus on interference mitigation and capacity maximization rather than on energy efficiency. In this paper, we propose two energy-efficient power control (EEPC) schemes for downlink transmissions in multichannel macro-femto networks, namely, gradient-based distributed power control (GDPC) and energy-efficient game-based power control (EGPC). In particular, GDPC maximizes energy efficiency by macro base stations (BS) or femto access points (FAPs) operating in a semi-autonomous mode, with partial derivatives and system power information exchanged periodically between neighboring macro BSs or FAPs. To make the power control completely distributed without inter-BS communications, EGPC is further proposed based on game theory, in which each BS updates its power-allocation strategy to maximize its utility. We theoretically prove the existence and uniqueness of equilibrium of the power control game in EGPC. Simulations are carried out based on realistic channel conditions to verify the analytical results and validate the effectiveness of GDPC and EGPC. It is shown that both GDPC and EGPC can effectively improve the system energy efficiency compared with average power allocation (APA) and another two power control schemes (i.e., a distributed power control algorithm and a noncooperative game) and that EGPC can converge to the equilibrium with better performance.

An Efficient Distributed Power Control in Cognitive Radio Networks

In cognitive radio networks, when primary users (PUs) do not use a shared frequency band, secondary users (SUs) are allowed to use that frequency band. However, when SU using frequency band and reclaiming PU also wants to use this band, there are two solutions: SU jumps to another existing spectrum band or stays in the same frequency band and changes its power so that interference in the reclaiming PUs does not exceed a threshold. Since the first solution interrupts the SUs' work, the second solution is more appropriate. Therefore, power control in cognitive networks is absolutely significant which should receive more attention. In the previous work (TPC-CBS) (2), TPC algorithm has been used for power control. The drawback of this algorithm is fixed target SIRs. In other words, while using this algorithm, all the users having enough sources or extra sources reach the same target SIR, which is not desirable. Therefore, in this paper, we are going to solve it by means of the proposed algorithm called An Efficient Distributed Power Control Algorithm for Cognitive Networks (EDPC). By the proposed algorithm, when a user has a bad channel, its SIR sets to the minimum target SIR and, when it has a good channel, its SIR will be more than the minimum value. Moreover, by means of fuzzy logic systems (FLS), the value of interference on reclaiming PU caused by SUs is checked in each iteration and power level of SUs is decreased if this value exceeds a threshold. Simulation results show that using our proposed algorithm not only allows SUs and PUs use frequency band simultaneously, but also enhances throughput significantly in comparison with the previous approaches (TPC-CBS).

Time-delay and estimation uncertainty impact on the heuristic-based power control of optical networks

In this work, the performance of distributed power control algorithm based on heuristic optimization and considering the time-delay and estimation uncertainty is investigated in the scenario of optical networks. Time-delay allied to signal-to-noise-plus-interference ratio estimation uncertainty at the receiver side can affect decisively the network stability and the quality solution of the transmitted power optimization. In this work we have deployed a heuristic method based on particle swarm optimization (PSO) approach to allocate suitable transmitted power for the optical network nodes. Our numerical finding has demonstrated a suitable convergence of the PSO algorithm, as well as its limits, when the delay and uncertainty caused by estimation errors arise. Indeed, the rate-of-converge of the PSO algorithm has degraded substantially when delay and uncertainty become superior to 20 %, indicating that a tolerable uncertainty is about 10–15 %.

Integration of scheduling and network coding in multi-rate wireless mesh networks: Optimization models and algorithms

In order to fully utilize spectrum resource in wireless mesh networks (WMNs), we propose a combination of some popular communication techniques, including link scheduling, spatial reuse, power and rate adaptation and network coding (NC), to activate as many transmission links as possible during one scheduling period, so that the total scheduling length can be minimized and network throughput can be maximized. Different from previous studies, we consider the interplay among these techniques and present an optimal NC-aware link scheduling mechanism in multi-rate WMNs, which relies on the enumeration of all possible schedules. Due to the high computational complexity of our proposed model, we utilize a column generation (CG)-based method to resolve the optimization problem and decompose it into a master problem (MP) and a pricing problem (PP). Furthermore, we present a distributed power control algorithm for PP, by which the computational complexity of the CG-based scheme can be largely reduced. Simulation results demonstrate the superiority of our method under various network situations.

