Dhananjaya Sarma Ponukumati

Robust Peer-to-Peer Relay Beamforming: A Probabilistic Approach

In this work, we design outage constrained collaborative relay beamforming (CRBF) vectors for a peer-to-peer amplify-and-forward (AF) relay network with imperfect channel state information (CSI) at the relays. Specifically, we model channel estimation error as a Gaussian random vector with known statistical distribution. The objective is to minimize the total transmit power at relays subject to probabilistic quality of service (QoS) constraints at each receiver. To solve the original non-convex problem, we utilize Bernstein-type inequalities and recast the original probabilistic constraints into conservative deterministic linear matrix inequalities (LMI). An alternative method is to replace the probabilistic constraint with a conservative one by applying S-procedure. Employing rank relaxation technique, the two convex reformulations are numerically solved with semidefinite programming (SDP). Simulation results are provided to corroborate our studies.

Robust Multicell Downlink Beamforming Based on Second-Order Statistics of Channel State Information

In this paper, we design downlink (DL) beamforming vectors for a multiuser multicell network when only imperfect knowledge of the channel covariance is available at base stations. Specifically, we consider two different models for covariance errors: a)deterministic error bounded in a spherical region and b) stochastic error with known probability distribution. Our objective is to minimize the total DL transmit power subject to quality of service (QoS) constraint of every user. It is shown that for both uncertainty models, the optimization can be formulated as a convex semidefinite programming (SDP) problem using the standard rank relaxation approach. Interestingly, numerical results show that the obtained solutions fulfill the rank constraint and are therefore exact.

Multicell Downlink Beamforming with Imperfect Channel Knowledge at Both Transceiver Sides

In this letter, we design downlink beamforming vectors for a multicell network when only imperfect channel state information (CSI) is available at both base stations (BS) and mobile users (MU). We consider two different optimization criteria, i.e., minimizing the total downlink transmit power subject to quality of service (QoS) constraints at each MU and maximizing the worst-case effective signal-to-interference-plus-noise ratio (SINR) subject to BS power constraints. To solve the optimization problems, the imperfect CSI embedded QoS constraints are converted to linear matrix inequalities (LMI) with the aid of S-procedure, and then a semidefinite relaxation (SDR) approach is applied. Numerical results are provided to corroborate the proposed studies.

Robust General Rank Precoding Design for Amplify-and-Forward Relay Network

In this paper, we consider the problem of precoding design for amplify-and-forward (AF relay network with imperfect channel state information (CSI). We find a general rank precoding matrix at the relay such that the relay transmit power is minimized subject to quality of service (QoS) constraint as the worst case signal-to-noise ratio (SNR) at the destination. Since the direct optimization is nonconvex, we apply conservative methods to reformulate it as a semi-definite programming (SDP) problem which provides the upperbound of the original objective. Specifically, we suggest two SDP formulations that can be solved efficiently via convex optimization tools. We numerically compare the proposed suboptimal methods with the existing method, i.e., collaborative robust relay beamforming (CRBF), and show that the proposed schemes achieve a significant performance gain for a majority of feasible uncertainty sizes.

Robust coordinated downlink beamforming for multicell-cognitive radio networks with probabilistic constraints

In this paper, we design downlink (DL) beam-forming vectors for a multiuser multicell cognitive radio (CR) network with imperfect channel state information (CSI) at base stations (BS). Specifically, we model channel estimation error as a random vector with known statistical distribution. Our objective is to minimize the total DL transmit power subject to probabilistic quality of service (QoS) constraints of every secondary user (SU) and primary user (PU). Utilizing Bernstein-type inequalities [12], we replace the probabilistic constraints with conservative deterministic constraints. By applying rank relaxation, the original problem is reformulated as semidefinite programming (SDP). Interestingly, numerical results show that the obtained solutions fulfill the rank constraint.

Robust coordinated downlink beamforming for multicell-cognitive radio networks

In this paper, we design downlink (DL) beamforming vectors for a multiuser multicell cognitive radio (CR) network with imperfect channel state information (CSI) at base stations (BS). Specifically, we consider deterministic error in both channel gain and channel covariance. Our objective is to minimize the total DL transmit power subject to quality of service (QoS) constraints of each secondary user (SU) and primary user (PU). The optimization problems for both uncertainty models can be transformed into convex semidefinite programming (SDP) from the standard rank relaxation approach. Interestingly, numerical results show that the obtained solutions fulfill the rank constraint and are therefore exact.