We consider the maximization of the weighted sum-rate on a multiuser-OFDM downlink with adaptive modulation and power under a total transmit power constraint and a user-wise target BER. We allow each subcarrier to be shared by more than one user. We show that this optimization problem can be decomposed into two subproblems: a subcarrier assignment and a power allocation. We prove that the optimal subcarrier assignment is exclusive, that is each subcarrier is allocated to only one user. The optimal power allocation corresponds to a multilevel water-fi lling. When the achievable rate region is convex, the optimality of the exclusive subcarrier assignment for arbitrary weights means that the OFDMA is the optimal sharing scheme for various performance criteria.
We address the problem of resource allocation on an OFDMA downlink under fairness constraints with limited Channel State Information (CSI). Target QoS corresponds to a minimum user data rate, a target bit-error rate and a maximum outage probability. The only available CSI is the channel average gain of each user. This partial CSI can be viewed as a shadowed pathloss that yields a modified user distribution on which resource allocation is based. Under these constraints, we provide the optimal resource allocation that maximizes the user rate. Compared to full-CSI-based allocation schemes, our solution offers a significant complexity and feedback reduction as well as a good robustness to CSI estimation errors.
We address the problem of resource allocation on the downlink of an OFDMA single-cell system under fairness constraints with limited channel state information (CSI). Target QoS corresponds to a minimum user data rate, a target bit-error rate and a maximum BER-outage probability. The channel model includes path-loss, shadowing, and fading. The only available CSI is the channel average gain of each user. This partial CSI defines a shadowed path-loss that yields a modified user distribution. Resource allocation is based on the shadowed user distribution that we characterize analytically. Thus, under the target QoS, we provide the optimal resource allocation that maximizes the user rate. Compared to full-CSI-based allocation schemes, our solution offers a significant complexity and feedback reduction. Finally, the performance of our method is compared to other existing methods and the robustness of its outage performance to CSI errors is shown.
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