Presents the table of contents for this issue of the publication.

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

Learning from massive network data to produce cognitive knowledge for efficient resource management in 5G and beyond 5G (B5G) is still challenging. We are delighted to introduce the readers to this special section of the IEEE Transactions on Cognitive Communications and Networking (TCCN), which aims at exploring recent advances and addressing practical challenges in the intelligent resource management

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

The development of 5G-satellite integrated networks suffers from limited spectrum resources. In this paper, we investigate how to assign spectrum intelligently based on dynamical cooperation among primary users (PUs) and cognitive users (CUs) for 5G-satellite integrated networks. Firstly, we propose the cooperative transmission ability model . The effective time for users to communicate with satellites

The autonomous placement of Virtual Network Functions (VNFs) is a key aspect of Zero-touch network and Service Management (ZSM) in Fifth Generation (5G) networking. Therefore, current orchestration frameworks need to be enhanced, accordingly. To address this need, this work presents an Adapted REinforcement Learning VNF Performance Prediction module for Autonomous VNF Placement, namely AREL3P. Our

A 5G network is the key driving factor in the development of vehicle-to-vehicle (V2V) communication technology, and V2V communication in 5G has recently attracted great interest. In the V2V communication network, users can choose different transmission modes and power levels for communication, to guarantee their quality-of-service (QoS), high capacity of vehicle-to-infrastructure (V2I) links and ultra-reliability

Recently, the installation of 5G networks offers a variety of real-time, high-performance and human-oriented customized services. However, the current laying 5G structure is unable to meet all of the growing communication needs by these new emerging services. In this paper, we propose a DQL (Deep Q-learning Network) based intelligent resource management method for 5G architecture, to improve the quality

In this paper, the problem of dynamic spectrum sensing and aggregation is investigated in a wireless network containing ${N}$ correlated channels, where these channels are occupied or vacant following an unknown joint 2-state Markov model. At each time slot, a single cognitive user with certain bandwidth requirement either stays idle or selects a segment comprising ${C}$ ( ${C} < {N}$ ) continuous

Ultra-dense networks (UDN) are considered as one of the key technologies for advancing the widespread deployment of 5G networks. To provide continuous and reliable connectivity to user devices and to achieve the integrity and authenticity of communication, UDN needs to address the challenges related to authentication and authorization of information. Existing authentication and key agreement (AKA)

This paper investigates the cooperative transmission and resource allocation in cloud based integrated terrestrial-satellite networks, where a resource pool at the cloud acts as the integrated resource management and control center of the entire network. Considering the operator offers two levels of services of different quality of service (QoS) and price, we formulate a two-layer game based resource

The ability to manage the distributed functionality of large multi-vendor networks will be an important step towards ultra-dense 5G networks. Managing distributed scheduling functionality is particularly important, due to its influence over inter-cell interference and the lack of standardization for schedulers. In this paper, we formulate a method of managing distributed scheduling methods across a

Cognitive Radio (CR) technology enables secondary users (SUs) to opportunistically access unused licensed spectrum owned by primary users (PUs). It has the potential to significantly enhance communication capacity, which is very critical to the next-generation wireless network design and has attracted intensive attention. One of the key issues in CR communications is to detect spectrum availability

With the rapid advance of smart wireless technologies, a plethora of human behavioral data are generated in 5G networks, which is reported capable to improve network performance by leveraging intelligent channel resource allocation through big data analytics. However, what information can be extracted for the network mobility management, how to exploit the knowledge for resource allocation and to meet

Service-oriented 5G mobile systems are commonly believed to reshape the landscape of the Internet with ubiquitous services and infrastructures. The micro-services architecture has attracted significant interests from both academia and industry, offering the capabilities of agile development and scale capacity. The emerging mobile edge computing is able to firmly maintain efficient resource utility

Machine learning will play a major role in handling the complexity of future mobile wireless networks by improving network management and orchestration capabilities. Due to the large number of parameters that can be monitored and configured in the network, collecting and processing high volumes of data is often unfeasible or too expensive at network runtime, which calls for taking resource management

Software Defined Radio (SDR)-enabled cognitive radio network architectures are expected to play an important role in the future 5G networks. Despite the increased research interest, the current implementations are of small-scale and provide limited functionality. In this paper, we contribute towards the alleviation of the limitations in SDR deployments by developing and evaluating a resource allocation

In this paper, a software-defined networking (SDN) based adaptive transmission protocol (SDATP) is proposed to support applications in fifth generation (5G) communication networks. For time-critical services, such as machine-type communication services for industrial automation, high reliability and low latency are required. To guarantee the strict service requirements, a slice-level customized protocol

The increasing traffic demand in cellular networks has recently led to the investigation of new strategies to save precious resources like spectrum and energy. Direct device-to-device (D2D) communication becomes a promising solution if the two terminals are located in close proximity. In this case, the D2D communications should coexist with cellular transmissions, so they must be carefully scheduled

As cellular networks get widely deployed, mobiles generate enormous amount of signaling data during every call and session. These signaling data contains rich location information. If at the network side, we can accurately locate large amounts of users using the signaling data, this will present opportunities for many novel applications, e.g., assisting wireless operators to troubleshoot the network

For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it is important to predict future long-term performance—e.g., the cumulative sum of time-varying data rates—proactively to avoid making myopic decisions. However, this study experimentally notices that a time-variation in the

