The increasing complexity of Beyond 5G and 6G networks necessitates new paradigms for autonomy and assur- ance. Traditional O-RAN control loops rely heavily on RIC- based orchestration, which centralizes intelligence and exposes the system to risks such as policy conflicts, data drift, and unsafe actions under unforeseen conditions. In this work, we argue that the future of autonomous networks lies in a multi-agentic architecture, where specialized agents collaborate to perform data collection, model training, prediction, policy generation, verification, deployment, and assurance. By replacing tightly- coupled centralized RIC-based workflows with distributed agents, the framework achieves autonomy, resilience, explainability, and system-wide safety. To substantiate this vision, we design and evaluate a traffic steering use case under surge and drift conditions. Results across four KPIs: RRC connected users, IP throughput, PRB utilization, and SINR, demonstrate that a naive predictor-driven deployment improves local KPIs but destabilizes neighbors, whereas the agentic system blocks unsafe policies, preserving global network health. This study highlights multi- agent architectures as a credible foundation for trustworthy AI- driven autonomy in next-generation RANs.

Instant messaging applications (IM apps) are among the most frequent sources of signalling storms in mobile networks. Poorly designed IM apps can be exploited by adversaries to initiate stealthy signalling storm attacks (SSAs). Recent advancements in the 5G Radio Resource Control (RRC) protocol have introduced a novel attack vector, where malicious actors manipulate the 5G inactivity timer to generate excessive signalling messages. Due to the inherent complexity of the working mechanism of the IM app and the diversity of user behavior, distinguishing between legitimate and malicious IM signalling traffic is a challenge. In this study, a comprehensive source traffic model (STM) is presented that captures the operational dynamics of modern IM applications alongside a wide spectrum of user interaction patterns. This model enables a deeper understanding of normal IM traffic and its corresponding signalling load. Building upon this foundation, we introduce a threat model that demonstrates how attackers can emulate normal user behavior and exploit IM app functionalities to launch inactivity timer-based SSAs in 5G networks. To detect such attacks, two novel traffic analysis features: Burst Correlation Index (BCI) and Kolmogorov–Smirnov Distance Contrast (KSDC) are proposed. These metrics effectively highlight subtle differences between benign and malicious traffic patterns, enhancing classification accuracy significantly.

Exploring multi-step forecasting in dynamic spectrum access (DSA) is vital for increasing spectrum efficiency and reliability. Accurately predicting future occupancy states can significantly advance our efforts to optimize spectrum utilization, enabling proactive management that reduces interference and enhances the performance of cognitive radio (CR) networks. For this purpose, the application of deep learning (DL) techniques for spectrum prediction has become increasingly important, as they excel at modeling complex temporal dependencies and learning from historical datasets. This paper deals with multi-step prediction in spectrum occupancy, utilizing second-order Markov models and Long-Short-Term Memory (LSTM) networks. The study systematically categorizes spectrum occupancy into six distinct categories to understand biased, random, and unbiased scenarios. Each one exhibits distinct prediction error behaviors. The findings of this study reveal the critical importance of knowledge of occupancy categories in spectrum prediction, and also their impact on LSTM networks when capturing and predicting temporal dependencies inherent in spectrum occupancy data.

Satellite and terrestrial communication systems often operate in congested and sometimes overlapping frequency bands due to the growing demand for spectrum. Existing and future systems might head towards intelligent spectrum access to address the limited spectrum problem. In this paper, we present a non-cooperative semantic segmentation spectrum sensing (SSSS) agent to tackle the first task of intelligent spectrum access. The proposed agent utilizes an encoder-decoder network architecture coupled with supervised machine learning to classify pixels in a spectrogram image as occupied or background. Furthermore, the proposed agent was trained to detect satellite and terrestrial signals under low and high signal-to-noise ratio (SNR) fading environments. The proposed agent removed underutilized convolution filters through pruning to improve its memory footprint and inference timing. We conducted an extensive performance evaluation under different simulated shared spectrum scenarios with varying channels. We validated the performance of the proposed agent through statistical analysis that was applied to the softmax output of the network.

This paper addresses the problem of fair coexistence in unlicensed spectrum of Wi-Fi with other co-located technologies (e.g., LTE) through the identification of the saturation status of a Wi-Fi network. Such status is identified with the application of Machine Learning (ML) algorithms to the statistics of medium occupation of the Wi-Fi frames. More specifically, the inter-frame spacing statistics of the Wi-Fi frames are used for the generation of a Random Forest (RF) model that can classify the Wi-Fi network saturation status. The performance of the ML algorithms relies on the quality of the input data and there is the need to define robust ML algorithms, which can mitigate potential errors in the statistics collected from the network. This paper proposes an enhancement of the ML-based analysis process, which uses Outlier Detection (OD) algorithms to detect and filter out anomalies to sanitize the statistical data before the application of the ML algorithm. The OD-based approach is applied to a public Wi-Fi Saturation data set, where it is shown to mitigate significantly (even more than 4% classification improvement) the presence of perturbed data in particular with the use of the sparse data observers (SDO) outlier detection algorithm, recently introduced in research literature.

The classification of wireless channel propagation scenarios is an important function in wireless communication design and machine learning (ML) has been increasingly used to implement such function. In recent times, Deep Learning (DL) models like Convolutional Neural Networks (CNN) or Long Short Term Memory (LSTM) have achieved a higher classification performance in comparison to feature extraction methods at a significant computational complexity and time. An alternative approach would be to rely on Time Series Classification (TSC) algorithms but they may also incur in high computational costs. A new computing efficient TSC algorithm called Supervised Time Series Forest (STSF) has been recently introduced in the literature for a different set of applications and this paper provides an evaluation in this specific context. In addition, this paper introduces an improvement on the initial design of the STSF with the addition of entropy measures to generate the Entropy Enhanced STSF (EESTSF) algorithm. EESTSF is applied to an open source data set with 9 different channel propagation scenarios, where it is shown to outperform significantly not only RF, KNN and CNN but also STSF, especially in low Signal to Noise (SNR) conditions.

Cooperative spectrum sensing (CSS) is essential for improving the spectrum efficiency and reliability of cognitive radio applications. Next-generation wireless communication networks increasingly employ uniform planar arrays (UPA) due to their ability to steer beamformers towards desired directions, mitigating interference and eavesdropping. However, the application of UPA-based CSS in cognitive radio remains largely unexplored. This paper proposes a multi-beam UPA-based weighted CSS (WCSS) framework to enhance detection reliability, applicable to various cognitive radio networks, including cellular, vehicular, and satellite communications. We first propose a weighting factor for commonly used energy detection (ED) and eigenvalue detection (EVD) techniques, based on the spatial variation of signal strengths resulting from UPA antenna beamforming. We then analytically characterize the performance of both weighted ED and weighted EVD by deriving closed-form expressions for false alarm and detection probabilities. Our numerical results, considering both static and dynamic user behaviors, demonstrate the superiority of WCSS in enhancing sensing performance compared to uniformly weighted detectors.

Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative technologies, enhancing spectral efficiency and improving interference management in multi-user cooperative communications. This paper investigates the integration of RIS with Flexible-Duplex (FlexD) communication, featuring dynamic scheduling capabilities, to mitigate unintended external interference in multi-user wireless networks. By leveraging the reconfigurability of RIS and dynamic scheduling, we propose a user-pair selection scheme to maximize system throughput when full channel state information (CSI) of interference is unavailable. We develop a mathematical framework to evaluate the throughput outage probability when RIS introduces spatial correlation. The derived analytical results are used for asymptotic analysis, providing insights into dynamic user scheduling under interference based on statistical channel knowledge. Finally, we compare FlexD with traditional Full Duplex (FD) and Half Duplex (HD) systems against RIS-assisted FlexD. Our results show FlexD’s superior throughput enhancement, energy efficiency and data management capability in interference-affected networks, typical in current and next-generation cooperative wireless applications like cellular and vehicular communications.

This paper considers a MIMO Integrated Sensing and Communication (ISAC) system, where a base station simultaneously serves a MIMO communication user and a remote MIMO sensing receiver, without channel state information (CSI) at the transmitter. Existing MIMO ISAC literature often prioritizes communication rate or detection probability, typically under constant false-alarm rate (CFAR) assumptions, without jointly analyzing detection reliability and communication constraints. To address this gap, we adopt an eigenvalue-based detector for robust sensing and use a performance metric—the total detection error—that jointly captures false-alarm and missed-detection probabilities. We derive novel closed-form expressions for both probabilities under the eigenvalue detector, enabling rigorous sensing analysis. Using these expressions, we formulate and solve a —joint power allocation and threshold optimization— problem that minimizes total detection error while meeting a minimum communication rate requirement. Simulation results demonstrate that the proposed joint design substantially outperforms conventional CFAR-based schemes, highlighting the benefits of power-and threshold-aware optimization in MIMO ISAC systems.

This paper presents an optimization approach for cooperative Medium Access Control (MAC) techniques in Vehic-ular Ad Hoc Networks (VANETs) equipped with Roadside Unit (RSU) to enhance network throughput. Our method employs a distributed cooperative MAC scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, featuring selective RSU probing and adaptive transmission. It utilizes a dual timescale channel access framework, with a “large-scale” phase accounting for gradual changes in vehicle locations and a “small-scale” phase adapting to rapid channel fluctuations. We propose the RSU Probing and Cooperative Access (RPCA) strategy, a two-stage approach based on dynamic inter-vehicle distances from the RSU. Using optimal sequential planned decision theory, we rigorously prove its optimality in maximizing average system throughput per large-scale phase. For practical implementation in VANETs, we develop a distributed MAC algorithm with periodic location updates. It adjusts thresholds based on inter-vehicle and vehicle-RSU distances during the large-scale phase and accesses channels following the RPCA strategy with updated thresholds during the small-scale phase. Simulation results confirm the effectiveness and efficiency of our algorithm.

This research paper delves into interference mitigation within Low Earth Orbit (LEO) satellite constellations, particularly when operating under constraints of limited radio environment information. Leveraging cognitive capabilities facilitated by the Radio Environment Map (REM), we explore strategies to mitigate the impact of both intentional and unintentional interference using planar antenna array (PAA) beamforming techniques. We address the complexities encountered in the design of beamforming weights, a challenge exacerbated by the array size and the increasing number of directions of interest and avoidance. Furthermore, we conduct an extensive analysis of beamforming performance from various perspectives associated with limited REM information: static versus dynamic, partial versus full, and perfect versus imperfect. To substantiate our findings, we provide simulation results and offer conclusions based on the outcomes of our investigation.

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting an unknown non-random signal by capitalizing on the leading eigenvalue of the whitened sample covariance matrix as the test statistic (a.k.a. Roy's largest root test). Since the unknown signal is non-random, the whitened sample covariance matrix turns out to have a non-central F-distribution. This distribution assumes a singular or non-singular form depending on whether the number of observations$p\lessgtr$the system dimensionality$m$. Therefore, we statistically characterize the leading eigenvalue of the singular and non-singular$F$-matrices by deriving their cumulative distribution functions (c.d.f.). Subsequently, they have been utilized in deriving the corresponding receiver operating characteristic (ROC) profiles. We also extend our analysis into the high dimensional domain. It turns out that, when the signal is sufficiently strong, the maximum eigenvalue can reliably detect it in this regime. Nevertheless, weak signals cannot be detected in the high dimensional regime with the leading eigenvalue.

Transmitter authentication is critical for secured Internet of Things (IoT) applications. Recently, there has been growing interest in utilizing the physical layer authentication technique, radio frequency (RF) fingerprinting, to introduce extra security measurements without adding additional components. This work presents a novel fingerprint exploitation modality, Density Trace Plot (DTP), to leverage RF fingerprints originating from symbol transition trajectories for transmitter authentication. With a particular focus on IQ imbalance as the source impairment for RF fingerprints, we investigate the feasibility of three types of DTPs based on constellation, eye, and phase traces. The potential fingerprints presented in the DTP modalities are then used in training three deep learning classifiers: 2D-convolutional neural network (CNN), 2D-CNN+bi-directional long short-term memory (biLSTM), and 3D-CNN for transmitter authentication. The feasibility of the proposed approach in both wired and wireless conditions is validated using an experimental setup built using ADALM-PLUTO software-defined radios (SDRs). Experimental results demonstrate the best authentication accuracy of 96.7% is achieved across signals of various modulation complexities.

Dynamic spectrum access is recognized as a potential solution to the problem of spectrum scarcity. Consequently, a significant amount of research has been conducted on spectrum allocation. In this study, we present a multi-traffic class queue-based spectrum allocation solution. The proposed solution is designed for a scenario where communication and other systems do not necessarily require agility in the frequency bands they operate in. The spectrum sensing and forecasting processes are carried out over relatively long time periods compared to the actual transmission rates. The study considers two traffic classes: priority and non-priority. Three packet handling strategies are presented and their average queuing delay performances are compared. While all three strategies yield satisfactory results under low/medium traffic conditions, Strategy 3 exhibits the greatest flexibility in terms of delay performance. Additionally, the study examines the selection of delay thresholds in Strategy 3 using three fairness indices. It is worth noting that although the proposed algorithm focuses on spectrum allocation, it can be easily adapted to address resource allocation problems involving requests with multiple priorities.

Utilizing Low Earth Orbit (LEO) satellite networks equipped with Inter-Satellite Links (ISL) is envisioned to provide lower delay compared to traditional optical networks. However, LEO satellites have constrained energy resources as they rely on solar energy in their operations. Thus requiring special consideration when designing network topologies that do not only have low-delay link paths but also low-power consumption. In this paper, we study different satellite constellation types and network typologies and propose a novel power-efficient topology. As such, we compare three common satellite architectures, namely; (i) the theoretical random constellation, the widely deployed (ii) Walker-Delta, and (iii) Walker-Star constellations. The comparison is performed based on both the power efficiency and end - to-end delay. The results show that the proposed algorithm outperforms long-haul ISL paths in terms of energy efficiency with only a slight hit to delay performance relative to the conventional ISL topology.