A Game Theory Approach for Efficient Power Control and Interference Management in Two-tier Femtocell Networks based on Local Aain

In the recent years, femtocell technology has received a considerable attention due to the ability to provide an efficient indoor wireless coverage as well as enhanced capacity. However, under the spectrum sharing between femtocell user equipment (FUEs) and the owner of spectrum macrocell user equipment (MUEs), both may experience higher uplink interference to each other. This paper proposes a novel distributed power control algorithm for the interference management in two-tier femtocell networks. Due to the assignment of licensed radio frequency to the outdoor macrocell users, the access priority of MUEs should be higher than FUEs. In addition, the quality of service (QoS) of MUEs that is expressed in the target signal-to-interference-plus-noise ratio (SINR) must always be achieved. On the other hand, we consider an efficient QoS provisioning cost function for the low-tier FUEs. The proposed algorithm requires only local information and converges even in cases where the frontiers of available power serve the target SINRs impossible. The advantage of the algorithm is the ability to implement in a distributed manner. Simulation results show that the proposed algorithm based on our cost function provides effective resource allocation and substantial power saving as compared to the traditional algorithms.

Game Theoretic Distributed Power Control Algorithms for Uplink Wireless Data in Flat Fading Channels

<p>In this paper we present a game-theoretic power control algorithms for wireless data in CDMA cellular systems under two realistic channels: (a1) Fast flat fading channel and (a2) Slow flat fading channel. The fading coefficients under both (a1) and (a2) are studied for three appropriate small scale channel models that are used in the CDMA cellular systems: Rayleigh channel, Rician channel and Nakagami channel. This work is inspired by the results presented by [1] under nonfading channels. In other words, we study the impact of the realistic channel models on the findings in [1] through the followings: we evaluate the average utility function, the average number of bits received correctly at the receiver per one Joule expended, for each channel model. Then, using the average utility function we study the existence, uniqueness of Nash equilibrium (NE) if it exists, and the social desirability of NE in the Pareto sense. Results show that in a non-cooperative game (NPG) the best policy for all users in the cell is to target a fixed signal-to-interference and noise ratio (SINR) similar to what was shown in [1] for non-fading channel. The difference however is that the target SINR in fading channels is much higher than that in a non-fading channel. Also, for spreading gain less than or equal to 100, both NPG and non-cooperative power control game with pricing (NPGP) perform poorly, where all the terminals except the nearest one were not able to attain their corresponding minimum SINR even if sending at the maximum powers in their strategy spaces.</p>

Algoritmo de Controle de Potência Distribuído Fundamentado no Modelo Populacional de Verhulst DOI: 10.14209/jcis.2006.5

In this paper the continuous dynamic model of Verhulst is used. It had been elaborated initially to describe the population growth of biological species with food and physical space restrictions, in order to synthesize a new Distributed Power Control Algorithm (DPCA) applicable in Direct Sequence Code Division Multiple Access (DSCDMA) systems. The discretization of the corresponding differential equation is accomplished via Numeric Integration Euler method (NIE). Analytical convergence conditions for the proposed DPCA were also established. The properties of the proposed recursive algorithm, as Euclidian distance from optimum vector after convergence, convergence speed, Normalized mean Squared Error (NSE), average power consumption per user and implementation complexity are investigated through simulations. The simulation results are confronted with two another DPCA: the classic algorithm derived by Foschini and Miljanic and the sigmoidal of Uykan and Koivo. With estimation errors the proposed DPCA showed smaller discrepancy from the optimum power vector allocation after convergence and better convergence. Additionally, the Gerschgorin Circles theory (GC) is applied for the feasibility of the power allocation problem.