Guangxia Li

Optimal Power Control for Real-Time Applications in Cognitive Satellite Terrestrial Networks

Cognitive satellite terrestrial networks have received considerable attention as a promising candidate to address the spectrum scarcity problem in future wireless communications. When satellite networks act as cognitive users in the networks, power control is a significant research challenge in the uplink case, especially for real-time applications. We propose two optimal power control schemes for maximizing the delay-limited capacity and outage capacity, respectively, which are useful performance indicators for real-time applications. From the long-term and short-term aspects, average and peak power constraints are adopted, respectively, at the satellite user to limit the harmful interference caused to the terrestrial base station. Extensive numerical results demonstrate the impact of interference constraints and channel condition parameters on the performance limits of satellite users.

Pilot Pattern Optimization for Sparse Channel Estimation in OFDM Systems

Compressive sensing (CS) based sparse channel estimation requires optimal pilot patterns, whose corresponding sensing matrices should have small mutual coherences, so as to efficiently exploit the inherent channel sparsity. For the purpose of minimizing the mutual coherence of the sensing matrix, we introduce a new estimation of distribution algorithm (EDA) to optimize the pilot pattern so as to improve the channel estimation performance. The proposed scheme guides the optimization process by building and sampling the probability distribution model of the promising pilot indexes, and approaches the optimal pilot pattern iteratively. The algorithm is able to not only preserve the current best pilot indexes, but also introduce diversity by sampling new ones, and hence is unlikely to trap into local minima and more robust than other methods. Simulation results show that our proposed method can generate sensing matrices with smaller mutual coherences than existing methods, and the corresponding optimized pilot pattern performs well in terms of sparse channel estimation.

Thresholded Smoothed Norm for Accelerated Sparse Recovery

Smoothed ℓ0 norm (SL0) is a fast and complex domain extendible sparse recovery algorithm which is suitable for many practical real-time applications. In this letter, we propose an improved algorithm termed “Thresholded Smoothed ℓ0 Norm (T-SL0)” for accelerating the iterative process of SL0. T-SL0 introduces an iterative efficiency indicator and compares it with a preset threshold in real time to determine whether or not the current iteration should be executed. Through identifying and bypassing low efficient iterations, our approach converges much faster than the original SL0 algorithm. Experimental results are presented to demonstrate that our approach can accelerate SL0 significantly without loss of accuracy.

Energy-Efficient Optimal Power Allocation in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks

This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach’s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.

Wideband spectrum compressive sensing for frequency availability in LEO-based mobile satellite systems

Summary Availability of L/S frequency band is a critical requirement for global communication provided by a constellation of low Earth orbit satellites. This paper investigates the issue of low Earth orbit mobile satellite system frequency availability, where wideband compressed signal detection approach is utilized to obtain active user subbands and their locations which should be avoided during frequency allocation. Compressed sensing can be achieved by exploiting the sparsity of wideband signal spectrum, whose useful frequency support occupies only a small portion of the entirely wide spectrum. An estimate of the signal spectrum can be obtained by using reconstruction algorithms. We define two novel wideband spectrum compressed sensing methods based on discrete cosine transform and Walsh–Hadamard transform, briefly named DCT-WSCS and WHT-WSCS, which significantly improve the performance of spectrum recovery and signal detection compared with conventional discrete Fourier transform-based compressed spectrum sensing method. Furthermore, with the help of inter-satellite links, the scheme of multiple satellites cooperatively sensing with a final decision according to OR and MAJ fusion rules is proposed, which can bring diversity gains. Finally, in-depth numerical simulations under a particular scenario demonstrate the performance of the proposed scheme in the aspect of signal reconstruction precision, detection probability, and processing time. Copyright

Optimal Power Control in Cognitive Satellite Terrestrial Networks With Imperfect Channel State Information

To address the spectrum scarcity in future satellite communications, employing the cognitive technique in the satellite systems is considered as a promising candidate, which leads to an advanced architecture known as cognitive satellite terrestrial networks. Power control is a significant research challenge in cognitive satellite terrestrial networks, especially when the perfect channel state information (CSI) of satellite or terrestrial links is unavailable. In this context, we investigate the impact of imperfect CSI of both desired satellite link and harmful terrestrial interference link on the power control scheme in cognitive satellite terrestrial networks. By adopting a pilot-based channel estimation of satellite link and a back-off interference power constraint of terrestrial interference link, a novel power control scheme is presented to maximize the outage capacity of the satellite user while guaranteeing the communication quality of primary terrestrial user. Extensive numerical results quantitatively demonstrate the effect of various system parameters on the proposed power control scheme in cognitive satellite terrestrial networks with imperfect CSI.

Toward Optimal Selective Beam Allocation with Guaranteed Fairness for Multibeam Satellite Systems

With the increasing of traffic demand in satellite systems, it is essential to maximize the efficiency of resource utilization due to the expense and scarcity of on-board resources. In this paper, we propose a selective beam allocation algorithm with guaranteed fairness for multibeam satellite systems. The algorithm can achieve an acceptable trade-off between the maximum total capacity and the fairness among the spot-beams by allocating the basic power and bandwidth for low priority beams. In addition, the resources allocated for low priority beams can be adjusted flexibly by introducing a lower bound of the capacity. Extensive simulations evaluate the performance of the proposed algorithm. The results demonstrate that the total capacity of the proposed algorithm is 99% of water-filling algorithm. Furthermore, comparing with common selective beam allocation algorithm, the Jain Fairness index is improved by 20%.

Efficient Wideband Spectrum Sensing with Maximal Spectral Efficiency for LEO Mobile Satellite Systems

The usable satellite spectrum is becoming scarce due to static spectrum allocation policies. Cognitive radio approaches have already demonstrated their potential towards spectral efficiency for providing more spectrum access opportunities to secondary user (SU) with sufficient protection to licensed primary user (PU). Hence, recent scientific literature has been focused on the tradeoff between spectrum reuse and PU protection within narrowband spectrum sensing (SS) in terrestrial wireless sensing networks. However, those narrowband SS techniques investigated in the context of terrestrial CR may not be applicable for detecting wideband satellite signals. In this paper, we mainly investigate the problem of joint designing sensing time and hard fusion scheme to maximize SU spectral efficiency in the scenario of low earth orbit (LEO) mobile satellite services based on wideband spectrum sensing. Compressed detection model is established to prove that there indeed exists one optimal sensing time achieving maximal spectral efficiency. Moreover, we propose novel wideband cooperative spectrum sensing (CSS) framework where each SU reporting duration can be utilized for its following SU sensing. The sensing performance benefits from the novel CSS framework because the equivalent sensing time is extended by making full use of reporting slot. Furthermore, in respect of time-varying channel, the spatiotemporal CSS (ST-CSS) is presented to attain space and time diversity gain simultaneously under hard decision fusion rule. Computer simulations show that the optimal sensing settings algorithm of joint optimization of sensing time, hard fusion rule and scheduling strategy achieves significant improvement in spectral efficiency. Additionally, the novel ST-CSS scheme performs much higher spectral efficiency than that of general CSS framework.

Joint power and bandwidth allocation for beam-hopping user downlinks in smart gateway multibeam satellite systems:

To improve the efficiency of on-board resources utilization, two joint power and bandwidth allocation schemes are proposed to optimize power and timeslot allocation for beam-hopping user downlinks of the smart gateway system. By decomposing the two-variable optimization problem into two single variable optimization problems, the joint optimization problem is solved effectively. Moreover, two novel algorithms are proposed to solve the two subproblems. Extensive simulation results demonstrate that the efficiency of resource utilization can be effectively improved with the joint power and bandwidth allocation schemes in contrast with conventional allocation scheme.

A Novel Approach to Wideband Spectrum Compressive Sensing Based on DST for Frequency Availability in LEO Mobile Satellite Systems

In LEO mobile satellite network, the L/S frequency availability is an essential task for global communication but entails several major technical challenges: high sampling rate required for wideband sensing, limited power and computing resources for processing load, and frequency-selective wireless fading. This paper investigates the issue of frequency availability in LEO mobile satellite system, and a novel wideband spectrum compressed signal detection approach is proposed to obtain active primary users (PUs) subbands and their locations that should be avoided during frequency allocation. We define the novel wideband spectrum compressed sensing method based on discrete sine transform (DST-WSCS), which significantly improves the performance of spectrum detection and recovery accuracy compared with conventional discrete Fourier transform based wideband spectrum compressed sensing scheme (DFT-WSCS). Additionally, with the help of intersatellite links (ISL), the scheme of multiple satellites cooperative sensing according to OR and MAJ decision fusion rules is presented to achieve spatial diversity against wireless fading. Finally, in-depth numerical simulations are performed to demonstrate the performance of the proposed scheme in aspect of signal detection probability, reconstruction precision, processing time, and so forth.