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. To be specific, we consider a scenario in which the number of signal bearing samples$(n)$is strictly smaller than the dimensionality of the signal space$(m)$. Our test statistic is the leading generalized eigenvalue of the whitened sample covariance matrix (a.k.a.$F- \mathbf{matrix}$) which is constructed by whitening the signal bearing sample covariance matrix with noise-only sample covariance matrix. The sample deficiency (i.e.,$m > n)$in turn makes this$F$-matrix rank deficient, thereby singular. Therefore, an exact statistical characterization of the leading generalized eigenvalue (l.g.e.) of a singular$F-\mathbf{matrix}$is of paramount importance to assess the performance of the detector (i.e., the receiver operating characteristics (ROC)). To this end, we employ the powerful orthogonal polynomial approach to derive a new finite dimensional c.d.f. expression for the l.g.e. of a singular F-matrix. It turns out that when the noise only sample covariance matrix is nearly rank deficient and the signal-to-noise ratio is$O(m)$, the ROC profile converges to a limit.

We introduce a new framework for fairness study of Power-Domain Non-Orthogonal Multiple Access (PD-NOMA) with fairness factor definitions to analyse pair and system behaviour. Aiming at both pair-wise and system-level fairness as key performance goals, first, we present a power allocation scheme to maximize sum bit rate within each pair. We demonstrate analytically that it is possible to enhance both users' bit rates in the pair over their Orthogonal Multiple Access (OMA) equivalent rates without impacting fairness. Additionally, at system level, we complement our work proposing two prioritization algorithms to enhance fairness through the NOMA paring process. Simulations show that the schemes are effective to maximize the system fairness when it is measured with, either the users' or the pairs' bit rates.

Transmission Control Protocol (TCP) is used for delivering internet data to users. TCP is prone to network congestion and its ability to combat network congestion has certain limitations, especially in the case of wireless networks. Currently, Long Term Evolution (LTE) is one of the main chosen standards for mobile and fixed wireless data communications. LTE uses various error correction techniques for reliable data delivery over the air-interface. These cause other issues such as excessive latency due to queuing in the Base Station (BS). Therefore, adequate congestion mitigation mechanisms are required. Existing mechanisms such as Discard Timer (DT) and Random Early Detection (RED) require parameter configuration and are therefore limited in their ability to mitigate congestion. In this paper, a new mechanism has been proposed of implementing congestion management using radio resource allocation techniques with Game Theory as an underlying mathematical framework.

This paper proposes the implementation of an LTE base station congestion mitigation algorithm that is developed based on the principles of non-cooperative game theory. This algorithm provides a unique game model for radio resource allocation as well as congestion mitigation in an LTE network. The game theoretic controller's congestion management capability is compared with other LTE based congestion mitigation models such as Discard Timer and Random Early Detection in an end to end wireless network. The transport layer protocol for user packets considered is Transmission Control Protocol. This study focuses on users with low mobility or connected in a fixed wireless network. The results from this study suggest that congestion management using non-cooperative game theory can be successfully implemented and it provides a more robust alternative to traditional congestion mitigation models such as Discard Timer or Random Early Detection for LTE networks.

Although the popularity of Software-Defined Networking (SDN) is increasing, it is also vulnerable to security attacks such as Denial of Service (DoS) attacks. Since in SDN, the control plane is isolated from the data plane, DoS attackers can easily target the control plane to impair the network infrastructure in addition to the data plane to degrade the user's Quality of Service (QoS). In our previous work, we introduced SECO, an SDN Secure Controller algorithm to detect and defend SDN against DoS attacks. Simulation results showed that SECO successfully defends SDN networks from DoS attacks. In this paper, we present SDN sEcure COntrol and Data Plane (SECOD), which is an improved version of SECO. Basically, SECOD introduces new triggers to detect and prevent DoS attacks in both control and data planes. Moreover, SECOD is implemented and tested using SDN-based hardware testbed, OpenFlow-based switch, and RYU controller to capture the dynamics of realistic hardware and software. The results show that SECOD successfully detects and effectively mitigates DoS attacks on SDN networks keeping data plane performance at 99.72% compared to a network not under attack.

Zodiac-FX is the first OpenFlow switch designed to sit on a desk, not in a datacenter. In this demo, we present Zodiac-FX the world's smallest OpenFlow Software Defined Network Switch. Our main objective is to showcase the usage and functionalities of Zodiac-FX in handling OpenFlow protocol. We will also demonstrate SDN sEcure COntrol and Data Plane (SECOD), an SDN secure controller algorithm to detect and defend SDN against DoS attacks. We will demonstrate Zodiac-FX and SECOD value via experiments within real traffic and Denial- of-Service (DoS) attacks allowing the audience to interact with the complete toolkit system.

As the use of automotive radar increases, performance limitations associated with radar-to-radar interference will become more significant. In this paper, we employ tools from stochastic geometry to characterize the statistics of radar interference. Specifically, using two different models for the spatial distributions of vehicles, namely, a Poisson point process and a Bernoulli lattice process, we calculate for each case the interference statistics and obtain analytical expressions for the probability of successful range estimation. This paper shows that the regularity of the geometrical model appears to have limited effect on the interference statistics, and so it is possible to obtain tractable tight bounds for the worst case performance. A technique is proposed for designing the duty cycle for the random spectrum access, which optimizes the total performance. This analytical framework is verified using Monte Carlo simulations.

Weighted centroid localization (WCL) based on received signal strength (RSS) measurements is an attractive low-complexity solution that enables cognitive radios (CRs) to have a geolocation awareness of the radio environment. In this paper, we propose a new analytical framework to accurately calculate the performance of WCL based on the statistical distribution of the ratio of two quadratic forms in normal variables. In particular, we derive an exact expression for the cumulative distribution function (CDF) of the two-dimensional location estimation in the presence of independent and identically distributed (i.i.d.) as well as correlated shadowing. Numerical results confirm that the analytical framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as CR network spatial topology.

Stepped frequency radar technology, where the transmit waveform consists of a sequence of tones, has long been suggested for cost-effective and high-resolution applications. One recent use of this technology is in automotive application where, in addition to cost-effectiveness, a random stepped frequency (RSF) waveform can significantly reduce the interference between vehicles. In this paper we provide a generic framework for the range and Doppler measurements for multiple targets. We further suggest two possible methods for reducing the computational complexity of RSF waveforms processing, which is important for future automotive applications.

