A non-cooperative power control game in delay-constrained multiple-access networks

In this paper, energy efficient power control for small cells underlaying a macro cellular network is investigated. We formulate the power control problem in self-organizing small cell networks as a non-cooperative game, and propose a distributed energy efficient power control scheme, which allows the small base stations (SBSs) to take individual decisions for attaining the Nash equilibrium (NE) with minimum information exchange. Specially, in the non-cooperative power control game, a non-convex optimization problem is formulated for each SBS to maximize their energy efficiency (EE). By exploiting the properties of parameter-free fractional programming and the concept of perspective function, the non-convex optimization problem for each SBS is transformed into an equivalent constrained convex optimization problem. Then, the constrained convex optimization problem is converted into an unconstrained convex optimization problem by exploiting the mixed penalty function method. The inequality constraints are eliminated by introducing the logarithmic barrier functions and the equality constraint is eliminated by introducing the quadratic penalty function. We also theoretically show the existence and the uniqueness of the NE in the non-cooperative power control game. Simulation results show remarkable improvements in terms of EE by using the proposed scheme.

An overview of game-theoretic approaches to energy-efficient resource allocation in wireless networks is presented. Focusing on multiple-access networks, it is demonstrated that game theory can be used as an effective tool to study resource allocation in wireless networks with quality-of-service (QoS) constraints. A family of non-cooperative (distributed) games is presented in which each user seeks to choose a strategy that maximizes its own utility while satisfying its QoS requirements. The utility function considered here measures the number of reliable bits that are transmitted per joule of energy consumed and, hence, is particulary suitable for energy-constrained networks. The actions available to each user in trying to maximize its own utility are at least the choice of the transmit power and, depending on the situation, the user may also be able to choose its transmission rate, modulation, packet size, multiuser receiver, multi-antenna processing algorithm, or carrier allocation strategy. The best-response strategy and Nash equilibrium for each game is presented. Using this game-theoretic framework, the effects of power control, rate control, modulation, temporal and spatial signal processing, carrier allocation strategy and delay QoS constraints on energy efficiency and network capacity are quantified.

Multimedia application integrates a variety of media, namely, audio, video, images, graphics, text, and data, each of which have different quality of service (QOS) requirements. In this paper we evaluate the support of reservation-based high speed protocols including DQDB (distributed queue dual bus), CRMA (cyclic-reservation multiple-access), DQMA (distributed-queue multiple-access), and FDQ (fair distributed queue), for various multimedia traffic including low and medium quality video, high quality MMPEG video, and data traffic. Through extensive experiments, we vary different traffic and protocol parameters including transmission rate, delay and loss constraint, data load and data burstiness, to determine the number of multimedia sessions supported by each of the four protocols. Delay constraints have moderate effect on some of the protocols, but loss constraints have very little effect on any protocol. Bursty data significantly degrade real-time traffic performance, but the negative effect is almost corrected when delay constraint is somewhat relaxed. These results show that the reservation-based protocols are suitable for integrating real-time multimedia traffic with bursty data traffic in very high speed network, especially when delay constraint is somewhat relaxed. Due to its excellent fairness, FDQ clearly stands out to be the best protocol among four to support heterogeneous traffic.

A major challenge in the operation of wireless communications systems is the efficient use of radio resources. One important component of radio resource management is power control, which has been studied extensively in the context of voice communications. With the increasing demand for wireless data services, it is necessary to establish power control algorithms for information sources other than voice. We present a power control solution for wireless data in the analytical setting of a game theoretic framework. In this context, the quality of service (QoS) a wireless terminal receives is referred to as the utility and distributed power control is a noncooperative power control game where users maximize their utility. The outcome of the game results in a Nash (1951) equilibrium that is inefficient. We introduce pricing of transmit powers in order to obtain Pareto improvement of the noncooperative power control game, i.e., to obtain improvements in user utilities relative to the case with no pricing. Specifically, we consider a pricing function that is a linear function of the transmit power. The simplicity of the pricing function allows a distributed implementation where the price can be broadcast by the base station to all the terminals. We see that pricing is especially helpful in a heavily loaded system.

