Abstract-Multicast beamforming exploits subscriber channel state information at the base station to steer the transmission power towards the subscribers, while minimizing interference to other users and systems. Such functionality has been provisioned in the long-term evolution (LTE) enhanced multimedia broadcast multicast service (EMBMS). As antennas become smaller and cheaper relative to up-conversion chains, transmit antenna selection at the base station becomes increasingly appealing in this context. This paper addresses the problem of joint multicast beamforming and antenna selection for multiple co-channel multicast groups. Whereas this problem (and even plain multicast beamforming) is NP-hard, it is shown that the mixed ℓ1,∞-norm squared is a prudent group-sparsity inducing convex regularization, in that it naturally yields a suitable semidefinite relaxation, which is further shown to be the Lagrange bi-dual of the original NP-hard problem. Careful simulations indicate that the proposed algorithm significantly reduces the number of antennas required to meet prescribed service levels, at relatively small excess transmission power. Furthermore, its performance is close to that attained by exhaustive search, at far lower complexity. Extensions to max-min-fair, robust, and capacity-achieving designs are also considered.
Quadratically constrained quadratic programs (QC-QPs) have a wide range of applications in signal processing and wireless communications. Non-convex QCQPs are NP-hard in general. Existing approaches relax the non-convexity using semidefinite relaxation (SDR) or linearize the non-convex part and solve the resulting convex problem. However, these techniques are seldom successful in even obtaining a feasible solution when the QCQP matrices are indefinite. In this paper, a new feasible point pursuit successive convex approximation (FPP-SCA) algorithm is proposed for non-convex QCQPs. FPP-SCA linearizes the non-convex parts of the problem as conventional SCA does, but adds slack variables to sustain feasibility, and a penalty to ensure slacks are sparingly used. When FPP-SCA is successful in identifying a feasible point of the non-convex QCQP, convergence to a Karush-Kuhn-Tucker (KKT) point is thereafter ensured. Simulations show the effectiveness of our proposed algorithm in obtaining feasible and near-optimal solutions, significantly outperforming existing approaches. Index Terms-Non-convex QCQP, feasible point pursuit, successive convex approximation, semi-definite relaxation, linearization, multicast beamforming.
Abstract-Wideband spectrum sensing is a key requirement for cognitive radio access. It now appears increasingly likely that spectrum sensing will be performed using networks of sensors, or crowd-sourced to handheld mobile devices. Here, a network sensing scenario is considered, where scattered low-end sensors filter and measure the average signal power across a band of interest, and each sensor communicates a single bit (or coarsely quantized level) to a fusion center, depending on whether its measurement is above a certain threshold. The focus is on the under-determined case, where relatively few bits are available at the fusion center. Exploiting non-negativity and the linear relationship between the power spectrum and the autocorrelation, it is shown that adequate power spectrum sensing is possible from few bits, even for dense spectra. The formulation can be viewed as generalizing classical nonparametric power spectrum estimation to the case where the data is in the form of inequalities, rather than equalities.
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