Distributed opportunistic scheduling for MIMO underlay cognitive radio networks

Abstract

Cognitive radio networks have emerged to improve the utilization of the scarce spectrum. In this paper, we propose a distributed resource allocation algorithm that allocates resources opportunistically to the secondary users in a multiple-input multiple-output environment. In order to reduce the complexity and cost, antenna selection schemes are employed to allow the secondary communication using a single radio frequency chain. The proposed algorithm is proved theoretically and using simulations, to give a performance very close to that of a centralized one with lower delay and overhead. Furthermore, we introduce two techniques for the proposed algorithm based on the allowable data rates referred to as limited and maximum rates. We derive closed-form expression for the consumed power and tight upper bounds for the average throughput achieved by each technique. A comparison between the proposed techniques is also provided. Both simulations and analytical results show that the proposed algorithm achieves high throughput with low complexity. Moreover, the results show that the tightness of the bounds improves with the diversity order. Finally, the proposed techniques are compared with two suggested random schemes to investigate their effectiveness.

Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems

“…The equal-split algorithm is a single-step algorithm that does not require information exchange among the secondary operators. The method starts with splitting up the interferencetemperature thresholds equally among the operators and formulating the optimization problems locally for all operators, i.e., (9)…”

“…On the other hand, there is no center node for the distributed approach, and RA is performed in a decentralized manner that often cannot obtain the optimal solution. Several distributed approaches for RA in CR have been presented [9], [10], where they either do not consider the coupling interference constraints or there is no flexibility in the level of information exchange among the operators. It should be pointed out that considering the coupling constraints, the information exchange among otherwise competing operators is often mandatory which in most scenarios, raises complexity and privacy issues.…”

Performance Enhancement for Multihop Cognitive DF and AF Relaying Protocols under Joint Impact of Interference and Hardware Noises: NOMA for Primary Network and Best‐Path Selection for Secondary Network

“…In CR, primary users who are used licensed bands at any time can share spectrum to secondary users. Underlay cognitive radio (UCR) [28][29][30] is one of efficient spectrum-sharing methods that can guarantee the continuous data transmission for the secondary network. However, the secondary transmitters in the UCR networks must adjust their transmit power so that quality of service (QoS) of the primary network is not harmful.…”

Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems

“…On the other hand, there is no center node for the distributed approach, and RA is performed in a decentralized manner that often cannot obtain the optimal solution. Several distributed approaches for RA in CR have been presented [9], [10], where they either do not consider the coupling interference constraints or there is no flexibility in the level of information exchange among the operators. It should be pointed out that considering the coupling constraints, the information exchange among otherwise competing operators is often mandatory which in most scenarios, raises complexity and privacy issues.…”