Abstracts:We investigate use of massive number of antennas at cognitive base station (BS) in reducing interference caused to primary users (PUs) under incomplete and/or imperfect channel state information (CSI) without deteriorating sum spectral efficiency of cognitive users (CUs). We derive analytical expressions for complement of interference outage probability with incomplete and/or imperfect CSI. These novel expressions provide ballpark number for the amount of back-off required to meet interference constraint. We deduce new expressions for sum spectral efficiency of CUs with MR beamforming under incomplete and/or imperfect CSI. We prove that under imperfect CSI, by deploying more antennas at cognitive BS, interference outage probability can be reduced while keeping sum spectral efficiency of CUs fixed. And larger number of PUs can be accommodated in the network while keeping interference outage probability at PUs and sum spectral efficiency of CUs fixed. Furthermore, outage probability reduces as number of channels <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> to which CSI is available increases, since the constraint is violated less often. And by deploying more antennas at cognitive BS, sum spectral efficiency of CUs can be kept fixed even if <inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> decreases. Impact of spatial correlation on outage probability at PUs and sum spectral efficiency of CUs is also elucidated.

Abstracts:This paper reports a relative performance analysis of decode-and-forward (DF) and amplify-and-forward (AF) relaying in a multi-antenna cooperative cognitive radio network (CCRN) that supports device-to-device (D2D) communications using spectrum sharing technique in cellular network. The system model considers cellular system as primary users (PUs) while Internet of Things Devices (IoDs), involved in D2D communications, as secondary system. The devices access the licensed spectrum by means of the cooperation in two-way primary communications. Furthermore, IoDs are energized through energy harvesting (EH) of PU radio frequency (RF) signals, using simultaneous wireless information and power transfer (SWIPT) protocol. Closed form expressions of the outage probability for both cellular and D2D communications are derived and the impact of various design parameters for both AF and DF relaying techniques are studied. Based on the simulation results, it is found that the proposed spectrum sharing protocol outperforms by 209&#x0025; and 49&#x0025; for DF relaying and AF relaying schemes, respectively. It is also observed that DF relaying performs better in term of peak energy efficiency (EE) at low transmit power while AF relaying at high transmit power.

Abstracts:The Industrial Internet of Things (IIoT) has been recognised as having the potential to benefit a range of industrial sectors substantially. However, widespread development and deployment of IIoT systems are limited for some reasons, the most significant of which are a shortage of spectrum resources and network security issues. Given the heterogeneity of IIoT devices, typical cryptographic security techniques are insufficient since they can suffer from challenges including computation, storage, latency, and interoperability. This paper presents a physical layer security analysis of the underlying cognitive radio networks for IIoT. Through consideration of the spectrum, IIoT devices can opportunistically utilise the primary spectrum, thereby improving spectrum efficiency and allowing access by an increased number of devices. Specifically, we propose two cognitive relay transmission (CRT) schemes, optimal single CRT (O-SCRT) and multiple CRT (MCRT), to improve transmission reliability further. Since it is challenging to obtain channel state information in the wiretap link, we provide a sub-optimal single CRT scheme and derive closed-form expressions of security outage probability by invoking both selection combination and maximal ratio combination techniques at the eavesdropper. To provide a benchmark, the round-robin single CRT scheme is also analyzed. Simulation results are provided to verify our analysis and show that O-SCRT provides the best system security outage performance.

Abstracts:Direct spread spectrum communication has the advantages of strong anti-jamming ability and low probability of interception, which plays an essential role in both civil and military communications. The detection of direct sequence spread spectrum (DSSS) signal becomes very difficult because of its low power spectral density. In this paper, the detection of DSSS signals under non-cooperative conditions is carried out based on the classification technology of deep learning. Firstly, a detection scheme based on convolutional neural network (CNN) is proposed, in which the neural network is used to learn the features of DSSS signal and noise automatically without extracting the features in advance. In addition, we also propose a hybrid CNN-correlation (CORR)-based detection scheme in order to reduce the computational complexity. In this scheme, the autocorrelation of the received signal is truncated and used as the input of the neural network for training and inference. A large number of simulation results show that the detection performance of the proposed two schemes is significantly better than the traditional autocorrelation-based detection method in various scenarios.

