Energy-Efficient Cooperative Spectrum Sensing in Cognitive Satellite Terrestrial Networks

Abstract

Having the ability to provide seamless coverage and alleviate the frequency scarcity, the cognitive satellite terrestrial network becomes a promising candidate for future communication networks. In the cognitive network, spectrum sensing plays an important role in detecting the channel state for opportunistic utilization, where cooperative spectrum sensing is employed to improve the sensing performance. Additionally, it is critical for battery-powered satellite mobile terminals to diminish energy consumption costs. In this regard, this paper proposes a novel sensing-based cognitive satellite terrestrial network (SCSTN), which integrates the cognitive satellite terrestrial network with the distributed cooperative spectrum sensing network. Specifically, we focus on energy-efficient cooperative sensing in the SCSTN, which maximizes the energy efficiency (EE) of the cognitive satellite network by a tradeoff between the average throughput and the average energy consumption. In the SCSTN, the energy detection threshold of the sensing node and the rule threshold of fusion affect the average throughput and the average energy consumption. Hence, the objective of this paper is to identify the energy detection threshold of the sensing node and the rule threshold of fusion to achieve the maximum EE. We first study the EE formulation of the rule threshold of fusion when the energy detection threshold of the sensing node is given, and transform the ratio-type objective function of EE into a parametric formulation. Subsequently, by exploring the relationship between the two formulations and making use of the monotonicity of the parametric formulation, an algorithm to obtain the optimal rule threshold of fusion for the original problem is developed. Furthermore, we study the optimal formulation of the energy sensing threshold of the sensing node and discuss the effect of the sensing duration and the number of distributed cooperative terminals on the EE. Lastly, the performance of the proposed method is evaluated through numerical simulation results.

Evaluation of Primary User Power Impact for Joint Optimization of Energy Efficiency in Cognitive Radio Networks

“…Some classic works [3,4] proposed the allocation of a suitable sensing time for SUs to achieve the optimization of the EE function, whereas other recent studies [18,20] defined EE as throughput per unit of energy consumption (i.e., unity: bit/Hz/Joule). Then, they took the number of SUs into consideration [18] and adapted either the optimal rule threshold of fusion or the optimal energy detector threshold [20] to further improve the performances. The authors of [1,6] studied the sensing-EE tradeoff.…”

“…In (20), ε is defined as the threshold of active probability according to the basic hypothesis of spectrum sensing: the PU's presence is not dominant in the band, and there are some opportunities to reuse the band. Then, the first lower-bound condition of α 0 is implied as (20):…”

“…In addition, EE and sensing performance are also interconnected, and the sensing metrics and thresholds must be strictly respected while designing the CRN system with the aim of achieving a higher EE. Regarding this complex optimization problem, more detailed analyses of a PU's transmission power and its influences on SUs' sensing and transmission phases are presented in Section 1 by referring to some recently proposed methods [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Table 1 lists some common abbreviations that are used in this paper.…”

On the Security of LEO Satellite Communication Systems: Vulnerabilities, Countermeasures, and Future Trends

“…By applying sophisticated relaxation and approximation schemes, they significantly reduced the complexity of the related optimization problem. The authors of [145] proposed a cognitive satellite-terrestrial network relying on a distributed cooperative spectrum sensing technique by striking a tradeoff between the average throughput and the average energy consumption under specific interference constraints.…”

“…Cognitive radio Strikes a trade-off between the average throughput and the average energy consumption under specific interference constraints in SAGINs considered. [145] 2017 Interference between LEO and GEO SCSs Modulation Coding Employs the angle between LEO and GEO SCSs to choose the modulation and coding scheme for interference mitigation. [149] 2018 Beam drifting Proposes tilting the direction normal of phased array antennas of LEO satellites to achieve the avoidance of the interference between LEO and GEO satellites.…”