Robust Beamforming Design for MISO Bursty Interference Channels Under Traffic Uncertainties

In this paper, we consider a coordinated beamforming design for a multiple-input single-output bursty interference channel. In practical scenarios, the distributed medium access control mechanisms or the decentralized networking protocols across different users contribute to the bursty nature of network traffic. The potential gains provided by such burstiness can be exploited by feeding back the interference state information to the transmitters. Errors may occur during such feedback, thus causing uncertainties in the user traffic and resulting in complicated beamforming design problems. In this paper, we model such traffic uncertainties by using a hidden Markov model (HMM). Assuming that perfect channel state information (CSI) is available at the transmitters, our goal is to maximize the average system utility subjected to average power constraints under traffic uncertainties. We also extend the optimization problem to the imperfect CSI case. However, the resulting problem is highly nonconvex. Hence, we apply a series of convex approximation techniques. We further improve the approximation accuracy by using our proposed successive convex approximation (SCA) algorithms (HMM-SCA). In our simulation results, we observe that our proposed HMM-SCA provides a
$46.3\%$
improvement in weighted sum rate over the SCA algorithm, in which the interference is assumed to be always present (NB-SCA). For the average weighted geometric mean rate, we observe that our proposed HMM-SCA also provides a
$41.3\%$
improvement over the NB-SCA when user fairness is considered. Therefore, it is crucial to exploit bursty traffic for a practical wireless communication system when channel estimation errors are considered.