No abstract

We address the problem of multi-user downlink beamforming and power allocation in a cognitive radio (CR) secondary network (SN) with constraints on the total interference in the primary network (PN). We derive the Lagrange dual of the problem and show that both problems are equivalent. Two algorithms are proposed to solve the problem. The first is based on convex optimization and the second algorithm exploits the uplink-downlink duality that is enforced by the introduction of appropriate slack variables in the constrained optimization problem. This leads to a simple iterative technique that enjoys easy implementation and low computational costs. Simulation results illustrate that the proposed iterative technique converges to the global optimum in all cases.

In this paper we take a new perspective on the worst case robust multiuser downlink beamforming problem with imperfect second order channel state information at the transmitter. Recognizing that all channel covariance matrices form a Riemannian manifold, we propose to use a measure properly defined along this manifold in order to model the set of mismatched channel covariance matrices for which robustness shall be guaranteed. This leads to a new robust beamforming problem formulation for which a convex approximation is derived. Simulation results show a dramatically improved performance of the proposed scheme, both in terms of transmission power and constraint satisfaction, as compared to the previous methods.

This paper introduces a low complexity method for antenna sector selection in mmWave Hybrid MIMO communication systems like the IEEE 802.11ay amendment for Wireless LANs. The method is backwards compatible to the methods already defined for the released mmWave standard IEEE 802.11ad. We introduce an extension of the 802.11ad channel model to support common Hybrid MIMO configurations. The proposed method is evaluated and compared to the theoretical limit of transmission rates found by exhaustive search. In contrast to state-of-the-art solutions, the presented method requires sparse channel information only. Numerical results show a significant complexity reduction in terms of number of necessary trainings, while approaching maximum achievable rate.

We examine the robust downlink beamforming design from the point of outage probability constraint. We further reason that since the estimated downlink channel correlation (DCC) matrices form a manifold in the signal space, the estimation error should be measured in terms of Riemannian distance (RD) instead of the commonly used Euclidean distance (ED). Applying this concept of measure to our design constraint, we establish approximated outage probability constraints using multidimensional ball set and multidimensional cube set. We transform the design problem into a convex optimization problem which can be solved efficiently by standard methods. Our proposed methods apply to both Gaussian distribution assumption and uniform distribution assumption. Simulation results show that the performance of our design is superior to those of other robust beamformers recently developed.