Abstract-We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer carrier allocations, different subchannelization schemes, a maximum SNR constraint per tone, and "self-noise" due to channel estimation errors and phase noise. During each time-slot a subset of users must be scheduled for transmission, and the available tones and transmission power must be allocated among the selected users. Employing a gradient-based scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each time-slot. Using dual decomposition techniques, we give an optimal algorithm for this problem when multiple users can time-share each carrier. We then give several low complexity heuristics that enforce an integer constraint on the carrier allocation. Simulations show that the algorithms presented all achieve similar performance under a wide range of scenarios, and that the performance gap between the optimal and suboptimal algorithms widens when per user SNR constraints or channel estimation errors are considered.

We consider a non-Bayesian infinite horizon version of the multi-armed bandit problem with the objective of designing simple policies whose regret increases slowly with time. In their seminal work on this problem, Lai and Robbins had obtained a O(log n) lower bound on the regret with a constant that depends on the Kullback–Leibler number. They also constructed policies for some specific families of probability distributions (including exponential families) that achieved the lower bound. In this paper we construct index policies that depend on the rewards from each arm only through their sample mean. These policies are computationally much simpler and are also applicable much more generally. They achieve a O(log n) regret with a constant that is also based on the Kullback–Leibler number. This constant turns out to be optimal for one-parameter exponential families; however, in general it is derived from the optimal one via a ‘contraction' principle. Our results rely entirely on a few key lemmas from the theory of large deviations.

Abstract-Orthogonal Frequency Division Multiplexing (OFDM) with dynamic scheduling and resource allocation is widely considered to be a key component of 4G cellular networks. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (1) the discrete nature of channel assignments, and (2) the heterogeneity of the users' channel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework presented in previous work. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem. We present an optimal solution using a dual decomposition. This solution has prohibitively high computational complexity but reveals guiding principles that we use to generate a family of lower complexity sub-optimal algorithms. We compare the performance of these algorithms via a realistic OFDM simulator.

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