Matrix analysis
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Comparison of ℓ∞-norm and ℓ1-norm optimization criteria for SIR-balanced multi-user beamforming
Signal Processing - Special issue on independent components analysis and beyond
QoS-based resource allocation and transceiver optimization
Communications and Information Theory
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
The geometry of the capacity region for CDMA systems with general power constraints
IEEE Transactions on Wireless Communications
A generalized framework for distributed power control in wireless networks
IEEE Transactions on Information Theory
A framework for uplink power control in cellular radio systems
IEEE Journal on Selected Areas in Communications
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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We introduce a model of a generalised multi-receiver radio network with quality-of-service (QoS) constraints. There are two key functions: (1) Ni is non-decreasing and homogeneous and gives i's QoS as function of its carrier-to-interference ratios at each of K receivers, (2) nu_ik is a semi-norm that gives the interference experienced by transmitter i at receiver k as function of the power vector. We utilise "norm" concepts and Banach's well-known fixed-point theorem to characterise the conditions under which a QoS vector is feasible, and the corresponding power-adjustment process converges. The critical power levels equal a_i/h_i where a_i is the QoS target, and h_i is the 'average' channel gain. hi=Ni(h_i1,..., h_i,K) where h_ik is the channel gain from transmitter i to receiver k. If the interference experienced by each transmitter i at each receiver k is less than 1 when each power is set to the critical level (i.e., nu_ik(a_1/h_1, a_2/h_2,..., a_N/h_N)