This paper studies the problem of secure transmission in dual-hop cooperative networks with untrusted relays, where each relay acts as both a potential helper and an eavesdropper. A security-aware relaying scheme is proposed, which employs the alternate jamming and secrecy-enhanced relay selection to prevent the confidential message from being eavesdropped by the untrusted relays. To evaluate the performance of the proposed strategies, we derive the lower bound of the achievable ergodic secrecy rate (ESR), and conduct the asymptotic analysis to examine how the ESR scales as the number of relays increases.

No abstract

The Internet of Things (IoT) depicts a bright future where any devices having sensorial and computing capabilities can interact with each other. Among all existing technologies, the techniques for the fifth generation (5G) systems are the main driving force for the actualization of IoT concept. However, due to the heterogeneous environment in 5G networks and the broadcast nature of radio propagation, the security assurance against eavesdropping is a vital yet challenging task. In this paper, we focus on the transmission design for secure relay communications in IoT networks, where the communication is exposed to eavesdroppers with unknown number and locations. The randomize-and-forward (RF) relay strategy specially designed for secure multi-hop communications is employed in our transmission protocol. First, we consider a single-antenna scenario where all the devices in the network are equipped with the single antenna. We derive the expression for the secrecy outage probability of the two-hop transmission. Following this, a secrecy-rate-maximization problem subject to a secrecyoutage-probability constraint is formulated. The optimal power allocation and codeword rate design are obtained. Furthermore, we generalize the above analyses to a more generic scenario where the relay and eavesdroppers are equipped with multiple antennas. Numerical results show that the proper use of relay transmission can enhance the secrecy throughput and extend the secure coverage range.

No abstract

No abstract

With the rapid development of wireless communications technologies, radio spectrum has become a type of extremely scarce resources in meeting the increasing demands for broadband wireless services. However, the traditional static spectrum allocation policy leads to severe spectrum underutilization and spectrum shortage problems. The cognitive radio (CR) technology can detect the occupancy of the spectrum and enable the dynamic spectrum access (DSA) to fill the spectrum hole caused by the static allocation policy, and thus has been widely recognized as an efficient approach to solve the above problems. The distributed cognitive wireless network (CWN), which does not have central entities, is one of the major networking architectures applying the CR technology. Correspondingly, the design of DSA in distributed CWNs is crucial, yet challenging, to increasing the utilization efficiency of the wireless spectrum with dynamically-varying occupancy statuses. In this article, we present a survey on DSA protocols for distributed CWNs. In particular, we first address the challenges in the design and implementation of distributed DSA protocols. Then, we categorize the existing distributed DSA protocols based on different criteria, such as spectrum sharing modes, spectrum allocation behaviors, spectrum access modes, the usage of common control channel, spectrum usage strategies, the number of radios, and spectrum sensing techniques. We also discuss the advantages and disadvantages of each category under diverse classification criterion. Moreover, we make a comprehensive survey of the state-of-the-art distributed DSA protocols using different spectrum access modes, which can be categorized into contention-based, time-slotted, and hybrid protocols. Through the study, we find out that most of distributed DSA protocols fall into the contention-based and hybrid protocols. In addition, the ongoing standardization efforts are also reviewed. Finally, several open research issues for the distributed DSA protocols are presented, such as spectrum handoff based protocols, spectrum prediction based protocols, adaptation of the spectrum-sharing modes, protocols with cooperative spectrum sensing, as well as distributed collision avoidance mechanisms.

5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly-outage and sudden hype in traffic activity that may result in congestion-detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)'s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes L-layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)-notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector.

No abstract