Abstracts:Index modulation recognition (IMR) at secondary user (SU) receiver is a challenging topic for MIMO-OFDM cognitive radio network (MIMO-OFDM-CRN) with index modulation scheme, in order to make SU preferably adapt to the communication environment by adjusting own parameters. For index modulated signals, this paper proposes an effective IMR algorithm based on projection residual analysis (PRA). The proposed algorithm is suitable for various types of modulation such as spatial index modulation (SIM), frequency index modulation (FIM) and space-frequency index modulation (SFIM). Firstly the sparse structure of primary user (PU) signal is detected through removing the joint sparsity of signal matrix. Secondly, according to the detected sparse structure, the problem of whether the signal is index modulation (IM) or unindexed modulation (UIM) is addressed by projection residual analysis with <inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>-test. The hypothesis test judges whether the projection residual power of the received signal is significant compared with that of the UIM case, where the projection residual is obtained through projecting the subcarrier signals in the current index modulation symbol into the subspace of those in the previous symbol. The distribution of the test statistic is derived theoretically under UIM case. Thirdly, combining the detected sparse structure and the results of <inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>-test, the index modulation mode of PU signal is identified. Simulation results verify the performance of the proposed algorithm in terms of bit error rate (BER) and recognition rate, respectively.

Abstracts:Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Abstracts:Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners&#x2019; raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training completion time, it is essential to recruit sufficient participants. Meanwhile, the data owners are rational and may be unwilling to participate in the collaborative learning process due to the resource consumption. To address the issues, there have been various works recently proposed to motivate the data owners to contribute their resources. In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for incentivizing data owners to participate in FL training process. In particular, we first present the fundamentals and background of FL, economic theories commonly used in incentive mechanism design. Then, we review applications of game theory and economic approaches applied for incentive mechanisms design of FL. Finally, we highlight some open issues and future research directions concerning incentive mechanism design of FL.

Abstracts:The prompt expansion of the Internet of Things (IoT) and its wide application in smart homes and transportation has brought tremendous convenience to people&#x2019;s lives. However, the increase of IoT devices has also brought huge security problems, threatening people&#x2019;s information and property security. This paper designs a new anomaly detection architecture based on the concept of the &#x201C;Internet of intelligence&#x201D;. It is a general architecture that can be applied to different IoT anomaly detection methods. The architecture effectively combines the blockchain and the IoT anomaly detection method, which can overcome the problems of data resource sharing and collective learning. At the same time, we propose a novel method for detecting abnormal HTTP traffic in IoT. It combines clustering and Autoencoder method to efficiently and exactly detect abnormal HTTP traffic in IoT devices. In addition, we propose an optimized feature extraction method, which is favorable to enhance the detection effect. Simulation results show the proposed architecture and method can enhance the detection performance of abnormal HTTP traffic in IoT and address the challenges of existing approaches.

Abstracts:Many of the machine learning tasks rely on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) entailing huge communication overhead. To overcome this, federated learning (FL) has been suggested as a promising tool, wherein the clients send only the model updates to the PS instead of the whole dataset. However, FL demands powerful computational resources from the clients. In practice, not all the clients have sufficient computational resources to participate in training. To address this common scenario, we propose a more efficient approach called hybrid federated and centralized learning (HFCL), wherein only the clients with sufficient resources employ FL, while the remaining ones send their datasets to the PS, which computes the model on behalf of them. Then, the model parameters are aggregated at the PS. To improve the efficiency of dataset transmission, we propose two different techniques: i) increased computation-per-client and ii) sequential data transmission. Notably, the HFCL frameworks outperform FL with up to 20&#x0025; improvement in the learning accuracy when only half of the clients perform FL while having 50&#x0025; less communication overhead than CL since all the clients collaborate on the learning process with their datasets.

Abstracts:In Cognitive Radio (CR) networks, Primary User (PU) and Secondary User (SU) coexist to efficiently share the spectrum. PU has the right to access its dedicated channel at any time, while SU, operating in an opportunistic mode. can access only when PU is absent. Thus, SU should continuously monitor the channel to avoid any interference with PU when transmitting. Several factors, such as fading and shadowing adversely impact the PU SNR at the SU receiver making the Spectrum Sensing (SS) process more challenging. Recently, Intelligent Reflecting Surface (IRS) has been proposed to control the propagation channel for wireless systems. Introducing IRS in CR networks impacts the SS performance because of altering the channel. In this paper, we investigate the effect of deploying IRS on the SS by considering two scenarios: in (S1) the IRS is configured to enhance the PU signal at SU, while in the second scenario (S2), the IRS is configured to assist the Primary Receiver (PR). First, we highlight several important challenges and research directions. Then, we derive the analytical average detection probability for both (S1) and (S2). Results show that deploying IRS can significantly enhance SS even when the IRS is deployed to assist the PR.