Abstract-The capacity region of multihop wireless network is involved in many capacity optimization problems. However, the membership of the capacity region is NP-complete in general, and hence the direct application of capacity region is quite limited. As a compromise, we often substitute the capacity region with a polynomial approximate capacity subregion. In this paper, we construct polynomial μ-approximate capacity subregions of multihop wireless network under either 802.11 interference model or protocol interference model in which all nodes have uniform communication radii normalized to one and uniform interference radii ρ ≥ 1. The approximation factor μ decreases with ρ in general and is smaller than the best-known ones in the literature. For example, μ = 3 when ρ ≥ 2.2907 under the 802.11 interference model or when ρ ≥ 4.2462 under the protocol interference model. Our construction exploits a nature of the wireless interference called strip-wise transitivity of independence discovered in this paper and utilize the independence polytopes of cocomparability graphs in a spatial-divide-conquer manner. We also apply these polynomial μ-approximate capacity subregions to compute μ-approximate solutions for maximum (concurrent) multiflows.
In Cognitive Radio Network (CRN), where Primary User (PU) and multiple Secondary Users (SUs) wish to communicate with their corresponding receivers simultaneously over fading channels, spectrum utilization and efficient resource allocation are both significant points for CRN. Interference between PU and SUs should be eliminated in order to realize spectrum sharing. Multi-user resource allocation with the target of maximizing the spectral efficiency of SUs and satisfying the proportional rate constraint between SUs are proposed under the conditions of total SU interference constraint. An adaptive low-complexity suboptimal algorithm for subcarrier and power joint allocation is presented based on Rate Adaptive (RA) criterion, where adaptive subcarrier allocation is performed by assuming equal power distribution, while each subcarrier is assigned in accordance with subcarrier efficiency function. Moreover, linear water-filling algorithm for power allocation is applied within each subcarrier. Simulation results indicate that, with the proposed adaptive subcarrier allocation, spectral efficiency of multiple SUs is superior to traditional subcarrier power joint allocation algorithm. Low computational complexity and adaptive features make it available for implementation.
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