In this paper we study 3GPP mobility robustness in the context of Public Safety (PS) communications. In specific, we investigate the scenario in which 4G LTE connectivity is provisioned by means of Aerial evolved Node Bs (AeNBs) mounted on Low Altitude Platforms (LAPs) raised in the sky at an altitude ranging from few hundreds meters to one kilometer. Given the peculiarities of PS communications, the event of handover (HO) is particularly crucial for PS users. It is well understood that a trade-off exists between events of radio link failure (RLF) and HO completion, which is usually achieved by proper tuning of handover parameters. We propose a policy-based HO parameters adjustment in the form of a heuristic, which exploits the information provided by the Mobility Robustness Optimisation (MRO), in order to to achieve such a trade-off. Using simulations, we show that our heuristic solution can achieve the above mentioned trade-off, taking into account the different HO phases and incorporating a suitable Air-to-Ground (ATG) signal propagation model.

It is our pleasure to write the Editorial for the Special Issue on Evolution and Development of 5G Wireless Communication Systems. Upon conclusion of fourth generation (4G) cellular network standardization tasks a few years ago, the direction of research has started to shift systematically towards fifth generation (5G) communication systems. The difference between 4G and 5G is not limited to the increased throughput and performance. 5G systems are supposed to be flexible to accommodate heterogeneous traffic and devices, and various applications with different quality-of-service (QoS) requirements. Particularly, the goal is to take full benefit of advances in technology including cloud computing, Internet of Things (IoT), ultra-dense networks, massive MIMO, device-to-device communication, pervasive and social computing. In order to meet stringent goals, 5G communication systems build upon the evolution of the existing technologies and the development of the new technologies mentioned above. The Special Issue contains 11 papers, each paper covers the subject from different prospective, and thus, offer readers a holistic view of different research challenges currently under investigation by research communities. The papers can be grouped under following topics: C. Hua et al. present a paper entitled “Wireless backhaul resource allocation and user-centric clustering in ultra-dense wireless networks”. It considers optimization of resource allocation in wireless backhaul links and user-centric clustering in the access links. The objective is to maximise the weighted sum rate of all users under the backhaul resource constraints. An iterative algorithm is proposed to solve the transformed problem based on its special property. Simulation results show that the proposed algorithm outperforms other existing schemes under different network settings. Z. Wang et al. present a paper entitled “Interference pricing in 5G ultra-dense small cell networks: a Stackelberg game approach” which models the scenario as a Stackelberg game, where the macrocell base stations (MBS) act as the leader and all small cell base stations (SCBSs) as followers. Simulation results show the correctness of the analysis and the significant benefits when the power control and channel allocation are jointly considered in the proposed schemes. Z. Kaleem et al. present “Public safety users’ priority-based energy and time-efficient device discovery scheme with contention resolution for ProSe in third generation partnership project long-term evolution-advanced systems”, which proposes a time and energy-efficient contention-resolving device discovery resource allocation (TEECR-DDRA) scheme that has the capability to enhance the success ratio for discovery of D2D users by reducing collisions among users. Moreover, the proposed TEECR-DDRA scheme has the ability to prioritise PS users to meet their QoS and latency requirements. System-level simulations show that the proposed TEECR-DDRA scheme performs remarkably well under D2D network. M. T. Gul et al. present a paper entitled “Merge-and-forward: a cooperative multimedia transmissions protocol using RaptorQ codes”, proposing a cooperative multimedia transmission protocol based on a novel merge-and-forward relaying and the best relay selection (RS) schemes. Moreover, to combat the packet loss for enhanced and reliable video delivery, they adopt application layer forward error correction scheme which is based on the most improved and advanced version of fountain codes (i.e., RaptorQ codes). They evaluate the performance of the proposed scheme in terms of decoding failure probability, decoding overhead, peak signal-to-noise ratio, and mean opinion score. K. Yang et al.'s paper “Edge aware cross-tier base station cooperation in heterogeneous wireless networks with non-uniformly-distributed nodes” investigates the cross-tier base station (BS) cooperation in non-uniform heterogeneous networks where the distribution of pico BSs (PBSs) is modelled as Neyman–Scott cluster process. The authors propose an edge aware cross-tier cooperation scheme to improve the performance of edge hotspot users that have weaker signal-to-interference-plus noise ratio (SINR). Stochastic geometry is utilised to derive the SINR and energy efficiency performance of the proposed scheme, which is compared with other classical schemes such as full cooperation (FC) and traditional non-cooperation scheme. Y. Cai et al. present “Secure transmission in the random cognitive radio networks with secrecy guard zone and artificial noise” which proposes a simple and decentralised secure transmission scheme by jointly incorporating the secrecy guard zone and artificial noise in cognitive radio networks. Numerical results show how the system parameters affect the achievable maximum secrecy throughput, the optimal transmission power and the optimal power allocation between the information-bearing signal and the artificial noise. Y. Sun et al. present “Local altruistic coalition formation game for spectrum sharing and interference management in hyper-dense cloud-RANs” and investigate the spectrum sharing and interference management in hyper-dense cloud radio access networks (C-RANs). The authors formulate this problem as a local altruistic coalition formation game (LACF) with externalities. The authors propose a distributed coalitional formation algorithm based on modified recursive core to obtain the final stable coalition partition. Furthermore, the system stability, convergence and complexity of the proposed algorithm are analysed. W. Chang et al. paper “Effects of non-uniform quantisation on the interference mitigation using multi-cell multiple-input and multiple-output coordinated beamforming” proposes a low complexity cumulative distribution function (CDF)-based non-uniform quantisation method with a limited number of feedback bits for applying more quantisation levels to represent feedback CSI, which occurs with higher probability. The simulation results proved the higher transmission rate, particularly in cases with fewer feedback bits. D. Liu et al.'s paper “Self-organising multiuser matching in cellular networks: a score-based mutually beneficial approach” studies the self-organising user assignment problem for the multi-user cooperation network. Furthermore, the multi-user assignment problem is formulated as a one-to-one matching game, in which idle users and active users rank one another individually based on their own preference. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results. D. C. Araújo et al.'s paper “Massive MIMO: survey and future research topics” presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research. R. Sun et al. present the paper “Transceiver design for cooperative nonorthogonal multiple access systems with wireless energy transfer”. The paper considers an energy harvesting-based cooperative non-orthogonal multiple access (NOMA) system. Transmitter beamforming, power splitter and receiver filter are jointly designed to maximise rate with the predefined QoS constraint of weaker node and the power constraint of node which simultaneously sends independent signals to a stronger node and weaker node. Since the problem is non-convex, they propose an iterative approach to solve it. Moreover, a zero-forcing based low-complexity solution is also presented. Simulation results demonstrate that, both two proposed schemes have better performance than the direction transmission. All of the papers in Special Issue show that 5G systems can support the specialized use cases which are not supported by the current access systems. In addition, authors investigated the issues related to backhaul for 5G systems and latency reduction. The integration of new technologies with the evolved current systems bring tremendous improvement in 5G systems. Alagan Anpalagan received the B.A.Sc., M.A.Sc., and Ph.D. degrees in electrical engineering from the University of Toronto, Toronto, ON, Canada. In 2001, he joined the Department of Electrical and Computer Engineering, Ryerson University, Toronto, where he was promoted to Full Professor in 2010. He served the department as the Graduate Program Director (2004–2009) and the Interim Electrical Engineering Program Director (2009–2010). He directs a research group working on radio resource management and radio access and networking areas within the WINCORE Lab. During his sabbatical (2010–2011), he was a Visiting Professor with Asian Institute of Technology and a Visiting Researcher with Kyoto University, Kyoto, Japan. His industrial experience includes working at Bell Mobility, Nortel Networks, and IBM Canada. He has coauthored three edited books, namely, Design and Deployment of Small Cell Networks (Cambridge University Press, 2014), Routing in Opportunistic Networks (Springer, 2013), and Handbook on Green Information and Communication Systems (Academic Press, 2012). His current research interests include cognitive radio resource allocation and management, wireless cross-layer design and optimization, cooperative communication, machine-to-machine communication, small cell networks, and green communications technologies. Dr. Anpalagan has served as an Associate Editor of the IEEE Communications Surveys & Tutorials since 2012 and Springer Wireless Personal Communications since 2009. Adnan Shahid received the B.Eng. and the M.Eng. degrees in computer engineering with communication specialization from the University of Engineering and Technology, Taxila, Pakistan in 2006 and 2010, respectively, and the Ph.D degree in information and communication engineering from the Sejong University, South Korea in 2015. He is currently working as a Postdoctoral Researcher at iMinds/IBCN, Department of Information Technology, University of Ghent, Belgium. From Sep 2015 – Jun 2016, he was with the Department of Computer Engineering, Taif University, Saudi Arabia. From Mar 2015 – Aug 2015, he worked as a Postdoc Researcher at Yonsei University, South Korea. From Aug 2012 – Feb 2015, he worked as a PhD research assistant in Sejong University, South Korea. From Mar 2007 – Aug 2012, he served as a Lecturer in electrical engineering department of National University of Computer and Emerging Sciences (NUCES-FAST), Pakistan. He was also the recipient of the prestigious BK 21 plus Postdoc program at Yonsei University, South Korea. He is a member of IEEE and actively involved in various research activities. He is also serving as an Associate Editor at IEEE Access Journal and Annals of Telecommunication Journal. His research interests includes the next generation wireless communication and networks with prime focus on resource management, interference management, cross-layer optimization, self-organizing networks, small cell networks, device to device communications, machine to machine communications, 5G wireless communications, etc. Waleed Ejaz (S’12, M’14, SM‱16) is a Senior Research Associate at the Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada. Prior to this, he was a Post-doctoral fellow at Queen's University, Kingston, Canada. He received his Ph.D. degree in Information and Communication Engineering from Sejong University, Republic of Korea in 2014. He earned his M.Sc. and B.Sc. degrees in Computer Engineering from National University of Sciences & Technology, Islamabad, Pakistan and University of Engineering & Technology, Taxila, Pakistan, respectively. He worked in top engineering universities in Pakistan and Saudi Arabia as a Faculty Member.His current research interests include Internet of Things (IoT), energy harvesting, 5G cellular networks, and mobile cloud computing. He is currently serving as an Associate Editor of the Canadian Journal of Electrical and Computer Engineering and the IEEE ACCESS. In addition, he is handling the special issues in IET Communications, the IEEE ACCESS, and the Journal of Internet Technology. He also completed certificate courses on Teaching and Learning in Higher Education from the Chang School at Ryerson University. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in the Centre for Communication Systems Research (CCSR) at the University of Surrey, UK. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has lead role in a number of multimillion international research projects including the new physical layer work area for 5G innovation centre at Surrey. He has supervised 17 successful PhD graduates and published over 150 peer-reviewed research papers including more than 20 IEEE Journals. His research interests include the derivation of information theoretic performance limits, energy efficient design of cellular system and learning/self-organizing techniques for optimization of cellular system operation. He is a senior member of IEEE and a Fellow of Higher Education Academy (FHEA), UK. Kandeepan Sithamparanathan has a PhD from the University of Technology, Sydney and is currently with the School of Electrical and Computer Engineering at RMIT University. He is also a NICTA Researcher at the NICTA Victoria Research Laboratory (VRL, Melbourne). In the past he had worked with the National ICT Australia (Canberra Research Laboratory) and CREATE-NET (Trento). Kandeepan served as one of the Vice Chairs for the IEEE Technical Committee on Cognitive Networks (TCCN) and has published a book together with Dr Andrea Giorgetti from the University of Bologna, Italy, titled ‘Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation and Localization’, published by Artech House (Boston). He currently Chairs the IEEE VIC Communication Society Chapter and is a Senior Member of the IEEE. He was awarded as one of the best IEEE Reviewers by the IEEE Communications Society. Kandeepan has published around ninety peer reviewed journal and conference papers. He has chaired several IEEE workshops and other conferences. His research interests are in 5G communications, cognitive radios and signal processing techniques. Yuhua Xu received his B.S. degree in Communications Engineering, and Ph.D. degree in Communications and Information Systems from College of Communications Engineering, PLA University of Science and Technology, in 2006 and 2014 respectively. He has been with College of Communications Engineering, PLA University of Science and Technology since 2012, and currently as an Assistant Professor. His research interests focus on opportunistic spectrum access, learning theory, game theory, and distributed optimization techniques for wireless communications. He has published several papers in international conferences and reputed journals in his research area. He served as Associate Editor for Wiley Transactions on Emerging Telecommunications Technologies and KSII Transactions on Internet and Information Systems. In 2011 and 2012, he was awarded Certificate of Appreciation as Exemplary Reviewer for the IEEE Communications Letters. He was selected to receive the IEEE Signal Processing Society's (SPS) 2015 Young Author Best Paper Award, and the Funds for Distinguished Young Scholars of Jiangsu Province in 2015.

