Rusdha Muharar

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In this paper, we study feedback optimization problems that maximize the users' signal to interference plus noise ratio (SINR) in a two-cell multiple-input multiple-output broadcast channel. Assuming the users learn their direct and interfering channels perfectly, they can feed back this information to the base stations (BSs) over the uplink channels. The(More)
We consider a multiuser system where a single transmitter equipped with multiple antennas (the base station) communicates with multiple users each with a single antenna. Regularized channel inversion is employed as the precoding strategy at the base station. Within this scenario we are interested in the problems of power allocation and user admission(More)
In this paper, we perform a large system analysis of regularized channel inversion (RCI) beamforming with channel uncertainty in Time-Division Duplexed (TDD) training based broadcast channels. In the analysis, we consider the problem of finding the optimal training period and power allocation for the uplink training and data transmission. Each user sends(More)
In a multicell network, the quality of the channel state knowledge at the base stations (BSs) affects system performance. When this knowledge is acquired through a quantized feedback scheme, its quality is roughly determined by the number of feedback bits. In this paper we investigate feedback optimization problems for the quantized feedback scheme via(More)
A common approach to obtain channel state information for massive MIMO networks is to use the same orthogonal training sequences in each cell. We call this the full-pilot reuse (FPR) scheme. In this paper, we study an alternative approach where each cell uses different sets of orthogonal pilot (DOP) sequences. Considering uplink communications with matched(More)
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