Optimal Beamforming and Scheduling for MIMO-OFDM Uplink Transmissions in Hierarchical Cognitive Radio Systems

Abstract

Cognitive radio (CR) technology can be been applied in hierarchical cellular systems to allow concurrent transmissions for licensed (primary) and unlicensed (secondary) users to improve spectrum utilization. The major challenge of hierarchical CR systems is to manage the inter-cell interference between the primary and secondary systems. In the paper, we first present an optimal multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) uplink transmission scheme for hierarchical CR systems that can maximize the sum rate of the underlay secondary CR users, and prevent the primary systems from the severe interference of the secondary systems. The main contribution of this work is to propose a methodology to transform the signal to interference plus noise ratio (SINR) maximization problem of the secondary system into a quasi-convex form, thereby obtaining the optimal beamforming solution using a simple bisection method. Furthermore, we proposed a sub-optimal user scheduling algorithm in the secondary system to effectively mitigate the interference to the primary system.

Distributed opportunistic scheduling for MIMO underlay cognitive radio networks

“…The problem of scheduling the SUs in an uplink secondary network was also considered in . In that, the authors formulated an optimization problem to maximize the SINR of the secondary system where by using a bisection method the optimal beamforming weights were obtained.…”

“…Moreover, unlike , our algorithm considers scheduling only the SUs taking into consideration the interference limitation of the primary network so that the desired transparency of the secondary network is achieved. Although Lu et al , considered an uplink secondary network as our model, the authors provided a centralized resource allocation that slows the performance and increases the system overhead. Also, unlike Knopp and Humblet who assumed SISO ad hoc secondary network and utilized a centralized scheduler, our proposed distributed algorithm allows the MIMO secondary network to transmit its data continuously.…”

On the Distributed Resource Allocation of MIMO Cognitive Radio Networks

“…To overcome the complexity and cost requirements, antenna selection techniques have been widely introduced where close to full MIMO system performance can be achieved with less number of RF chains [11]- [13]. The problem of scheduling the SUs in a MIMO secondary network was considered in [14] where the optimal beamforming weights were obtained. Then, a suboptimal scheduling technique assuming that the secondary network knows the beamforming weights of the scheduled PUs was provided.…”

“…In [15], two heuristic secondary scheduling algorithms for MIMO cognitive radio networks (CRN) rely on the orthogonality between the primary and the secondary links were introduced. The work in [14], [15] is based on centralized scheduling. In [16], the authors provided a distributed scheduling algorithm depends on the nodes competition in transmitting out control messages over a common control channel (CCC) using CSMA-based scheme.…”