Partially-Distributed Resource Allocation in Small-Cell Networks

We propose a four-stage hierarchical resource allocation scheme for the downlink of a large-scale small-cell network in the context of orthogonal frequency-division multiple access (OFDMA). Since interference limits the capabilities of such networks, resource allocation and interference management are crucial. However, obtaining the globally optimum resource allocation is exponentially complex and mathematically intractable. Here, we develop a partially decentralized algorithm to obtain an effective solution. The three major advantages of our work are: 1) as opposed to a fixed resource allocation, we consider load demand at each access point (AP) when allocating spectrum; 2) to prevent overloaded APs, our scheme is dynamic in the sense that as the users move from one AP to the other, so do the allocated resources, if necessary, and such considerations generally result in huge computational complexity, which brings us to the third advantage: 3) we tackle complexity by introducing a hierarchical scheme comprising four phases: user association, load estimation, interference management via graph coloring, and scheduling. We provide mathematical analysis for the first three steps modeling the user and AP locations as Poisson point processes. Finally, we provide results of numerical simulations to illustrate the efficacy of our scheme.Comment: Accepted on May 15, 2014 for publication in the IEEE Transactions on Wireless Communication

Optimal decentralized spectral resource allocation for OFDMA downlink of femto networks via adaptive gradient vector step size approach

For the orthogonal frequency division multiple access (OFDMA) downlink of a femto network, the resource allocation scheme would aim to maximize the area spectral efficiency (ASE) subject to constraints on the radio resources per transmission interval accessible by each femtocell. An optimal resource allocation scheme for completely decentralized femtocell deployments leads to a nonlinear optimization problem because the cost function of the optimization problem is nonlinear. In this paper, an adaptive gradient vector step size approach is proposed for finding the optimal solution of the optimization problem. Computer numerical simulation results show that our proposed method is more efficient than existing exhaustive search methods

Decentralized spectral resource allocation for OFDMA downlink of coexisting macro/femto networks using filled function method

For an orthogonal frequency division multiple access (OFDMA) downlink of a spectrally coexisting macro and femto network, a resource allocation scheme would aim to maximize the area spectral efficiency (ASE) subject to constraints on the radio resources per transmission interval accessible by each femtocell. An optimal resource allocation scheme for completely decentralized deployments leads however to a nonconvex optimization problem. In this paper, a filled function method is employed to find the global maximum of the optimization problem. Simulation results show that our proposed method is efficient and effective

Feedback Allocation For OFDMA Systems With Slow Frequency-domain Scheduling

We study the problem of allocating limited feedback resources across multiple users in an orthogonal-frequency-division-multiple-access downlink system with slow frequency-domain scheduling. Many flavors of slow frequency-domain scheduling (e.g., persistent scheduling, semi-persistent scheduling), that adapt user-sub-band assignments on a slower time-scale, are being considered in standards such as 3GPP Long-Term Evolution. In this paper, we develop a feedback allocation algorithm that operates in conjunction with any arbitrary slow frequency-domain scheduler with the goal of improving the throughput of the system. Given a user-sub-band assignment chosen by the scheduler, the feedback allocation algorithm involves solving a weighted sum-rate maximization at each (slow) scheduling instant. We first develop an optimal dynamic-programming-based algorithm to solve the feedback allocation problem with pseudo-polynomial complexity in the number of users and in the total feedback bit budget. We then propose two approximation algorithms with complexity further reduced, for scenarios where the problem exhibits additional structure.Comment: Accepted to IEEE Transactions on Signal Processin