References of "ShahbazPanahi, Shahram"
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See detailJoint Power Allocation and Access Point Selection for Cell-free Massive MIMO
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; ShahbazPanahi, Shahram et al

in IEEE International Conference on Communications (2020)

Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users ... [more ▼]

Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users. Each AP applies conjugate beamforming to precode data, which is based only on the AP's local channel state information. However, by having the nature of a (very) large number of APs, the operation of CF-MIMO can be energy-inefficient. In this paper, we investigate the energy efficiency performance of CF-MIMO by considering a practical energy consumption model which includes both the signal transmit energy as well as the static energy consumed by hardware components. In particular, a joint power allocation and AP selection design is proposed to minimize the total energy consumption subject to given quality of service (QoS) constraints. In order to deal with the combinatorial complexity of the formulated problem, we employ norm $l_{2,1}$-based block-sparsity and successive convex optimization to leverage the AP selection process. Numerical results show significant energy savings obtained by the proposed design, compared to all-active APs scheme and the large-scale based AP selection. [less ▲]

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See detailConvex Optimization-based Beamforming: From Receive to Transmit and Network Designs
Gershman, Alex B.; Sidiropoulos, Nicholas D.; Shahbazpanahi, Shahram et al

in IEEE Signal Processing Magazine (2010), 27(3), 62-75

In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that ... [more ▼]

In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that apply to a broad class of receive, transmit, and network beamformer design problems. It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems. [less ▲]

Detailed reference viewed: 193 (2 UL)