Publisher Copyright: © 2026 IEEE. EC/HE/101192113/EU//AMBIENT-6G

Ambient backscatter communication (AmBC) has become an integral part of ubiquitous Internet of Things (IoT) applications due to its energy-harvesting capabilities and ultra-low- power consumption. However, the open wireless environment exposes AmBC systems to various attacks, and existing authentication methods cannot be implemented between resource-constrained backscatter devices (BDs) due to their high computational demands. To this end, this paper proposes PLCRA-BD, a novel physical layer challenge-response authentication scheme between BDs in AmBC that overcomes BDs’ limitations, supports high mobility, and performs robustly against impersonation and wireless attacks. It constructs embedded keys as physical layer fingerprints for lightweight identification and designs a joint transceiver that integrates BDs’ backscatter waveform with receiver functionality to mitigate interference from ambient RF signals by exploiting repeated patterns in orthogonal frequency division multiplexing (OFDM) symbols. Based on this, a challenge-response authentication procedure is introduced to enable low-complexity fingerprint exchange between two paired BDs leveraging channel coherence, while securing the exchange process using a random number and unpredictable channel fading. Additionally, we optimize the authentication procedure for high-mobility scenarios, completing exchanges within the channel coherence time to minimize the impact of dynamic channel fluctuations. Security analysis confirms its resistance against impersonation, eavesdropping, replay, and counterfeiting attacks. Extensive simulations validate its effectiveness in resource-constrained BDs, demonstrating high authentication accuracy across diverse channel conditions, robustness against multiple wireless attacks, and superior efficiency compared to traditional authentication schemes.

Passive eavesdropping compromises confidentiality in wireless networks, especially in resource-constrained environments where heavyweight cryptography is impractical. Physical layer security (PLS) exploits channel randomness and spatial selectivity to confine information to an intended receiver with modest overhead. However, typical PLS techniques, such as beamforming, artificial noise, and reconfigurable intelligent surfaces, often require additional active power or specialized deployment and rely on precise time synchronization and perfect CSI estimation, which limits their practicality. Meanwhile, the role of ambient backscatter devices (AmBDs) in potentially strengthening the legitimate channel while limiting eavesdroppers in generalized wireless network settings has not been fully investigated. To this end, we propose AmbShield, an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers' reception without requiring extra transmit power and with minimal deployment overhead. In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices. We further develop a unified analytical framework that analyzes the exact probability density function (PDF) and cumulative distribution function (CDF) of legitimate and eavesdropper signal-to-interference-noise ratio (SINR), a closed-form secrecy outage probability (SOP), its high-SNR asymptote, and a secrecy diversity order (SDO). The analysis provides clear design guidelines on various practical system parameters to minimize SOP. Extensive experiments that include Monte Carlo simulations, theoretical derivations, and high-SNR asymptotic analysis demonstrate the security gains of AmbShield across diverse system parameters under imperfect synchronization and CSI estimation.

Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban and non-line-of-sight scenarios, thus limiting their effectiveness in practical deployments. To deal with these challenges, this paper proposes a backscatter device (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in underlying environments for performance enhancement. Specifically, the additional reflective signal paths provided by these ambient devices are exploited to enhance sensing accuracy and communication reliability, respectively. In this system, we define the Pareto boundary characterizing the trade-off between sensing mutual information (SMI) and communication rates to provide fundamental insights for its design. To derive the boundary, we formulate a performance optimization problem within an orthogonal frequency division multiplexing (OFDM) framework, by jointly optimizing time-frequency resource element (RE) allocation, transmit power management, and BD modulation decisions. To tackle the non-convexity of the problem, we decompose it into three subproblems, solved iteratively through a block coordinate descent (BCD) algorithm. Specifically, the RE subproblem is addressed using the successive convex approximation (SCA) method, the power subproblem is solved using an augmented Lagrangian combined water-filling method, and the BD modulation subproblem is tackled using semidefinite relaxation (SDR) methods. Additionally, we demonstrate the generality of the proposed system by showing its adaptability to bistatic ISAC scenarios and MIMO settings. Finally, extensive simulation results validate the effectiveness of the proposed system and its superior performance compared to existing state-of-the-art ISAC schemes.

Integrated sensing and communication (ISAC) has emerged as a key enabling technology for next-generation wireless networks, seamlessly combining communication and radar sensing functionalities by utilizing shared wireless resources. However, ISAC systems suffer from diminished sensing accuracy and compromised communication reliability in complex propagation environments, particularly in densely obstructed urban or non-line-of-sight scenarios. To cope with these issues, this paper proposes a novel backscatter device (BD)-assisted ISAC system where passive BDs are utilized to concurrently enhance sensing accuracy and communication reliability by providing additional signal-reflecting paths. Furthermore, a joint time-frequency-power resource optimization problem is formulated between sensing mutual information and communication capacity in an orthogonal frequency division multiplexing (OFDM) setting. Then, a Pareto boundary is characterized by solving this dual problem, providing clear insights into fundamental tradeoffs involved between sensing and communication. Specifically, we decompose the formulated optimization problem into two manageable subproblems and iteratively solve them through successive convex approximation (SCA) techniques with a block coordinate descent (BCD) algorithm to address the inherent non-convexity of the problem. Simulation results demonstrate the superior performance of the proposed BD-assisted ISAC system, showing substantial improvements in both sensing and communication metrics compared to state-of-the-art ISAC methods.

