Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services

TLDR

Femtocells promise higher indoor capacity and coverage, yet cross‑tier interference in spectrum‑sharing deployments can severely degrade system performance. This study investigates the uplink and downlink resource‑allocation problem for two‑tier networks of spectrum‑sharing femtocells and macrocells. A scheme that maximizes capacity for delay‑sensitive and delay‑tolerant users under QoS and macrocell interference constraints is proposed, formulated as a mixed‑integer program, relaxed to a convex problem, and solved via dual decomposition, iterative subgradient updates, and a low‑complexity distributed algorithm. Simulation results confirm that the proposed algorithms achieve the desired performance while maintaining manageable computational complexity.

Abstract

Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.