Resource Allocation In Cognitive Networks Research Articles

Efficient Resource Allocation in Cognitive Networks

Cognitive radio (CR) in conjunction with multiple-input multiple-output (MIMO) orthogonal frequency-division multiple access is a candidate technology for future mobile radio networks. The short communication range of underlay CR systems is commonly a major limiting factor. In this paper, we propose a computationally and spectrally efficient resource allocation scheme for multiuser MIMO orthogonal-frequency-division-multiplexing-based underlay CR networks to provide good spectral efficiency gain and, therefore, increased communication range. Since the formulated optimization problem defines a mixed-integer programming (combinatorial task) that is hard to solve, we propose a two-phase scheme to produce efficient solutions for both the downlink and the uplink. Particularly, the first procedure elaborates on an adaptive precoding that is characterized by spectral efficiency due to the degrees of freedom it can provide. The second procedure develops a fast subcarrier mapping algorithm, which can be worked out through optimal power distribution among the CR users. The proposed scheme is optimal for the downlink but, however, near-optimal for the uplink. Simulation results demonstrate the bandwidth and computational efficiencies of the proposed scheme compared with the state of the art.

Quantized Ergodic Radio Resource Allocation in Cognitive Networks With Guaranteed Quality of Service for Primary Network

In this paper, for cognitive networks, ergodic resource allocation (ERA) in the downlink of an OFDMA-based secondary network is investigated, assuming the availability of quantized channel state information (CSI) between the secondary base station and secondary users. The main objective is to maximize the average sum rate of the secondary network subject to the average total transmission power constraint and the collision probability constraint of the primary users on each subcarrier. In the proposed scheme, there is no need for the instantaneous handshaking between secondary and primary networks to provide CSI between the secondary base station and primary receivers. In fact, the secondary base station only needs the knowledge of channel distribution information. Due to the probabilistic nature of the collision probability constraint in the proposed ERA problem, it cannot be solved by conventional methods such as dual decomposition. Therefore, we propose two novel suboptimal solutions called the iterative and analytical approaches. It is also demonstrated that the solutions obtained based on the proposed approaches are very close to the optimal solution. Numerical experiments are then carried out to demonstrate the performance of the proposed methods. Numerical results show that the performance of the analytical approach is inferior to that of the iterative approach, although it exhibits significantly less computational complexity.

Joint sub-carrier pairing and resource allocation for cognitive networks with adaptive relaying

Relayed transmission in a cognitive radio (CR) environment could be used to increase the coverage and capacity of communication system that benefits already from the efficient management of the spectrum developed by CR. Furthermore, there are many types of cooperative communications, including decode-and-forward (DAF) and amplify-and-forward (AAF). In this paper, these techniques are combined in an adaptive mode to benefit from its forwarding advantages; this mode is called adaptive relaying protocol (ARP). Moreover, this work focuses on the joint power allocation in a cognitive radio system in a cooperative mode that operates ARP in multi-carrier mode. The multi-carrier scenario is used in an orthogonal frequency division multiplexing (OFDM) mode, and the problem is formulated to maximize the end-to-end rate by searching the best power allocation at the transmitters. This work includes, besides the ARP model, a sub-carrier pairing strategy that allows the relays to switch to the best sub-carrier pairs to increase the throughput. The optimization problem is formulated and solved under the interference and power budget constraints using the sub-gradient algorithm. The simulation results confirm the efficiency of the proposed adaptive relaying protocol in comparison to other relaying techniques. The results show also the consequence of the choice of the pairing strategy.