Data networks
Convex separable optimization is not much harder than linear optimization
Journal of the ACM (JACM)
Polynomial Methods for Separable Convex Optimization in Unimodular Linear Spaces with Applications
SIAM Journal on Computing
Adaptive Scatternet Support for Bluetooth Using Sniff Mode
LCN '01 Proceedings of the 26th Annual IEEE Conference on Local Computer Networks
Capacity assignment in Bluetooth scatternets: optimal and heuristic algorithms
Mobile Networks and Applications
Communication nets; stochastic message flow and delay
Communication nets; stochastic message flow and delay
A flexible scatternet-wide scheduling algorithm for Bluetooth networks
PCC '02 Proceedings of the Performance, Computing, and Communications Conference, 2002. on 21st IEEE International
Bluetooth scatternet formation: A survey
Ad Hoc Networks
Ultra-wideband radio technology: potential and challenges ahead
IEEE Communications Magazine
Load-adaptive inter-piconet scheduling in small-scale Bluetooth scatternets
IEEE Communications Magazine
Maximizing throughput in wireless networks via gossiping
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Enabling distributed throughput maximization in wireless mesh networks: a partitioning approach
Proceedings of the 12th annual international conference on Mobile computing and networking
Performance Analysis of Dynamic Priority Shifting
EPEW '08 Proceedings of the 5th European Performance Engineering Workshop on Computer Performance Engineering
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A major issue in the design and operation of ad hoc networks is sharing the common spectrum among links in the same geographic area. Bandwidth allocation, to optimize the performance of networks in which each station can converse with at most a single neighbor at a time, has been recently studied in the context of Bluetooth Personal Area Networks. There, centralized and distributed, capacity assignment heuristics were developed, with applicability to a variety of ad hoc networks. Yet, no guarantees on the performance of these heuristics have been provided. In this paper, we extend these heuristics such that they can operate with general convex objective functions. Then, we present our analytic results regarding these heuristics. Specifically, we show that they are β-approximation (β