Convex Optimization-Based Beamforming

Robust SINR-constrained MISO downlink beamforming: when is semidefinite programming relaxation tight?

“…In the examples, we consider the channel uncertainty regions as (18). We set and Figure 5 illustrates the change in the rank with respect to the increase in the uncertainty bound 3 …”

“…where γ k > 0 is the SINR requirement for receiver k. Although problem (P NR ) is nonconvex, it can be transformed to a convex problem and solved globally and efficiently [1][2][3][4]. Because of the finite length of training signal and/or limited feedback bandwidth, perfect CSIT is not available in practice.…”

“…It is well known that if the perfect channel state information is available at the transmitter (CSIT), the optimal transmit beamforming problem can be solved efficiently via convex optimization [1][2][3][4]. However, perfect CSIT is impossible to obtain in practice due to the finite length of training signal and/or limited feedback bandwidth [5,6].…”

Robust precoding design for multibeam downlink satellite channel with phase uncertainty

“…In general, there are several different strategies to obtain robustness against channel uncertainty [12]: (a) Worstcase design using a deterministic uncertainty model, where the true value is known to be within a certain interval, and optimizing the performance of the worst case situation. This is sometimes called maxmin robustness [8].…”

“…In particular, the probabilistic approach will be used for the robust design since it can guarantee a certain level of user availability (1− outage probability×100%) which is a very important QoS measure for satellite operators. While literature focuses on additive uncertainty model for robust designs [8][9][10][11][12][13], the current study employs a multiplicative model for phase induced channel uncertainty. The use of such an uncertainty model and the ensuing analysis are novel, especially in the satellite communication literature.…”