This paper lays a mathematical framework for estimating the energy saving of a relay assisting a pair of wireless devices. We derive closed-form expressions for describing the geometrical zone where relaying is energy efficient. In addition, we obtain the probabilistic distribution of the energy saving introduced by relays that are randomly distributed according to a spatial Poisson point process. Furthermore, we present a comparison methodology for fairly evaluating the energy consumption of conventional cellular network from one side and relay-assisted device-to-device communication from another side. Results suggest that a significant energy saving can be achieved when relay-assisted device-to-device communication is adopted for distances below a certain threshold. In order to test the analytical framework, we perform Monte-Carlo simulations and compare the results with those obtained from the mathematical framework.

In this paper we introduce a novel analytic approach in clustering wireless nodes on ground fields through the assistance of an aerial base station, forming an aerial backhauled network. On the ground, some of the deployed nodes act as cluster heads, and aggregate and relay the terrestrial data towards the aerial platform. We present a novel cluster-head selection algorithm based on Matern hard-core point process, and then obtain the analytical formulation to optimize the spacing between cluster heads to minimize the overall energy consumption. The formulated problem utilizes stochastic geometry to capture the random nature of the locations of the deployed nodes, leading to a tractable analysis of the expected network metrics. Furthermore, we compare the performance of the proposed approach with other clustering algorithms, and show the improvement in energy efficiency.

