Xiaoge Huang

Dynamic suspicious reputation based collaborative sensing in cognitive radio networks

Collaborative sensing is an available technology to increase access opportunity for unused spectrum and avoid interference to the primary users in cognitive radio networks. Due to the complexity of wireless environment and the primary users location is unknown, the sensing performance will be affected by many unfavorable factors such as multipath fading, shadowing, and so on. Especially, some secondary users (SUs), defined as attackers, could cause security problem by reporting false information to other SUs. In this paper, we proposed a Dynamic Suspicious Reputation Scheme to detect attackers in collaborative sensing. In the proposed scheme, a SU with suspicious reputation makes the sensing decision by evaluating the correlation value between its own sensing reports and those from other SUs. If the correlation value exceed a given threshold, the compared SU is regarded as an attacker. The suspicious reputation is updated according to its own sensing reports in each collaborative sensing iteration. Once the suspicious reputation of a SU exceed a certain threshold, the SU is regarded as Malicious Attacker, and removed out the collaborative sensing group. Numerical analysis show that the proposed scheme can achieve a considerable performance improvement compared with a User-centric Misbehavior Detection Scheme for secure collaborative sensing. In addition, we also discuss the affection of the suspicious reputation to the probability of false alarm and detection.

Dynamic cell selection and resource allocation in cognitive small cell networks

We consider a sensing-based power allocation scheme in a cognitive small cell network to maximize the sum rate of each small cell by jointly optimizing both the cell selection, the sensing operation and the power allocation over channels, under the condition of interference to primary users below a certain value. Due to intercell interference and the integer nature of the cell selection, the resulting optimization problems lead to a non-convex integer programming which is NP-hard. In order to deal with the non-convexity, we reformulate the problem to a non-convex power allocation game and use the relaxed equilibria concept, namely, quasi-Nash equilibrium. A sensing-based power allocation optimization algorithm that converges to a quasi-Nash equilibrium is also discussed in this paper. Simulation results show that the proposed approach achieves substantial performance gains with respect to a deterministic approach.

Dynamic base station assignment and resource allocation in MIMO CR small-cell networks

Cognitive radio (CR) is an effective technology to deal with the spectrum scarcity and improve spectrum utilization. The growing small-cell is another effective method to enhance the spectrum efficiency of wireless networks by way of the spatial reuse of frequencies. In this paper, we consider a Multiple Input Multiple Output (MIMO) CR in small-cell networks and propose an efficient algorithm joint optimize both cell selection and resource allocation, to maximize the sum rate of CR users, under the condition of interference to primary users below a certain value. CR users decide to access the channel based on the results from spectrum sensing. The influence of the sensing time is also taken into account, since the longer the sensing time the shorter the data transmission time. To deal with the non-convexity of the optimization problem, we introduce a sensing-based successive convex approximations (SSCA) algorithm to get a locally optimal solution of the non-convex problem. Simulation results show that the proposed SSCA algorithm can achieve a better performance.

Coexistence of Cognitive Small Cell and WiFi System: A Traffic Balancing Dual-Access Resource Allocation Scheme

We consider a holistic approach for dual-access cognitive small cell (DACS) networks, which uses the LTE air interface in both licensed and unlicensed bands. In the licensed band, we consider a sensing-based power allocation scheme to maximize the sum data rate of DACSs by jointly optimizing the cell selection, the sensing operation, and the power allocation under the interference constraint to macrocell users. Due to intercell interference and the integer nature of the cell selection, the resulting optimization problems lead to a nonconvex integer programming. We reformulate the problem to a nonconvex power allocation game and find the relaxed equilibria, quasi-Nash equilibrium. Furthermore, in order to guarantee the fairness of the whole system, we propose a dynamic satisfaction-based dual-band traffic balancing (SDTB) algorithm over licensed and unlicensed bands for DACSs which aims at maximizing the overall satisfaction of the system. We obtain the optimal transmission time in the unlicensed band to ensure the proportional fair coexistence with WiFi while guaranteeing the traffic balancing of DACSs. Simulation results demonstrate that the SDTB algorithm could achieve a considerable performance improvement relative to the schemes in literature, while providing a tradeoff between maximizing the total data rate and achieving better fairness among networks.

Cost-effective interference coordination scheme in high dense small cell heterogeneous network

In this paper, we propose a cost-effective dynamic interference coordination scheme, which jointly optimizes the cooperative small cell base stations (SBSs) association and power allocation over subchannels. Our goal is to maximize the total small cell heterogeneous network utility, which consists of four parts: the rate of the macro cell, the rate of the hotzones, the rate of edge small cells as well as the cost from all SBSs. Coordinated multi-point processing scheme is used to reduce the inter-cell interference while increasing the transmission power. Moreover, User Equipments (UEs) behaviors which affect the state of SBS are also taken into consideration. In order to further improve the utility of the network, we deploy the edge SBSs to serve UEs far from macro base station (MBS) and other SBSs. Simulation results validate the effectiveness of the proposed scheme.

Joint malicious user detection and resource allocation in cognitive radio networks

Cognitive radio is an effective technology to solve the increasing spectrum demands for wireless application by reducing the spectrum based on the sensing results. However, the reliability of each secondary user (SU) is uncertain. In order to improve the chance to access the idle spectrum, some SUs send false sensing results and become malicious users (MUs). Our objective is to design a novel algorithm that joint MUs detection and power allocation to optimize the total utility of the whole cognitive radio network, while meets the requirement of MUs detection accuracy as well as the interference limitation to primary users (PUs). The utility is composed of three parts: the detection probability of MUs, sum-rate and the energy consumption for MUs detection. We formulate the problem into two sub-problems. Firstly, we detect MUs to get the detection probability of MUs and the energy consumption for MUs detection. Sencondly, based on the results from the first step we use the Lagrange dual decomposition method to maximize the sum-rate in the network.

Optimal user power manipulation aided cooperative sensing schemes for cognitive radios

In recent years, cooperative spectrum sensing (CSS) for cognitive radio networks (CRN) have been widely investigated in various research aspects. With an aim to develop easy-to-implement CSS schemes, this paper proposes two optimal strategies to manipulate the transmit powers of the cooperative secondary users (SU) in the CSS intervals. When the joint channel statistics (CS) of the sensing and reporting channels are perfectly known at the fusion center (FC), we first derive a CS aided optimal user power manipulation (CSA-OUPM) scheme in closed-form; when the CS are entirely unknown, a blind user power control method is developed, namely the principal eigenvector aided OUPM (PEV-OUPM). Theoretical analysis and computer simulations verify that the proposed OUPM schemes are capable of potentially allocating more power to the cooperative SUs with better channel condition and hence achieve significantly ameliorated performance.