Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Greed is good: approximating independent sets in sparse and bounded-degree graphs
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Modulation scaling for Energy Aware Communication Systems
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 1)-Volume - Volume 1
Capacity regions for wireless ad hoc networks
IEEE Transactions on Wireless Communications
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In this paper, we solve the resource allocation problem of maximizing the sum of transmitter utilities subject to QoS and peak power constraints per link in a wireless multihop network. Each node in the network has an associated utility function that models its valuation of its data rate (or signal power) in terms of its transmission power and multi-access interJrence. By explicitly accounting for multi-access interference in the utility function, our framework can model and salve a wide variety of resource allocation problems. Each link in the network is subject to a minimum and a maximum data rate constraint and each transmitter is subject to a peak power constraint. We present an iterative power control algorithm that solves the above problem using a penalty function approach andprove its convergence to the optimal solution. Ourpower control policy is applicable over any subset of links scheduled. To achieve high data rates over the links in addition to maximizing system utility, we schedule links using a degree-based greedy algorithm that limits multi-access interference by scheduling a small number of transmissions around any scheduled receive. The link scheduling algorithm and the power control algorithm are both amenable to distributed implementation in the framework of 802.11 LANs. Finally, we compare the performance of ourjoint scheduling and power control algorithms against CDMA using example utility functions and illustrate the superior performance of our algorithms.