In this paper, the issue of non-cooperative, energy-efficient power control in a relay-assisted interference channel is considered. The energy efficiency of a given terminal is defined as the ratio between the throughput of that terminal and the consumed power. As far as the computation of the consumed power is concerned, not only the transmit power, but also the power dissipated in terminal's electronic circuitry to operate the device is considered. A non-cooperative power control game is devised, which admits a unique Nash equilibrium, and whose best-response-dynamics is guaranteed to converge to the unique equilibrium. A cooperative power control algorithm is also devised, which is used as a benchmark for the non-cooperative game. Finally, numerical results are provided to show the merits of the proposed algorithms.

In cognitive radio network (CRN), the utilities results in Nash equilibrium of power control game without using pricing are inefficient. In this paper, a distributed power control algorithm is proposed to improve the utilities of both primary user (PU) and secondary users (SUs) in the CRN based on game theoretic framework. A distributed power control is a non-cooperative power control game, and the quality of service (QoS) received by PU and SUs terminals are referred to as the utility function. PU and SUs act as decision makers in the game and they maximize their utilities in a distributed fashion. We introduce a new pricing function for SUs as a function of transmit power and square amount of interference in order to guide SUs to an efficient Nash equilibrium point. Analysis of the existence and uniqueness of Nash equilibrium for the proposed power control game with pricing is presented. Simulation results show that the proposed power control algorithm via a new pricing function maximizes the number of SUs access the unused spectrum, and improves the utilities of PU and SUs.

In some application scenarios, several Wireless Body Area Networks (WBANs) coexist in a small area, resulting in very strong inter-network interference, which seriously disturbs intra-network communications. The goal of this paper is to mitigate inter-WBAN interference by non-cooperative power control game. A Power control game is considering both energy efficiency and co-existence interference of WBANs. In this paper, we derive a self-adaptive power control algorithm called Optimal Response Iteration Strategy (ORI), which can reach the Nash Equilibrium with the transmitting power representing the tradeoff between network utility and power consumption. We compare our algorithm with the K-G and PAPU algorithm and show that ORI can reduce the effect of inter-network interference effectively by adaptively selecting the suitable channel and transmission power. In addition, we present that the performance of the system can be improved through the suitable selection of the price factor.

We present a game-theoretic treatment of distributed power control in CDMA wireless systems using outage probabilities. We first prove that the noncooperative power control game considered admits a unique Nash equilibrium (NE) for uniformly strictly convex pricing functions and under some technical assumptions on the SIR threshold levels. We then analyze global convergence of continuous-time as well as discrete-time synchronous and asynchronous iterative power update algorithms to the unique NE of the game. Furthermore, we show that a stochastic version of the discrete-time update scheme, which models the uncertainty due to quantization and estimation errors, converges almost surely to the unique NE point. We finally investigate and demonstrate the convergence and robustness properties of these update schemes through simulation studies.

Energy saving is one of the main themes for the green communication. But the conflict between energy consumption and transmission delay in stochastic wireless networks has become an inevitable problem which must be solved. In this paper, an optimization problem is formulated with the objective to minimize the energy consumption under a transmission delay constraint. We use the theory of stochastic network calculus to investigate the relationship among: the delay constraint, the transmission rate and the energy consumption. Thereafter, we achieve the optimum solution of the optimization problem. In the end, numerical results under different configurations are presented and discussed.

Considering development of delay-sensitive applications in wireless communications and cellular networks along with lack of system resources and environmental factors including fading and noise all together make it impossible for all users to be active and to achieve quality of service requirements simultaneously. To analyze wireless systems performance, user capacity is defined as the maximum number of users that can be activated at the same time. In order to obtain user capacity scaling laws, channel distribution information is required and channel gains can be estimated by different channel estimators such as minimum mean square error estimators. In this paper, estimated channel gains are substituted for true channel gains and effects of imperfect channel estimation errors on the user capacity scaling laws are analyzed. It is shown that the user capacity scales double logarithmically and there is a gap depending on channel estimators accuracy between the lower and upper bounds. Moreover, assuming estimated channels for different users are independent and identically distributed, it is shown that the user capacity scaling laws are asymptotically tight.

