Interference-based Optimal Power-efficient Access Scheme for Cognitive Radio Networks (original) (raw)

In this paper, we propose a new optimization-based access strategy of multipacket reception (MPR) channel for multiple secondary users (SUs) accessing the primary user (PU) spectrum opportunistically. We devise an analytical model that realizes the multipacket access strategy of SUs that maximizes the throughput of individual backlogged SUs subject to queue stability of the PU. All the network receiving nodes have MPR capability. We aim at maximizing the throughput of the individual SUs such that the PU's queue is maintained stable. Moreover, we are interested in providing an energy efficient cognitive scheme. Therefore, we include energy constraints on the PU and SU average transmitted energy to the optimization problem. Each SU accesses the medium with certain probability that depends on the PU's activity, i.e., active or inactive. The results show a significant gain of the proposed scheme relative to other well-known access schemes up to $ 44 \% $ in terms of SU throughput.

Resource allocation for multiuser cognitive radio with primary user's cooperation

2011

In this paper, we consider a downlink cognitive radio network (CRN) consisting of one cognitive base station (CBS) and multiple secondary users (SUs) that shares multiple channels with a primary network. The CRN is permitted to access the channel licensed to the primary user (PU) only if the signal to interference plus noise ratio (SINR) on that channel at the PU is higher than a predetermined level. The objective is to maximize the total throughput of the CRN with cooperation from the PU. For this, we propose a centralized efficient near-optimal joint channel and power allocation scheme based on dual optimization method. Specifically, the duality gap between the dual scheme and the optimal scheme is nearly zero as the number of channels gets large, while, in contrast to the exponential complexity of the optimal scheme, the complexity of the dual scheme is only linear in the number of channels. Additionally, a low-complexity sub-optimal two-step scheme is also proposed, the complexity of which is further reduced. Simulation results are obtained to verify the effectiveness of the proposed schemes. It is shown that the dual scheme achieves almost the same total throughput compared with that achieved by the optimal scheme even with small number of channels. Moreover, the performance of the two-step scheme is very close to that of the optimal scheme.

Resource Management in Spectrum Sharing Cognitive Radio Networks with Probabilistic Interference Constraints

the present study attempted to investigate the resource management in spectrum sharing cognitive radio networks where the transmit power and modulation level of secondary users (SUs) are adapted using iterative Foschini- Miljanic algorithm. We assumed that the SUs and primary user (PU) could use the same spectrum band, simultaneous to that of originally allocated to PU. The aim was to investigate the problem of minimizing the overall transmission power of SUs, while keeping the probabilistic interference and power budget below the specified thresholds. The main advantage of using probabilistic interference constraint is that it does not require the instantaneous feedback channel between SU transmitters (SUTs) and PU receiver (PUR). Furthermore, allocated power to SUs was first calculated using mentioned algorithm. Second, the signal to noise ratio (SNR) for all SUs was calculated based on allocated power and channel fading gains and then modulation level can be obtained based on calculated SNR and target bit error rate (BER). Numerical and comparison results representing the efficiency of the proposed network are also provided

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