Joint power and rate control using game theory in heterogeneous networks

Efficient resource allocation is a major challenge in cognitive radio networks, especially when Cognitive Users (CUs) share the same frequency band with the Primary User. In this paper, we consider minimizing the total power consumption by combining power control, rate control and adaptive modulation. We analyze the existence, uniqueness and Pareto optimality of Nash Equilibrium (NE) in the power control game, and propose an iterative algorithm to find the NE followed by the adjustment of both the transmission rate and modulation scheme based on the convergent power. If compared with previous works, the key feature of the proposed strategy is that each CU can prolong its battery life in energy-constrained networks to support heterogenous services with different transmission rates and modulation schemes requirements. Simulation results are provided to confirm the effectiveness of the proposed method in power saving, improvement of both the transmission rate and the spectral efficiency and the simplicity of implementation.

Random linear packet coding combined with adaptive power and rate control is being considered as an alternative to traditional ARQ techniques for the underwater acoustic channel. We first analyze a scheme where the number of coded packets to transmit is determined so as to achieve a pre-determined reliability at the receiver, and hence eliminate the need for explicit feedback. We next investigate joint power and rate control with constrained resources. Using the channel gain information which is obtained via feedback from the receiver, the transmitter adjusts its transmit power and the number of coded packets such that the average energy per bit is minimized. We impose two main constraints for the optimization: (a) the transmit power should be within a maximum available power, and (b) the number of coded packets should not exceed a maximum value. Under these constraints, we define adaptation policies to minimize the average energy per bit and show analytical results for average energy savings available from the adaptation policies. We also show experimental results obtained using the measured channel gains from the SPACE-08 experiment, an at-sea experiment conducted off the coast of Martha's Vineyard in Massachusetts.

The conventional method of joint rate and power control (JRPC) relies on high signal-to-interference ratio (SIR) assumption which achieves only suboptimal results. By using a novel successive convex approximations method, we can attain the global optimal source rates and link powers in a distributed fashion exploiting message passing. Through simulations, our method converges faster than the previous work based on logarithm successive convex approximations.

Joint power control and spectrum allocation for cognitive radio networks via pricing

In cognitive networks, the cognitive radio users can share spectrum resource with the primary user while ensuring the normal communication of primary user. In addition, next generation wireless systems needs to provide flexible transmission rate to each terminal. In this paper, a utility-based game-theoretic model is proposed to study the joint power and rate control problem in spectrum underlay fashion in cognitive networks. We adopt a novel utility function based on cognitive network which focuses on social optimal resource allocation through pricing. Further, we show an interference threshold elasticity perspective, which is the key for primary network to maximize its utility by increasing its transmitted power to adjust the tolerable interference constraint. And also, the cognitive network can increase its total throughput capacity. In Stackelberg game, the primary user and the cognitive radio users interact with each other by adjusting their own actions. The cooperative cognitive networks model follows the “best-effort” principle as well as the win-win perspective of primary-cognitive user. Numerical simulations are conducted to demonstrate the performance of the model. The results show that the PU network can get more profit and also the CRU network can increase its total throughput capacity by the adjustment of the interference threshold.

In this paper, we present a game-theoretic approach for the purpose of deriving the problem of joint beamforming and power control in cognitive radio (CR) multiple-input multiple-output (MIMO) broadcast channels (CR MIMO-BCs), where the primary users (PUs) coexist with the secondary users (SUs) and they share the same spectrum. The cognitive base station (CBS), which is equipped with multiple antennas, is capable of transmitting data to the SU’s multiple-antenna receiver by employing the technology of beamforming. The proposed approach is an application of separable games, which are formally stated by the subgames of beamforming and power control. Furthermore, based on the model of noncooperative separate games, separable cost functions for the parameters of beamforming and power control are also proposed, showing that these cost functions are convex. Therefore, the convex theory of a noncooperative game can be employed to investigate the best response strategies as well as existence of Nash equilibrium solutions. Finally, we propose an iterative algorithm to achieve the optimal Nash equilibrium of the proposed joint beamforming subgame and power control subgame. Numerical results verify both the convergence and the tracking properties of the proposed algorithm for variant scenarios.

In vehicle to vehicle communications, periodic broadcast of beacons allows awareness of vehicles in a cooperative manner. In this paper, a joint beacon rate and power control protocol based on game theory is presented to combat channel congestion that is used for transmission of beacons. The existence, uniqueness and stability of the Nash Equilibrium (NE) as the key concept of the game are proved mathematically. In order to find the equilibrium point in a distributed manner, an algorithm is devised and its stability and convergence has been validated in a simulation environment. This algorithm converges to the NE from any initial rate and power. The proposed protocol has per vehicle parameters where each vehicle can control its share of the bandwidth according to its dynamics or safety application requirements while the whole bandwidth usage is controlled. This paper reports on utilizing the rate and power control protocol and presents some early results obtained from a representative simulation environment for VANET.

We investigate the energy-aware joint power and rate control in the overlay cognitive radio networks, and we expect to achieve the optimal performance tradeoff between individual fairness and network energy efficiency. Therefore, a quality of energy efficiency-based Nash bargaining cooperative game (QoE$^2$-NBCG) is formulated with the aid of the newly-built 'energy-per-bit' individual utility function design in this paper. Mathematically, a multi-constrained optimization problem is formulated with Nash product function as the optimization object, which is consisted of the novel individual QoE$^2$ utility function design. To reduce the implementation complexity, we utilize decomposition technique to decouple this multi-constrained problem. In addition, the existence and uniqueness of the socially optimal NB solutions are validated via theorems.

