Development of Adaptive Sensing Algorithm for Minimizing Energy and Bandwidth Consumption in Cooperative Spectrum Sensing Technology

Optimized consumption of energy and bandwidth is crucial for efficient utilization of the limited electromagnetic spectrum for telecommunication purposes. Cognitive radio is one of the dynamic spectrum management applications with numerous benefits related to the management of available spectrum. But it has the challenge of high energy and bandwidth usage when the cooperative scheme of spectrum sensing is applied for accurate sensing. In this paper, an adaptive spectrum sensing algorithm was developed to minimize energy and bandwidth consumption in cognitive radio spectrum sensing while ensuring accurate spectrum sensing. The adaptive algorithm was developed based on the signal-to-noise ratio conditions of the channel. Results reveal that the energy and bandwidth usage by the cooperative spectrum sensing can be significantly reduced without negatively affecting the performance and detection of the cognitive radio in varying noisy condition

A study on social-based cooperative sensing in cognitive radio networks

A cognitive radio (CR) is an intelligent radio that reuses frequency band based on dynamic spectrum access (DSA). CR implements spectrum sensing to detect primary users' (PU) presence, and exploits available spectrum without interfering PU. In contrast with local spectrum sensing, cooperative sensing which is implemented by multiple CRs, is more efficient and effective generally. Previous work on cooperative spectrum sensing in cognitive radio (CR) assumes a default mode that CRs are willing to cooperate for others unconditionally. While this situation does not always hold, the requested CR might reject the cooperation request due to its insufficient energy, or security concerns. In this thesis, we propose a social-based cooperative sensing scheme (SBC) that exploits social ties of CRs on their cooperative sensing. Simulation results show that SBC fulfills improved sensing quality, and the sensing performance of CRs correlate to the social degree and social network topology

Some MIMO applications in cognitive radio networks

In the last decade, the wireless communication technology has witnessed a rapid development, which led to a rapid growth in wireless applications and services. However, the radio spectrum resources scarcity resulting from using the traditional methods of fixed spectrum resources allocation has potential constraints on this wireless services rapid growth. Consequently, cognitive radio has been emerged as a possible solution for alleviating this spectrum scarcity problem by employing dynamic resource allocation strategies in order to utilize the available spectrum in a more efficient way so that finding opportunities for new wireless application services could be achieved. In cognitive radio networks, the radio spectrum resources utilization is improved by allowing unlicensed users, known as secondary users, to share the spectrum with licensed users, known as primary users, as long as this sharing do not induce harmful interference on the primary users, which completely entitled to utilize the spectrum. Motivated by MIMO techniques that have been used in practical systems as a means for high data rate transmission and a source for spatial diversity, and by its ease implementation with OFDM, different issues in multi-user MIMO (MU-MIMO) in both the uplink and downlink in the context of cognitive radio are studied in this thesis. More specifically, in the first thrust of this thesis, the spectrum spatial holes which could exist in an uplink MU-MIMO cell as a result of the possible free spatial dimensions resulted from the sparse activity of the primary users is studied; a modified sensing algorithm for these spectrum spatial holes that exploit both the block structure of the OFDM signals and the correlation of their activity states along time are proposed. The second thrust is concerned with cognitive radio relaying in the physical layer where the cognitive radio base station (CBS) relays the PU signal while transmitting its own signals to its SUs. We define secondary users with different priorities (different quality of service requirements); the different levels of priority for SUs are achieved by a newly proposed simple linear scheme based on zero forcing called Hierarchal Priority Zero Forcing scheme HPZF

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing.  The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing.  This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties.  The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing