Qixun Zhang

Efficient Mesh Division and Differential Information Coding Schemes in Broadcast Cognitive Pilot Channel

Driven by the inevitable trends of heterogeneous network convergence and cooperation technology innovations have been put forward to improve the spectrum usage efficiency, such as the Cognitive Radio (CR) technology. As one of the candidate solutions for heterogeneous network information delivery for dynamic spectrum sharing in CR environment, the Cognitive Pilot Channel (CPC) techniques are proposed, which greatly improve the efficiency and accuracy of network information delivery to User Equipments. Based on the assumption that the geographical region is divided into meshes and covered by different Radio Access Technologies (RATs), an Efficient Mesh Division (EMD) scheme is brought forward by taking into account both the error probability parameter in the Global Position System localization shift scenario and the information loss ratio parameter in the multi-RATs overlapped scenario. Besides, the impacts of these parameters to the EMD scheme are analyzed and evaluated by using the Analytic Hierarchy Process and Grey Relational Analysis algorithms. Furthermore, in order to improve the efficiency of network information coding among different meshes, the Differential Information Coding (DIC) scheme is put forward, which includes the Homogeneous Meshes Grouping scheme that is based on the frequency occupancy graph and the basic mesh selection strategy with the most popular commonality using image processing techniques. In virtue of the DIC scheme, the duplicate network information in homogeneous meshes is greatly reduced and information coding efficiency is improved by quantizing only the differential information against the basic mesh. Finally, contributions of the proposed EMD and DIC schemes reside in the improvements of the accuracy and efficiency of heterogeneous network information delivery via broadcast CPC channel, which are proved by numerous results.

A Novel Cooperative Sensing Based on Spatial Distance and Reliability Clustering Scheme in Cognitive Radio System

In this paper, a novel cooperative sensing based on local spatial distance sensing and reliability clustering scheme is proposed. Based on the signal propagation model, the distance between primary user and secondary user follows the logarithmic normal distribution. Then we propose a distance threshold to detect the primary user and put forward a reliability-based clustering cooperative sensing scheme, taking into account the accuracy and throughput. In time domain, different locations may have different sensing periods. Through theoretical formulations, we can conclude that the cluster numbers will influence the system detection performance. Compared with the conventional energy detection, the performance of cooperative sensing has been improved. Based on the accurate fixed position of primary users, the system throughput has been increased.

Efficient Coding Scheme for Broadcast Cognitive Pilot Channel in Cognitive Radio Networks

With the trend of technology innovations in recent years, network heterogeneity and inefficient spectrum usage are the great challenges in Cognitive Radio Networks (CRNs). As one of the solutions for efficient heterogeneous network information delivery in CRNs, a common broadcast signaling channel named Cognitive Pilot Channel (CPC) is proposed with its large coverage and easy implementation characteristics in contrast to usually inefficient and time-consuming spectrum sensing techniques. In order to improve the accuracy of network information delivery, the geographical regions are divided into small meshes and the network information in each mesh is broadcast one by one. This paper proposes an efficient coding scheme for broadcast CPC, called Differential Mesh Information Coding (DMIC), to reduce the redundancy of similar network information among different meshes. The strategies of choosing the basic mesh with popular commonality and quantizing the differential information among meshes are also proposed and proved by numerous results.

An Architecture for Cognitive Radio Networks with Cognition, Self-Organization and Reconfiguration Capabilities

Cognitive radio is considered to be a key technology for future heterogeneous networks. Cognitive radio network is an evolution of the cognitive radio by extending the radio link scope to network scope, and is defined as a network that can observe its environment, make decisions based on the observations, and then reconfigure according to the decisions, all while taking into account the end-to-end goals. This paper proposes a high level abstraction of the cognitive radio network architecture. The operation of the proposed architecture is guided by the end-to-end goals. The proposed architecture consists of four components: end-to-end goals management, cognition management, self-organization management, and reconfiguration management. The proposed architecture provides the functionality to manage these components, enable communication between them, and facilitate the interfaces between cognitive radio network and its surrounding environment. In order to demonstrate the functionality of the proposed architecture, we present a use case of ubiquitous wireless access services and show that the proposed architecture enables ubiquitous connectivity with harmonized networks and integrated services.

Outage Performance of Cognitive Relay Networks with Primary User's ISR Constraint

In the underlay spectrum sharing systems, secondary users (SUs) are allowed to transmit their data in the licensed spectrum band when primary users(PUs) are also transmitting, as long as the transmission of SUs do not interfere PUs communications. In cognitive relay networks, the source and relay nodes both need to tune their transmit power to mitigate the interference to PU. In this paper, we investigate the outage performance of cognitive relay networks with PUs interference to signal ratio (ISR) constraint, where both average and peak ISR constraint are considered. Finally, We derive the exact outage probability in the scenario without cooperation and the upper bound of outage probability in the scenario with cooperation.

Topology Reconfiguration in Cognitive Radio Networks Using Ant Colony Optimization

Considering the inevitable trends for heterogeneous network convergence and self-adaptation ability, Cognitive Radio Network (CRN) concept has been proposed with some essential characteristics to achieve global end-to-end goals. CRNs are composed of cognitive devices which have the capable of changing network configurations based on the dynamic environment. This capability opens up the possibility of designing flexible and dynamic topology control strategies with the purpose of opportunistically reusing idle licensed spectrum and effectively achieving data transmission. This work focuses on the problem of designing effective topology reconfiguration algorithm to offer optimal routing solutions. We analyze the topology control for CRNs finding the topology problem can be formularized as a multi-objective optimization problem. In this context, as an intelligent technology for complex multi-objective optimization, the ant colony optimization (ACO) techniques are applied in topology reconfiguration for optimal decision making in this paper. Finally, the reconfiguration algorithm is simulated, and the simulation results with detailed are analyzed.

Joint Power Control and Scheduling Strategies for OFDMA Femtocells in Hierarchical Networks

With the increasing demands for high data rate applications with high quality of service in the next generation networks, OFDMA based femtocell technology is a promising solution for indoor coverage extension and network capacity boosting with its automatic and easy deployment features in contrast to the high CAPital EXpenditure (CAPEX) and OPerating EXpense (OPEX) investments for macrocell deployments and operations. Lots of researches have been done on interference mitigation and resource allocation in femtocell networks. However, little attention has been paid to the analysis of the upper bound of macro/femtocell hierarchical network capacity by using the joint femtocell scheduling and optimization strategies. Therefore, this paper has formulated the problem by optimizing the weighted sum capacity of the macro/femtocell hierarchical network with two levels of solutions: one is the optimal solution; the other is a suboptimal local solution, which is raised to approximate the optimal setting with reduced complexity. And a distributed Soft Frequency Reuse (SFR) based approach named Soft Control (SC) is proposed for the fully localized solution. Simulation results verify that the proposed suboptimal and distributed solutions can provide the good performance and approximate the optimal solution with low complexity, with only small loss of the weighted capacity.

A Novel Algorithm to Optimize Sampling Rate for Compressed Sensing

The fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge for the radio environment cognition. Traditional spectrum sensing technique is neither efficient nor necessary, wasting the spectrum access opportunities on the vacant spectrum holes of primary users (PUs). Considering the sparse signal feature, a novel compressed sensing technique is proposed by using the minimal sampling rate to detect spectrum holes, which is more efficient than the Nyquist sampling rate and traditional compressed sampling rate that is required to reconstruct the original signal. The proposed compressed sensing process is divided into two stages called approaching stage and monitoring stage. The first stage is to gradually approach the minimal sampling rate required to achieve the spectrum detection performance by using the feedback mechanism. And the second stage is to monitor the status of PU according to the threshold using the sampling rate from the first stage. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm. Numerous results show that the proposed compressed sensing technique can reduce the sampling rate to 35%, with acceptable detection probability over 0.9.

A location-aware discrete region approach for spatial access opportunity analysis in cognitive radio networks

Spectrum access is an important research topic in cognitive radio networks, in which secondary users (SUs) manage to utilize the licensed spectrum bands of primary users opportunistically. However, there is a tradeoff between the access opportunities for SUs and communication constraints from primary networks. Moreover, considering the interplay among SUs sharing the limited spatial access resources, how much the access opportunity a SU can derive become complex to determine. In this paper, to protect the primary users(PUs) from the interference caused by SUs, a location-aware discrete region model for spatial spectrum access opportunities of SUs is highlighted, in which the exact value of access opportunity is quantitative as the expected allowed-transmitting number (EATN) of SUs surrounding a PU. Considering the impact of SUs transmission probability and mobility, a probabilistic analysis and a finite-state Markov chain is undertaken. Simulation results show the change rules of SUs access opportunities with parameters of primary and secondary networks, which is full of conductive in determining the deployment and operation of secondary network.

ANN Based Joint Coverage Self-optimization for Random-Deployed Cognitive Femtocell Networks

Driven by the emerging demands of enhancing the indoor coverage, the network capacity and users’ service experience, small scaled femtocell networks will be implemented extensively in home, office and hotspots, which can provide substantial benefits for both operators and users, such as the capital expenditures (CAPEX) and the operational expenditures (OPEX) reduction, the easy plug-and-play (PnP) deployment. Due to users’ random-deployed manner indoors, femto base station (FBS) should have cognitive abilities, such as the position awareness, the automatic parameter tuning and the learning ability, in order to self-optimize its indoor coverage and minimize the unexpected interference outdoors. In contrast to the traditional static power allocation algorithm, the FBS has applied the dynamic power allocation scheme to minimize the interference to macro base station (MBS) and macro user equipment (MUE) by considering the path loss under different wall penetration conditions. Based on the position awareness ability, the optimal coverage radii for FBS indoors are proposed and proved by closed-form formulas at typical positions, which were not considered before. To further enable the intelligent coverage self-optimization for the random-deployed FBS indoors, a joint dynamic power allocation and antenna pattern selection scheme has been proposed based on the Artificial Neural Network (ANN) with training and self-tuning abilities. Numerous results prove both the effectiveness and the accuracy of the proposed coverage self-optimization scheme for the random-deployed femtocell networks.