This paper proposes a joint adaptive rate selection and power control algorithm for broadband TDMA/TDM wireless networks. The proposed algorithm is a modified, enhanced, and more robust version of the selective power control with active link protection (SPC-ALP) algorithm proposed for adaptive transmission rate and power control in CDMA networks [R.Jantti and S. Kim, 2000]. Unlike SPC-ALP, the proposed algorithm uses adaptive coding and modulation (instead of variable spreading gain) for transmission rate control. In addition, the proposed algorithm is different in two aspects; the first one is the inclusion of the cochannel interferer assistance mode, while the second one is the use of the signal quality as a criterion for user removal. Results show that the proposed algorithm outperforms SPC-ALP in terms of the net throughput and signal quality measures such as the outage probability and frame error rate. Channel reallocation is also studied and found to be very effective in enhancing the system performance particularly at low to medium loading.

Joint random access and power control game in ad hoc networks with noncooperative users

Interference is one of the most critical issues in the device-to-device (D2D)-enabled heterogeneous cellular networks (HetCNets) due to the coexistence of D2D pairs and various types of cellular user equipments (UEs) that operate in the same spectrum. In this paper, an interference management scheme of joint mode selection and power control is proposed to solve the issue in D2D-enabled HetCNets. In the scheme, we present power control with interference limited area (PC-ILA) in which power control is utilized to adjust the ILA dynamically. Furthermore, according to different ILA, users can select different communication modes to solve the problem of spectrum resource waste caused by a single communication mode. Numerical simulation results are provided to demonstrate the feasibility of the proposed scheme and its superiority over other schemes. Moreover, the proposed mode selection method achieves superior performance compared with existing well-known methods.

This paper studies the problem of energy efficiency (EE) maximization via user association, power, and backhaul (BH) flow control in the downlink of millimeter wave BH heterogeneous networks. This problem is mathematically formulated as a mixed-integer non-linear program, which is non-convex. To get a tractable solution, the initial problem is separated into two sub-problems and optimized sequentially. The first is a joint user association and power control sub-problem for the access network (AN) (AN sub-problem). The second is a joint flow and power control sub-problem for the BH network (BH sub-problem). While the BH sub-problem is a convex optimization problem and hence can be efficiently solved, the AN sub-problem assumes the form of a generalized assignment problem, which is known to be NP-hard. To that end, we utilize Lagrangian decomposition to propose two polynomial time solution techniques that obtain a high-quality solution for the AN sub-problem. The first, referred to as Technique A, uses dynamic programming, the subgradient method, and a heuristic. The second, named Technique B, uses the multiplier adjustment method, the sorting algorithm, and a heuristic. Simulation results are used to demonstrate the effectiveness of the proposed energy efficient user association, power, and BH flow control algorithms as compared with benchmark user association schemes that incorporate the BH sub-problem algorithm, in terms of the total AN power, BH power, and overall network (AN plus BH) EE. The computational complexity and practical implementation of the proposed algorithms are discussed.

Traditional beamforming and power control algo-rithms in cognitive radio (CR) are based on the assumption of perfect channel state information (CSI) however; this may lead to performance degradation in realistic systems.In this paper, the problem of joint beamforming and power control is investigated in underlay CR networks with imperfect CSI.Our objective is to maximize the sum utility of secondary users (SUs) under the primary users (PUs) interference power constraints and the transmission power constraint of SUs.First, the joint beamforming and power control problem is formulated under game theory framework, where the SUs compete with each other over the beamforming vectors and transmission power level made available by the PUs.Moreover, the channel uncertainty is described using ellipsoid sets and the interference power constraints can be converted into robust interference power constraints.Besides, Nash equilibrium (NE) is considered as the solution of this game.Finally, simulation results show that the proposed scheme can converge to a locally optimal pair of beamforming vector and transmission power level in the presence of channel uncertainty.

The introduction of advanced radio resource management features such as directional antennas, power control and variable data rates have been proposed to increase the performance of multihop wireless networks. In this paper we will study the combined effects of adaptive rate selection and power control in spatial time division multiple access (STDMA) networks where nodes are equipped with advanced (beamforming) antennas. In our evaluation, the rates are selected from a discrete set. To assess the optimum performance of the various techniques, joint scheduling, rate and power control is done by solving a non-linear optimization problem. The results show that variable rates schemes can significantly improve the end-to-end network throughput compared to fixed rate schemes, whereas power control adds only a limited additional gain. The latter effect becomes even more pronounced as more narrow-beam antennas are used.

Transmitter adaptation in conjunction with power control enables individual users in multi-user wireless systems to meet specific quality of service (QoS) constraints imposed in terms of target values for the users' signal-to-interference+noise-ratio (SINR) at their intended receiver. Existing algorithms for joint transmitter adaptation and power control require knowledge of channel state information (CSI) which is in general difficult to acquire in wireless environments that are subject to fading. In this paper we study the performance of incremental algorithms for joint transmitter adaptation and power control with fading channels by formally defining a probability of outage measure and evaluating it through Monte-Carlo simulations in specific scenarios.