Energy efficiency is one of the most critical design issues in wireless sensor networks (WSNs). Recently, RF energy transfer emerges as a promising solution to enhance energy efficiency. In RF energy transfer, energy is supplied to wireless networks through dedicated energy transmitters. WSNs can be equipped with the RF energy charging capabilities such that the WSNs are referred as wireless rechargeable sensor networks. This paper aims to 1) optimally place the energy transmitters and 2) determine optimal number of energy transmitters in WSNs with RF energy transfer. For optimal placement of energy transmitters, a trade-off between maximum energy charged in the network and fair distribution of energy is studied. We present a mechanism by defining a utility function to maximize both total energy charged and fairness. For optimal number of energy transmitters, an optimization problem is formulated and solved while satisfying the constraint on minimum energy charged by each sensor node. Simulation results illustrate the performance of WSNs with RF energy transfer in terms of average energy charged, fairness, and optimal number of energy transmitters.

Blind localization of primary user (PU) is a geo-location spectrum awareness feature that can be very useful in enhancing the functionality of cognitive radios (CRs) in terms of minimizing the interference to the PU. However, the estimation of the PU position within the region is made difficult because cooperation between the PU and the secondary user (SU) does not exist and therefore the PU signal parameters remain unknown to the SU. The centroid-based localization techniques have significantly been adopted as suitable candidates that do not require knowledge of such parameters. In this paper we investigate the localization performance of such techniques by imposing constraints to the selection of the SU nodes, termed as SU cluster, to estimate the PU location. In particular, we impose a minimum distance constraint between any two SU nodes and group the qualifying nodes into a cluster. Only the SU nodes from the constrained cluster can take part in localizing the PU. We simulate the proposed method for a shadow fading wireless environment and compare the results with the centroid and the weighted centroid based blind localization methods. Our results show that the mean squared error in the estimation of the position of the PU is significantly improved for the proposed method compared to the two standard centroid localization techniques especially when the true PU location is away from the center of the region.

In this paper we present a thorough study of spectrum sensing performance in cognitive radio (CR) scenarios where the primary user (PU) transmission is not continuous. In particular, we consider a sensing scheme in which the spectrum is monitored periodically for a fraction of time. In such a situation, sensing is affected by common detection impairments, including noise and fading, as well as by the PU temporal behavior. It is thus necessary to properly design periodic sensing parameters to balance between sensing overhead and detection performance. In this context, we derive a comprehensive analytical framework which accounts for detector performance, presence of noise and fading, PU temporal statistics, and periodic sensing. The analysis allows expression of the detection and false alarm probabilities in closed-forms to capture an explicit relationship between the PU temporal statistic and periodic sensing parameters. Our results show that the temporal behavior of the PU have a significant impact on the detection performance, and therefore a proper design of the sensing parameters is important. Based on our analysis we propose useful strategies for the design of effective periodic sensing.

The reliable prediction of coverage footprint resulting from an airborne wireless radio base station, is at utmost importance, when it comes to the new emerging applications of air-to-ground wireless services. These applications include the rapid recovery of damaged terrestrial wireless infrastructure due to a natural disaster, as well as the fulfillment of sudden wireless traffic overload in certain spots due to massive movement of crowds. In this paper, we propose a statistical propagation model for predicting the air-to-ground path loss between a low altitude platform and a terrestrial terminal. The prediction is based on the urban environment properties, and is dependent on the elevation angle between the terminal and the platform. The model shows that air-to-ground path loss is following two main propagation groups, characterized by two different path loss profiles. In this paper we illustrate the methodology of which the model was deduced, as well as we present the different path loss profiles including the occurrence probability of each.

Cognitive vehicular networks provide the necessary intelligence for vehicular communication networks in order to optimally utilize the limited resources and maximize the performance. One of the important functions of cognitive networks is to learn the radio environment by means of detecting and identifying existing radios. In this context we use the cyclostationarity features of dedicated short range communication (DSRC) signals to blindly detect them in the environment. We present experimental results on the cyclostationarity properties of DSRC wireless transmissions considering the CEN (European) standards for both uplink and downlink signals. By performing cyclostationarity analysis we compute the cyclic power spectrum (CPS) of the CEN DSRC signals which is then used for detecting the presence of the CEN DSRC radios. We obtain CEN DSRC signals from experiments and use the recorded data to perform post-signal analysis to determine the detection performance. The probability of false alarm and the probability of missed detection are computed and the results are presented for different detection strategies. Results show that the cyclostationarity feature based detection can be robust compared to the well known energy based technique for low signal to noise ratio levels.

Managing the power resource in battery operated wireless devices is very crucial for extending the lifetime, here we propose the concept of power trading in wireless communications. We present a business model using sealed bid procurement auction based game theory for power-trading in cooperative wireless communication with quality of service (QoS) constraints. We formulate the problem as an auction in a buyer's market sequentially/repeatedly played with a single source and a multiple relay network. The source, in-need of cooperation of a relay due to lack of battery power to communicate with the destination, broadcasts a cooperation-request specifying its QoS requirements. The QoS that we consider here are the bit error rate and the total delay associated with relaying the source data. The relays respond with their bids in terms of Euros/bit, and the source selects the best relay based on the bids. The relays compete with each other to win the game and profit from power trading. Each relay updates its pricing index via reinforcement learning to win the game during successive bidding intervals of the repeated game. Based on this model our results show that the relay node with the best features such as a better wireless channel and a better geographical position with respect to the source and destination nodes has a better chance of winning the game, and hence giving rise to a dominant strategy. More importantly, we show that the gains from the wireless channels can be converted into economic profits which is an attractive feature of the proposed business model for power trading.

In this paper we present a distributed spectrum sensing technique based on a ring-formulation of the cognitive radio (CR) nodes in a network. The CR nodes in a network form a ring based on a particular criteria and distributes the local spectrum sensing decisions along the ring in a sequential manner to the successive CR nodes. Considering this method, we eliminate the requirement for all the CR nodes to send/broadcast its local decisions to all the other CR nodes as in the traditional distributed detection method. Moreover, in our method all the CR nodes in the ring will have the spectrum sensing information from all the other nodes in the ring unlike the traditional sequential distributed-sensing technique (without the ring formation). We also consider the temporal behavior of the primary user modeled as a Poisson-Pareto burst process, and present two distributed sensing techniques based on the 'ring-around' strategy for the energy based local detection method. We provide closed-form solutions for the detection and false alarm probabilities for the ring-around detection methods and present numerical results for Rayleigh fading signals with AWGN.

