We consider a downlink multi-cell multiple-input multiple-output (MIMO) interference broadcast channel (IBC) using orthogonal frequency division multiplexing (OFDM) with multiple users contending for space-frequency resources in a given scheduling instant. The problem is to design precoders efficiently to minimize the number of backlogged packets queuing in the coordinating base stations (BSs). Conventionally, the queue weighted sum rate maximization (Q-WSRM) formulation with the number of backlogged packets as the corresponding weights is used to design the precoders. In contrast, we propose joint space-frequency resource allocation (JSFRA) formulation, in which the precoders are designed jointly across the space-frequency resources for all users by minimizing the total number of backlogged packets in each transmission instant, thereby performing user scheduling implicitly. Since the problem is nonconvex, we use the combination of successive convex approximation (SCA) and alternating optimization (AO) to handle nonconvex constraints in the JSFRA formulation. In the first method, we approximate the signalto-interference-plus-noise ratio (SINR) by convex relaxations, while in the second approach, the equivalence between the SINR and the mean squared error (MSE) is exploited. We then discuss the distributed approaches for the centralized algorithms using primal decomposition and alternating directions method of multipliers. Finally, we propose a more practical iterative precoder design by solving the Karush-Kuhn-Tucker expressions for the MSE reformulation that requires minimal information exchange for each update. Numerical results are used to compare the proposed algorithms to the existing solutions.
We study the problem of designing transmit beamformers for a multi-group multicasting by considering a multipleinput single-output (MISO) orthogonal frequency division multiplexing (OFDM) framework. The design objective involves either minimizing the total transmit power for certain guaranteed quality-of-service (QoS) or maximizing the minimum achievable rate among the users for a given transmit power budget. The problem of interest can be formulated as a nonconvex quadratically constrained quadratic programming (QCQP) for which the prevailing semidefinite relaxation (SDR) technique is inefficient for at least two reasons. At first, the relaxed problem cannot be reformulated as a semidefinite programming. Secondly, even if the relaxed problem is solved, the so-called randomization procedure should be used to generate a feasible solution to the original QCQP, which is difficult to derive for the considered problem. To overcome these shortcomings, we adopt successive convex approximation (SCA) framework to find multicast beamformers directly. The proposed method not only avoids the need of randomization search but also incurs less computational complexity compared to an SDR approach. In addition, we also extend multicasting beamformer design problem with an additional constraint on the number of active elements, which is particularly relevant when the number of antennas is larger than that of radio frequency (RF) chains. Numerical results are used to demonstrate the superior performance of our proposed methods over the existing solutions.
Abstract-Traffic aware precoder/decoder design in multi-cell multi-user multiple-input multiple-output systems is considered with the objective of weighted queue minimization, where the original non-convex optimization problem is solved via successive convex approximation. Centralized pilot reuse algorithms for mitigating the pilot contamination are investigated to reflect the traffic aware optimization objective. Distinctive feature of the proposed pilot reuse algorithms is to utilize the user buffer state information jointly with the traditional large scale fading values when allocating the limited pilot resources among the served users. Numerical examples compare the performance of the proposed pilot reuse algorithms for varying number of available pilots and different traffic arrival models. The results demonstrate that significant performance gains are available when the pilot allocation strategy is designed to reflect closely the overall system optimization objective.
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