Utility‐optimal cross‐layer resource allocation in distributed wireless cooperative networks

SUMMARY This paper presents an adaptive power pricing scheme to address the fairness issue with relay selection and power allocation in decode‐and‐forward‐based opportunistic relaying networks, which is focused on the scenario where a distributed energy‐constrained cooperative communication system is employed over Rayleigh fading channels. Specifically, the proposed scheme is conducted analytically by obtaining the mathematical expression for the symbol error probability using the statistical characteristic of the signal‐to‐noise ratio, and it also takes the residual energy of nodes into consideration. Numerical results show that the proposed scheme has the advantage of better system reliability and further improves the throughput of the whole network compared with previous algorithms without considering fairness. Copyright © 2013 John Wiley & Sons, Ltd.

U_{s, r} = \frac{E \{Rf (γ_{r})\}}{E_{s} + {normalπ}_{r} E_{r}}

Section: Adaptive Pricing Schemementioning

confidence: 99%

“…That is to say, cooperative transmission with help of relay nodes needs pay cost by the source node. Different from utility function by the ratio of throughput to transmit power [12,13] and by the effective transmission rate minus transmission power [14][15][16][17][18][19], we consider the transmission cost-efficiency function as our design objective, which is defined as the ratio of effective throughput to transmission power cost of packet transmission, that is, the net number of information symbols, transmitted without error, per unit power cost that equals power multiplying power price.…”

Section: Network Objectivesmentioning

confidence: 99%

An adaptive power pricing scheme for improved fairness in energy‐constrained cooperative networks

Cui

Zhong

et al. 2013

“…The issue of resource allocation in non-cognitive cooperative systems was explored extensively in [6][7][8][9] and references therein. In [6], a distributed cross-layer resource allocation algorithm for wireless cooperative networks based on a network utility maximization framework was presented.…”

Section: Introductionmentioning

confidence: 99%

“…The issue of resource allocation in non‐cognitive cooperative systems was explored extensively in and references therein. In , a distributed cross‐layer resource allocation algorithm for wireless cooperative networks based on a network utility maximization framework was presented. The authors in proposed a joint precoding and power allocation strategy to maximize the sum rate of multiuser MIMO relay networks.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation in MIMO‐OFDM‐based cooperative cognitive radio networks: optimal and suboptimal low complexity approaches

Adian

Aghaeinia

2014

SUMMARYThe problem of resources allocation in multiple-input multiple-output-orthogonal frequency division multiplexing based cooperative cognitive radio networks is considered, in this paper. The cooperation strategy between the secondary users is decode-and-forward (DF) strategy. In order to obtain an optimal subcarrier pairing, relay selection and power allocation in the system, the dual decomposition technique is recruited. The optimal resource allocation is realized under the individual power constraints in source and relays so that the sum rate is maximized while the interference induced to the primary system is kept below a pre-specified interference temperature limit. Moreover, because of the high computational complexity of the optimal approach, a suboptimal algorithm is further proposed. The jointly allocation of the resources in suboptimal algorithm is carried out taking into account the channel qualities, the DF cooperation strategy, the interference induced to the primary system and the individual power budgets. The performance of the different approaches and the impact of the constraint values and deploying multiple antennas at users are discussed through the numerical simulation results.

“…It is known that only a small amount of energy is consumed for data processing, while bulk of the energy is utilized for communication among sensor nodes (i.e., the power amplification for signals). In order to improve energy efficiency for node communication, distributed power control methods are recommended [4][5][6]. If the channel gains of WSNs can be estimated beforehand, we can conduct distributed power control method to adjust transmission power according to the channel state for energy saving.…”

Section: Introductionmentioning

confidence: 99%

Energy‐aware cooperative and distributed channel estimation schemes for wireless sensor networks

Ning

Song

Yao

et al. 2015

SUMMARYIt is a fact that energy consumption is a key issue in wireless sensor networks. In order to improve the energy efficiency of network utilization, distributed power control methods are advocated. However, the sensors are deployed randomly, which results in the channel gains that are difficult to be obtained; thus, how to estimate channel gains accurately to increase node communication efficiency and reduce energy consumption is the motivation of our work. In this paper, a cooperative and distributed algorithm based on diffusion least mean square (LMS) is firstly proposed to estimate channel gain by utilizing the equality and collaboration of sensor nodes. Its objective is to enhance the accuracy and convergence of the estimation process. Because the presented globe-based scheme is unrealistic for practical utilization, two distributed estimation algorithms are investigated for different network scenarios, namely, exchange diffusion combine LMS and exchange combine diffusion LMS. Simulation results demonstrate the effectiveness of our methods.