Effective Capacity Of Secondary User Research Articles

Effective capacity optimization for cognitive radio networks under primary QoS provisioning

Cognitive radios have emerged as a key enabler for opportunistic spectrum access, in order to tackle the wireless spectrum scarcity and under utilization problems over the past two decades. In this paper, we aim to enhance the secondary user (SU) performance while maintaining the desired average packet delay for the primary user (PU). In particular, we investigate the trade-off between delay-constrained primary and secondary users in cog- nitive radio systems. In the first part of this work, we use the hard-sensing scheme to make a decision on the PU activity and maximize the SU effective capacity subject to an average PU delay constraint. Second, we propose a soft-sensing scheme by dividing the PU energy interval where the PU is decided to be idle into multiple decision. We also maximize the SU effective capacity subject to an average primary user delay constraint; then, we present three modifications for the proposed soft-sensing scheme to allow for low complexity implementation that is comparable to the complexity of the hard-sensing scheme, but with better performance. The numerical results reveal interesting insights comparing our soft sensing to the hard-sensing models in terms of the optimal performance obtained from our optimization solution compared to the unconstrained PU delay baseline system studied earlier in the literature. For instance, the hard sensing system in Akin and Gursoy (IEEE Trans Wirel Commun 9(11):3354–3364, 2010) and Abdel-Malek et al. (CrownCom 156:30–42, 2015) yields a SU effective capacity of only 50 % of the ideal, perfect sensing system. On the other hand, we show that the soft sensing system yields almost 87 % of the perfect sensing performance (at a primary user arrival rate of $$\lambda _p = 0.1$$), which further increases for a larger number of decision sub-intervals.

QoS-Driven Power Allocation Under Peak and Average Interference Constraints in Cognitive Radio Networks

Efficient radio spectrum utilization can be improved using cognitive radio technology. In this work, we consider a spectrum underlay cognitive radio system operating in a fading environment. We propose an efficient power control scheme that maximizes the effective capacity of the secondary user, provisioning quality of service while on the same time the communication of the primary user is guaranteed through interference constraints. The specific power allocation scheme uses a policy in which the outage events of the primary user are exploited leading to a significant increase of the secondary user's effective capacity. Moreover, the interference of the primary link to the secondary is taken into account so as to study a more realistic scenario. In order to safeguard primary user's communication, two types of restrictions are considered: the traditional interference power constraint and the proposed inverse signal to interference plus noise ratio constraint. Different scenarios depending on the nature of the constraints (peak/average) are studied and their impact on the performance of the primary and secondary users is investigated. The superiority of the proposed schemes is demonstrated through their comparison with two reference power control schemes. Finally, numerical calculations, validated with simulation results, confirm the theoretical analysis and evaluate the performance of the proposed scheme for all the different scenarios.

Protecting Primary Users in Cognitive Radio Networks with Effective Capacity Loss Constraint

Cognitive radio (CR) in spectrum sharing mode allows secondary user (SU) to share the same spectrum simultaneously with primary user (PU), as long as the former guarantees no harmful interference is caused to the latter. This letter proposes a new type of constraint to protect the PU systems that are carrying delay-sensitive applications, namely the PU effective capacity loss constraint, which sets an upper bound on the maximum effective capacity loss of the PU due to the SU transmission. In addition, the PU effective capacity loss constraint is approximately transformed to the interference temperature (power) constraint, to make it easier to be implemented. As an example, we obtain a closed form expression of the SU effective capacity under the approximated peak interference power constraint and the results of simulations validate the proposed PU protection criterion.