Aerial telecommunications networks based on Low Altitude Platforms (LAPs) are expected to optimally meet the urgent needs of emergency relief and recovery operations for tackling large scale natural disasters. The energy efficient operation of such networks is important given the fact that the entire network infrastructure including the battery operated ground terminals, exhibit requirements to operate under power constrained situations. In this paper, we show that the cooperation between mobile terrestrial terminals on the ground could improve the energy efficiency in the uplink depending on the temporal behavior of the terrestrial and the aerial uplink channels. We consider cognitive context aware capabilities that could be utilized to improve the overall lifetime of the mobile ground terminals by dynamically adapting between direct and cooperative relay links. We also discuss a novel network architecture constituted of an integrated and highly dynamic multi-purpose aerial-terrestrial communications infrastructure that can be contextually extended with fast-deploying aerial platforms for emergency communications.

Excessive energy consumption has been recognized as a number one problem that may weaken the growth in future communication networks. This means that the main effort of nowadays wireless communications research shall be moved to energy efficient solutions. One of the most interesting approaches to achieve energy efficiency is to enable the short-range cooperative strategies among multi-standard mobile devices. The short-range cooperative strategies are highly beneficial in terms of energy savings, as they shift the wireless communications paradigm from high power cellular interfaces to low power WPAN interfaces. Keeping this in mind, our proposed clustering framework builds a solid fundament to bring short-range cooperative strategies into the wireless world. The key features of our method are robustness, simplicity and reliability, which are achieved via novel mechanisms of silent association and discrimination of non-persistent neighbors. Herein we present the analysis of state of the art clustering designs, which is followed by the detailed description of the presented method along with the results, from WiMedia MAC ns-2 simulator, that verify principles of the proposed method.

In this paper we study the delay characteristics of cooperative communications using opportunistic relay access with delay constraints. We consider the scenario where the source node opportunistically tries to access the relay, amongst several Primary Users (PU) of the relay network in the environment, in order to send data across (a fixed) network to its destination. The first-hop connection for the source node to the relay is considered as an opportunistic access link. The traffic from the PUs in the first-hop and the delay associated in getting the data across the network to the destination from the relay node are modeled as random processes. Considering such a link and a random network-delay model we study the probability of successful end-to-end transmission by the source node with a given delay constraint. For the considered model, we present closed form analytical expressions for the success probability, the mean delay and the expected delay jitter for the end-to-end transmission. We also present Monte-Carlo based simulation results to verify our theoretical analysis.

This paper introduces a reduced complexity approach to cyclostationarity detection of primary user signals in cognitive radio systems. The decision statistic is formulated by exploiting the variations of portions the sensed signal's spectral correlation density function estimate, due to either the presence or absence of the primary user signal. The presented detector has low computational complexity and has decision error performance which is superior to energy detection; these qualities make it suitable for energy-efficient cognitive radio terminals operating with limited power sources. The performance of this detection method is obtained by computer simulation and the computational complexity is evaluated.

In this paper we present the concept of cognitive satellite terrestrial radios (CSTR) for hybrid satellite-terrestrial systems (HSTS). The cognitive radio (CR) technology is being motivated by the radio regulatory bodies for efficient utilization of the radio spectrum. The future satellite ground terminals therefore need to integrate and co-exist with the spectrally crowded terrestrial wireless systems and hence we propose the idea of CSTR. The key issues are addressed and the concepts of developing CR based satellite ground terminals in HSTS for dynamic spectrum access on the ground are presented. Furthermore, the concept of 3D-Spatial reuse of the spectrum is also presented using the CSTR considering low elevated satellite ground stations. Two particular HSTS applications to illustrate the CSTR concept, a) the hybrid satellite-UWB (ultra wideband) communication system for personal area networks (PAN) with short range on ground communications, and b) the hybrid satellite-WRAN (wireless regional area networks) for long range on ground communications are presented. In both the applications the satellite uplink and the terrestrial radios adopt the CR functionalities. The key enabling technologies and their integrated architecture for the CSTR are also presented.

In this paper we consider cooperative spectrum sensing to detect incumbent spectrum users (ISU) in cognitive radio (CR) networks. We propose a time-divisional cooperative periodic spectrum sensing (TD-CPSS) technique and analyze the detection performance based on the blind energy based detection scheme. The CR detects the presence of the ISU by means of TD-CPSS and opportunistically uses the spectrum for secondary communications. The proposed technique saves energy at the local CR nodes due to periodic sensing and at the same time maintains the minimum required detection probability by optimizing the sensing period. The detection probability together with the false alarm probability are derived for the TD-CPSS technique based on the additive noise at the sensing node and the temporal statistics of the ISU transmissions. In our model, we consider additive white Gaussian noise (AWGN) for local sensing and the Poisson-Pareto spectral occupancy model for the temporal behavior of the ISU transmissions. We also provide expression for the required time period for the proposed sensing technique which attains the minimum required detection probability whilst minimizing the energy consumption considering the noise and temporal statistics.

In this paper we study the performance of periodic spectrum sensing in cognitive radios (CR) based on experimentally obtained temporal spectral occupancy statistics of the legacy user (LU). The detection performance of the periodic sensing technique depends on the occupancy statistics of the LU such as the traffic arrival rate and the occupancy duration. The sensing period and the sensing duration requirements also depend on the temporal statistics of the LU in order to achieve a predefined detection probability. The LU services that we consider here are the UMTS voice and the HSDPA data services that operate in the 2100MHz frequency range. The UMTS and HSDPA spectral occupancy behaviors are experimentally analyzed using real world measurements performed by Telefónica I+D in order to find the white spaces or spectrum holes associated with it, which are then used to study the detection performance of the periodic sensing technique. The temporal characteristics such as the occupancy distribution and the approximated occupancy-time plot are presented for the UMTS/HSDPA services based on the experimentally obtained data. The probability of detection is then presented with respect to the sensing period and the duration, based on a temporal spectral occupancy model that we derive from the experimentally obtained occupancy statistics.

The arctan function is a well-known Maximum Likelihood (ML) estimator of the phase angle α ∈ [-π,π) of a complex signal in additive white Gaussian noise. In this paper we revisit the arctan-based ML phase estimator and identify the bias problem for phase tracking. We show that the posteriori probability density function of α becomes a bi-modal distribution for small values of signal to noise ratio ρ and larger values of α. In such cases the mean and the mode differ from each other, and as a result when such ML phase estimates are used as an input to a linear system (LS), example for phase tracking, the resulting output (which is essentially the mean value of the phase) differs from its true value which is the mode. In such situations there exist a mean (tracking) error at the output of the LS from its true value α, and in (non-Bayesian) statistical terms there exist a bias in the estimates. In this paper, we provide some statistical analysis to explain the above problem, and also provide solutions for bias correction when a LS is used for tracking phase. Furthermore, we also provide two nonlinear phase tracking systems, 1) a Monte-Carlo based sequential phase tracking technique and 2) a second-order digital-phase locked loop based method, for bias-free phase tracking which eliminate the bias problem that occurs in the case of linear phase tracking with ML estimates.