Selection of a cognitive radio channel in cognitive-radio vehicular ad hoc network is a challenging task; it requires periodic sensing of cognitive radio channels by vehicles. This makes vehicles to spend valuable time in sensing that could have been used for data transmission. Road side units may help the vehicles in performing the sensing task. The sensed data should be processed in such a way that

This paper explores the issues of energy harvesting (EH) and multiple eavesdropping in an underlay cognitive radio network (CRN) that comprises of a secondary transmitter ( $SU_{\mathcal {T}}$ ) and destination ( $SU_{\mathcal {D}}$ ) node connected through a decode-forward (DF) relay ( $S_{\mathcal {R}}$ ). CRN is operated following a frame structure that includes three non-overlapping slots where

Aim: We set out to investigate the benefit of the “memory” of long short term memory (LSTM) networks in predicting spectrum occupancy in multiple time horizons in Land Mobile Radio (LMR) bands. Background: ANNs are a popular choice for spectrum prediction. Traditionally, ARIMA models have been at the forefront of forecasting. However, recurrent ANNs have demonstrated good prediction performance. Methodology:

In this paper, the physical layer security of a dual-hop underlay uplink cognitive radio network is investigated over Nakagami- ${m}$ fading channels. Specifically, multiple secondary sources $(S_{i})_{1\leq i \leq N}$ are taking turns in accessing the licensed spectrum of the primary users and communicating with a multi-antenna secondary base station ( ${D}$ ) through the aid of a multi-antenna relay

In this paper, we study the problem of designing adaptive Medium Access Control (MAC) solutions for wireless sensor networks (WSNs) under the Irregular Repetition Slotted ALOHA (IRSA) protocol. In particular, we optimize the degree distribution employed by IRSA for finite frame sizes. Motivated by characteristics of WSNs, such as the restricted computational resources and partial observability, we

Network data collection is a vital part in the process of network monitoring, traffic billing, network management and intrusion detection. As a new kind of network architecture, Software Defined Network (SDN) provides a possibility of intelligent and adaptive network data collection with centralized control and programming. However, existing literatures lack a concrete solution to economically collect

Cognitive relay networks are designed to increase access to under-utilized spectrum. The direct link communication can be exploited to decrease the outage probability with a buffer-aided relaying network. To this end, a novel transmission policy is proposed to combine the benefits of direct communication and buffer-aided relaying. In addition, a modification to the hybrid interweave-underlay spectrum

The increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extremely likely that these devices receive compute supplements from Clouds and upload data to the back-end datacentres for execution. Increasing number of

The popularity of smart mobile devices has led to a tremendous increase in mobile traffic, which has put a considerable strain on the fifth generation of mobile communication networks (5G). Among the three application scenarios covered by 5G, ultra-high reliability and ultra-low latency (uRLLC) communication can best be realized with the assistance of artificial intelligence. For a combined 5G, edge

In order to provide information privacy for the primary network and support the secondary quality-of-service (QoS) requirement, we design a cooperative primary preserving strategy which simultaneously transfers information and power in cognitive radio networks. In this strategy, the secondary network provides information security for the primary information transmission in three phases and obtains

Network slicing is a key enabler to successfully support 5G services with specific requirements and priorities. Due to the diversity of these services, slice deployment and orchestration are essential to guarantee service performance in a cost-effective way. Here, we propose an Artificial Intelligence framework for cross-slice admission and congestion control that simultaneously considers communication

This paper studies the improvement in network throughput of an ad hoc network from using both licensed and unlicensed spectra compared to the case where only unlicensed spectrum is used. We address the problem of how the nodes of the network, or secondary users (SUs), should spread their transmissions on both licensed and unlicensed spectra to maximize network throughput, and characterize ‘sharing

Recent advances in wireless technologies have led to several autonomous deployments of such networks. As nodes across distributed networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become

Automated wireless spectrum monitoring across frequency, time and space will be essential for many future applications. Manual and fine-grained spectrum analysis is becoming impossible because of the large number of measurement locations and complexity of the spectrum use landscape. Detecting unexpected behaviors in the wireless spectrum from the collected data is a crucial part of this automated monitoring

We investigate the cyclic redundancy check (CRC) codes aided successive cancellation list (SCL) decoding schemes to improve the performance of Polar codes. Distinguished from the existing literatures of Polar codes that only consider the error detection capability of CRC, we attempt to take advantage of the inherent error correction capability of CRC and we first devise a segmented CRC-error-correcting

Non-orthogonal multiple access (NOMA) holds the promise to be a key enabler of 5G communication. However, the existing design of NOMA systems must be optimized to achieve maximum rate while using minimum transmit power. To do so, this paper provides a novel technique based on multi-objective optimization to efficiently allocate resources in the multi-user NOMA systems supporting downlink transmission

In this paper, we consider a cognitive radio (CR) network where the primary network’s feedback information is utilized to develop an access scheme for the secondary network to exploit the underutilized primary spectrum resources. Secondary users (SUs) identify the spectrum opportunities by sensing the spectrum for primary users (PUs) activities and by listening to the PUs feedback. The feedback signals

In this paper, we propose a novel system state aware multichannel protocol for energy harvesting cognitive radio networks (EH-CRNs). In contrast with the state-of-the-art approaches, the proposed scheme jointly accounts for the primary users traffic characteristics as well as the instantaneous channel state over a particular channel. Our analysis demonstrates that the channel state is a critical parameter