This paper deals with online QoS multicast tree construction problem, which is essential to support multimedia services, in multi-channel multi-radio wireless mesh networks. The proposed algorithm satisfies both delay and bandwidth requirements of each arrived session. It breaks down the original problem into two phases of satisfying delay and bandwidth constraints. The first phase devotes to preserving delay constraint of the arrived session, in which a number of delay-bounded paths are determined between each receiver and the source node. In the proposed scheme, to diminish the utilized bandwidth, the overlapping of the regarding paths of the receivers is maximized as much as possible. We propose a mathematical model to satisfy bandwidth requirement in the second phase, which constructs the tree over the selected paths. The data transmission rate on the tree is set to the requested rate by the session, to satisfy its bandwidth requirement. The proposed model has a global view over the network and constructs the most appropriate trees. It also exploits wireless broadcast advantage property to enhance the performance. Through extensive simulations, the effectiveness of the proposed algorithm is verified. In particular, it improves the acceptance rate by 60% compared to previous schemes.

This work considers the relay selection and resource allocation problem (i.e., link scheduling, and rate allocation) for multi-source, multi-relay dual-hop wireless networks. The relays employ buffers to store the received data from the sources for future transmissions. End-to-end (E2E) delay of each traffic flow originated from a source or a relay is constrained in terms of maximum allowable delay-outage probability. To solve this problem, we first study the resource allocation problem to maximize the constant supportable arrival rate of a non-prioritized source under minimum rate requirements of the prioritized sources and relays for a given relay selection solution. Then, the optimal relay selection can be determined to support the largest rate of the non-prioritized source among all possible relay selection solutions. We derive the resource allocation solutions using asymptotic delay analysis and convex optimization techniques. We also develop an online allocation algorithm which does not require the knowledge of the fading statistics by using stochastic approximation theory. Numerical results are presented to demonstrate the usefulness of the proposed resource allocation design for relay selection under different delay and rate constraint regimes.

This paper examines and provides the theoretical evidence of the feasibility of 60 GHz mmWave in wireless body area networks (WBANs), by analyzing its properties. It has been shown that 60 GHz based communication could better fit WBANs compared to traditional 2.4 GHz based communication because of its compact network coverage, miniaturized devices, superior frequency reuse, multi-gigabyte transmission rate and the therapeutic merits for human health. Since allowing coexistence among the WBANs can enhance the efficiency of the mmWave based WBANs, we formulated the coexistence problem as a non-cooperative distributed power control game. This paper proves the existence of Nash equilibrium (NE) and derives the best response move as a solution. The efficiency of the NE is also improved by modifying the utility function and introducing a pair of pricing factors. Our simulation results indicate that the proposed pricing policy significantly improves the efficiency in terms of Pareto optimality and social optimality.

This paper is focused on the cross-layer design problem of joint multiuser detection and power control for energy-efficiency optimization in wireless data networks through a game-theoretic approach. Unlike previous studies in this area, which have considered the application of game theory to achieve energy-efficiency in idealized synchronous code division multiple access systems, more realistic assumptions including system asynchronism, the use of band-limited chip-pulses, and the multipath distortion induced by the wireless channel are explicitly incorporated in the development of this paper. Several noncooperative games are proposed wherein each user may vary its transmit power and uplink receiver in order to maximize its utility, which is defined here as the ratio of data throughput to transmit power. In particular, the case in which a linear multiuser detector is adopted at the receiver is considered first, and then, the more challenging case in which a nonlinear decision feedback multiuser detector is employed is considered. Via large system analysis (LSA), a decentralized implementation of the power allocation game requiring very little prior information on the interference background is proposed. LSA is also used to compare the energy efficiency of several linear multiuser detectors, and to obtain the optimal (i.e., utility-maximizing) length of the training sequence of each data frame. Numerical results show the effectiveness of the proposed solutions, as well as a very satisfactory agreement of the LSA-based analysis with simulation results obtained for systems with finite (and not so large) numbers of users.

In broadband networks with guaranteed Quality of Service (QoS), dimensioning becomes an even harder task than in traditional data or circuit networks. The network operator has two resources to manage — the trunk capacity and the switch memory (buffers) — and multiple QoS constraints to respect — call blocking, cell loss ratio and cell delays. We investigate how this new context can affect the design, cost and dimensioning of networks. In particular, we study how much can be gained if we do not restrict ourselves to the two limit cases of a bufferless or an infinite buffer system. We deal first with the single-service case for a single link. We then investigate the multi-service case on a single link. We study the cost of integration when service classes have very different delay constraints. The main results obtained are that finite buffers can have a very significant effect on the optimal cost whenever buffer costs are high, as in satellite systems, especially when a delay constraint is imposed on the problem and that services integration can be very costly when different delay requirements are involved.