Most critical component of the cognitive radio paradigm is spectrum sensing and accordingly, detection of primary users. Recently proposed cooperative spectrum sensing methods do not consider errors occurring during the transmission of local cognitive radio decisions to the cognitive base station. However, perfect communication is clearly not the case in realistic cooperative spectrum sensing scenarios and might lead to misleading performance result interpretations. In this paper, we extend the simple cooperative spectrum sensing communication model to admit transmission imperfections. Specifically, we consider the case where the local hard cognitive radio decisions that are based on any local detection scheme are corrupted by additive noise during transmission from cognitive radios to cognitive base station. Utilizing this extended cooperative spectrum sensing model, we present the complex optimal and a practical and effective suboptimal detector that is capable of operating with any local cognitive radio detection scheme: Two-step detector. We present simulation results investigating the performance of the proposed detectors and the effect of parameters of interest such as number of cognitive radios and signal to noise ratio. Moreover, we provide comparisons between non-cooperative and cooperative spectrum sensing performances revealing some cases where non-cooperative scheme is more effective.

Cooperative spectrum sensing for cognitive radio networks is recently being studied to simultaneously minimize uncertainty in primary user detection and solve hidden terminal problem. Sensing wideband spectrum is another challenging task for a single cognitive radio due to large sensing time required. In this paper, we introduce a technique to tackle both wideband and cooperative spectrum sensing tasks. We divide the wideband spectrum into several subbands. Then a group of cognitive radios is assigned for sensing of a particular narrow subband. A cognitive base station is used for collecting the results and making the final decision over the full spectrum. Our proposed algorithm minimizes time and amount of energy spent for wideband spectrum scanning by a cognitive radio, and effectively detects the primary users in the wideband spectrum thanks to cooperative shared spectrum sensing.

In this paper we present some experimental results on the cyclostationarity properties of the IEEE802.11n Wi-Fi transmissions, and the usage of the cyclostationarity features to detect IEEE802.11n radios in the context of ultra wide band (UWB) based cognitive radios (CR). The IEEE 802.11n lies in the UWB frequency range, at 5.2GHz, and the CR needs to successfully detect the legacy user to avoid interference. By performing cyclostationarity analysis we compute the cyclic power spectrum (CPS) of the IEEE802.11n transmissions and use it for detecting the presence of the legacy user. We conduct experimentations to perform our study under different channel conditions and perform post signal analysis to measure the detection performance using the CPS based detection technique. The probability of false alarm and the probability of missed detection are computed and the results are presented for different channel conditions, together with the CPS of the legacy user's signal.

In this paper we consider cooperative localization and tracking of primary users (PU) in a cognitive radio network using Bayesian techniques. We use particle filtering methods to track the location of a PU in the network using cooperative localization techniques and present some results for noisy measurements. The cognitive radio (CR) nodes estimate the information related to the geographical position of the PU based on existing location identification and localization techniques and forward the noisy information to a cognitive radio base station (CRB), which then fuses the information to estimate the position of the PU in the network in order to perform a radio scene analysis. We propose a particle filtering approach that is suitable for tracking Gaussian and non-Gaussian noisy signals at the CRB to estimate the position of a PU, two importance-functions relative to the particle filtering algorithm are also presented. Simulations are performed on the proposed tracking algorithm and the results are presented in terms of the mean squared error of the positional estimates.

A hyperbolic arctan based digital tanlock loop (D-TLL) operating with complex signals at base-band or intermediate frequencies in high Doppler environments is treated here. The arctan based loop, known as the tanlock loop (TLL), is used in software defined radio architectures for frequency acquisition and tracking. The hyperbolic nonlinearity intentionally introduced within the phase detector extends the pull-in range of the frequency for a given loop, compared to the normal D-TLL, allowing a wider frequency acquisition range which is suitable for high Doppler communications environment. In this paper we study the steady state phase noise performances of such a feedback loop for additive Gaussian noise using stochastic analysis. The stochastic model of a first-order hyperbolic loop and the theoretical analysis for the corresponding statistical distribution of the closed loop steady state phase noise are presented. The theoretical results are also verified by simulations.

In this paper we present a novel frequency estimation technique, assisted by an imperfect second order arctan based Digital Phase-Locked Loop (D-PLL), for complex single sinusoidal signals in additive white Gaussian noise. The imperfect loop contains the frequency information in its phase error process, at steady state, which is then used to estimate the frequency after the signal has been acquired by the D-PLL. For N samples, this particular estimator has a lower bound on the frequency error variance proportional to 1/N which is different to the well known Maximum Likelihood (ML) estimator that has a lower bound proportional to 1/N3known as the Cramer-Rao bound (CRB). However, for given combinations of the loop parameters of the D-PLL, the error variance of the proposed estimator gives an improvement over the CRB for small values of N. We also present some detailed noise analysis on the proposed frequency estimator and derive a lower bound for the frequency error variance.

Nonlinear system design is a common practice in communication engineering for improved performances in advanced receivers. Here we look into a logarithmic based nonlinear loop for a second order arctan based digital phase locked loop (D-PLL) for frequency synchronisation. The steady state and the acquisition performances of the loop are analyzed. The logarithmic nonlinearity is intentionally introduced to have improved phase noise performances during the steady state tracking mode. We present a close form expression for the open loop statistical distribution of the phase noise process and compare it with the linear PLL model on its performances. We also study the acquisition process of the loop by looking at the phase plane trajectories.

Multicarrier system such as orthogonal frequency division multiplexing (OFDM) is considered as a promising candidate for wireless networks that support high data rate communication. In this paper, we investigate the performance of a multi-user OFDM system under imperfect synchronization which is caused due to noise, Doppler shift and frequency selective fades in the channel. Monte Carlo analysis indicates an average of 22% loss in the number of allocatable subcarriers under imperfect synchronization as compared to perfect synchronization. As the number of users is increased, the average number of available subcarriers remains moderately constant, but the minimum and maximum number of allocatable subcarriers varies significantly. Based on empirical modelling, we characterize the available number of subcarriers as a Poisson random variable. In addition, we determine the percentage decrease in the total number of allocatable subcarriers under varying channel parameters. The results indicate 19% decrease in the number of available subcarriers as average AWGN power is increased by 10 dB; 44% decrease as the Doppler frequency is varied from 10 Hz to 100 Hz; and 56% decrease as the fading gain is varied from 0 dB to -30 dB. Hence, determining the availability of subcarriers under imperfect synchronization is essential in optimization process of radio resource allocation for multiuser systems.

A statistical analysis of the single-delay multiplication based frequency estimator is treated here. The single-delay multiplication frequency estimator for complex single sinusoid signals is a phase averaged estimator, which is similar to the Kay's estimator. Here we provide a study on the statistical distribution of the frequency estimates made by the estimator, and verify the analytical results using simulations. It is shown that the analytical results hold true even at very low signal to noise ratio levels. In deriving the probability density function of the frequency estimates, we explore the cross product noise terms resulting due to the multiplication operation of the estimator by making valid assumptions.