A Unified QoS and Security Provisioning Framework for Wiretap Cognitive Radio Networks A Statistical

A Unified QoS and Security Provisioning Framework for Wiretap Cognitive Radio Networks a Statistical Queuing Analysis Approach

Abstract: Due to the spectrum-sharing feature of cognitive radio networks (CRNs) and the broadcasting nature of wireless channels, providing quality-of-service (QoS) provisioning for primary users (PUs) and protecting information security for secondary users (SUs) are two crucial and fundamental issues for CRNs. Consequently, in this paper, we establish a unified QoS and security provisioning framework for wiretap CRNs. Specifically, different from the widely used deterministic QoS provisioning method and information-theoretical security protection approach, our established framework, which is built on the theory of statistical queuing analysis, can quantitatively characterize the PU’s QoS and the SU’s security requirements. By adopting the theories of effective capacity and effective bandwidth, we further convert the QoS and security requirements to the equivalent PU’s effective capacity and SU’s effective bandwidth constraints.

Following our developed framework, we formulate the no convex optimization problem, which aims at maximizing the average throughput of SU subject to PU’s QoS requirement, SU’s security constraint, as well as SU’s average and peak transmit power limitations. Then, we adopt the techniques of convex hull and

probabilistic transmission to convert the original non convex problem to the equivalent convex problem and obtain the optimal power allocation scheme through the Lagrangian method. Moreover, we also develop a fixed power allocation scheme which is suboptimal but has low complexity. The simulation results are also provided, which demonstrate the impact of the PU’s QoS and the SU’s security requirements on SU’s throughput as well as the advantage of our proposed optimal power allocation scheme over the fixed power allocation scheme and the conventional security based water-filling policy.

Existing system: Following our established framework, we formulate the non convex optimization problem which aims at maximizing the average throughput of SU subject to PU’s delay QoS requirement, SU’s delay sensitive secrecy constraint as well as SU’s average and peak transmit power constraints. Thanks to the theories of convex hull and probabilistic transmission, we convert the original non convex problem to an equivalent convex problem and obtain the optimal power allocation strategy by using the Lagrangian method. Move over, a suboptimal scheme named fixed power allocation policy is also developed, which only adapts to PU’s delay QoS and SU’s delay-sensitive secrecy requirements.

Proposed system: Different from the widely-used physical-layer security technology, our proposed statistical delay-sensitive secrecy protection approach aims to provide the information secrecy provisioning within the predefined period by utilizing the delay-sensitive feature of applications/services. In other words, we guarantee the eavesdropper will not accumulate enough data to extract the legitimate user’s information before the protected information expires. In this way, not only the delay sensitive secrecy requirement of arbitrarily given application/ service can be characterized, but also the legitimate user is enabled to adapt the resource allocation to the secrecy requirement of the application/service and the network performance can be improved. Based on the theory of statistical queuing analysis, we develop the unified QoS and security protection framework for wiretap CRNs. Advantages:

As keyless secure transmission can be realized by taking advantages of the intrinsic characteristics of wireless channels, physic allayer security technology has been extensively researched in recent years.

The specific research about physical-layer security technology includes artificial noise injection, anti-eavesdropping signal design secure beam forming and precoding scheme, cooperation based secure transmission, as well as security oriented power control and resource allocation. To provide efficient information security protection for CRNs, CR-based physical-layer security techniques attract lots of research attentions.

Disadvantages: our established framework, we formulate the non convex optimization problem which aims at maximizing the average throughput of SU subject to PU’s delay QoS requirement, SU’s delay sensitive secrecy constraint as well as SU’s average and peak transmit power constraints.

Thanks to the theories of convex hull and probabilistic transmission, we convert the original nonconvex problem to an equivalent convex problem and obtain the optimal power allocation strategy by using the Lagrangian method.

Modules: Primary network: We consider a wiretap CRN coexists with a primary network (PN) by sharing the same spectrum band with bandwidth W, as shown. The PN consists of one primary sender (PS) and one primary receiver (PR). The wiretap CRN includes one secondary sender (SS), one secondary receiver (SR), and one secondary eavesdropper (SE). Specifically, the PS and SS transmit information to their corresponding receivers PR and SR, respectively. The SE eavesdrops the information sent by SS and will be interfered by the transmission of the PS-PR link. The channel power gains between PS and PR, PS and SR, PS and SE, SS and PR, SS and SR, as well as SS and SE, which are denoted by hpp, hps, hpe, hsp, hss, and hse, respectively, follow the Rayleigh fading model. We assume that all channel power gains are stationary, ergodic, independent and block fading processes,1 which implies that the channel gains keep unchanged during each

frame, but vary independently from one frame to another. This assumption is widely accepted in existing literatures. Furthermore, we denote the duration of each frame as T and define the network gain vector (NGV).

Signal – to noise plus interference: Moreover, PU’s minimum instantaneous transmission rate requirement can be converted to the equivalent signal-to noise plus interference (SINR) requirement and can also be relaxed to PU’s transmission outage probability constraint.

Currently, the information security protection technologies for wireless communications are mainly categorized by two classes: the cryptography theory based encryption technique and the physical-layer security method. The encryption is usually applied in the high protocol layers. However, physical-layer security approach is built on the information theory for secure data transmissions. As keyless secure transmission can be realized by taking advantages of the intrinsic characteristics of wireless channels, physic allayer security technology has been extensively researched in recent years. The specific research about physical-layer security technology includes artificial noise injection, anti-eavesdropping signal design, and secure beam forming and precoding scheme, cooperation based secure transmission, as well as security oriented power control and resource allocation.

Secondary receiver: We consider a wiretap CRN coexists with a primary network (PN) by sharing the same spectrum band with bandwidth W, as shown. The PN consists of one primary sender (PS) and one primary receiver (PR). The wiretap CRN includes one secondary sender (SS), one secondary receiver (SR), and one secondary eavesdropper (SE). Specifically, the PS and SS transmit information to their corresponding receivers PR and SR, respectively. The SE eavesdrops the information sent by SS and will be interfered by the transmission of the PS-PR link. The channel power gains between PS and PR, PS and SR, PS and SE, SS and PR, SS and SR, as well as SS and SE, which are denoted by hpp, hps, hpe, hsp, hss, and hse, respectively, follow the Rayleigh fading model. We assume that all channel power gains are stationary, ergodic, independent and block fading processes,1 which implies that the channel gains keep unchanged during each

frame, but vary independently from one frame to another. This assumption is widely accepted in existing literatures.

Convex hull : The convex hull and probabilistic transmissions approach employed in this paper, where we mainly focus on the single variable scenario, can also be extended to the multiple variables scenario, such as the multi-user networks. However, different from the inflexion point determined in this paper, we might need to construct the inflexion hyper plane for the multiple variables case. Then, the arbitrary nonconvex optimization problem can be converted to the equivalent convex problem and easily solved by the well known Lagrangian method. Consequently, the utilized convex hull and probabilistic transmissions approach can be regarded as an efficient and unified method for solving the non-convex problem.