Backscatter communication (BC) becomes a promising energy-efficient solution for future wireless sensor networks (WSNs). Unmanned aerial vehicles (UAVs) enable flexible data collection from remote backscatter devices (BDs), yet conventional UAVs rely on omni-directional fixed-position antennas (FPAs), limiting channel gain and prolonging data collection time. To address this issue, we consider equipping a UAV with a directional movable antenna (MA) with high directivity and flexibility. The MA enhances channel gain by precisely aiming its main lobe at each BD, focusing transmission power for efficient communication. Our goal is to minimize the total data collection time by jointly optimizing the UAV's trajectory and the MA's orientation. We develop a deep reinforcement learning (DRL)based strategy using the azimuth angle and distance between the UAV and each BD to simplify the agent's observation space. To ensure stability during training, we adopt Soft Actor-Critic (SAC) algorithm that balances exploration with reward maximization for efficient and reliable learning. Simulation results demonstrate that our proposed MA-equipped UAV with SAC outperforms both FPA-equipped UAVs and other RL methods, achieving significant reductions in both data collection time and energy consumption.

Ambient backscatter communication (AmBC) has become increasingly popular for facilitating ubiquitous Internet of Things (IoT) due to its ultra-low-power consumption and energy-harvesting capabilities. However, the open nature of wireless channels and the resource-limited devices in AmBC systems expose them to threats such as identity impersonation and wireless spoofing attacks. Recently, physical layer authentication (PLA) offered a lightweight solution to address the above security challenges by utilizing inherent wireless properties, like channel fading and signal patterns, as unique fingerprints to identify legitimate devices and distinguish them from attackers. Despite its promise, current research still lacks an effective PLA mechanism that can accurately authenticate backscatter devices (BDs) and defend against spoofing attacks in AmBC systems. This paper presents AmbAu, a two-stage PLA scheme designed to authenticate BDs by leveraging the signal correlation between an ambient source and the BD. In the initialization stage, the verifier extracts a complex covariance matrix from the downlink signals between the ambient source and the BD. This matrix is later used during the authentication stage to verify the BD's identity and detect spoofing attempts. Through security analysis, we demonstrate AmbAu's resilience against replay, relay, and counterfeiting attacks. Simulations under various conditions demonstrate AmbAu's accuracy, efficiency, and superior performance when compared to traditional PLA schemes.

The increasing demand for high-quality real-time 360° video streams from mobile platforms, such as 5G-connected Unmanned Aerial Vehicles (UAVs), is challenging modern B5G networks. Vehicular mobility and fluctuating conditions in high-altitude, high-speed scenarios, known as high volatility, complicate maintaining an effective Quality of Experience (QoE) for cellular networks. This work introduces FlyBit, a Deep Reinforcement Learning (DRL)-based bitrate selection framework for live 360° video streaming in 5G-connected UAV applications, designed to enhance video quality, reduce packet loss, and minimize End-to-End (E2E) latency. We developed and deployed a real-world testbed to evaluate the impact of dynamic network conditions, UAV mobility, and trajectory on streaming performance, analyzing FlyBit with real-world data. Experimental results show that FlyBit improves Video Multimethod Assessment Fusion (VMAF) by ~29% and average bitrate by ~50%, while maintaining low latency and packet loss compared to baseline approaches, demonstrating its ability to adjust bitrate in real-time and significantly improve QoE for ultra-low-latency video streaming.

5G and the future 6G are proposed for industrial environments where reliability and low latency processing is required for factory automation. TSN has been used in fixed networks to deliver highly reliable deterministic communications. The 6G core can be used to support services but requires re-designing the user plane to support TSN service specific requirements. This paper presents a novel design for integrating unlicensed DECT radio technology as part of 6G Radio Access Network (RAN) to support services with deterministic requirements such as TSN. Design and implementation of SW gNB using DECT as radio technology and interaction with 5G network to support TSN is presented.

This paper investigates the impact of deploying the fluid antenna system (FAS) on the performance of covert communications. In particular, we focus on a scenario where a transmitter seeks to covertly communicate with a receiver, while a warden attempts to detect the transmission. Both the receiver and the warden are assumed to utilize planar FAS. We derive compact analytical expressions for the covertness outage probability (COP), defined as the complement of the sum of false alarm (FA) and missed detection (MD) probabilities. By determining the optimal detection threshold that maximizes the COP, we characterize the success probability for the legitimate transmission, highlighting the trade-off between covertness and transmission success. Our numerical results confirm that while deploying FAS at the warden enhances its detection ability compared to fixed-position antennas (FPAs), equipping the receiver with FAS rather than FPAs significantly improves reception quality, leading to more reliable transmission.

Reconfigurable Intelligent Surfaces (RIS) are gaining attention for enhancing localization in Global Navigation Satellite System (GNSS)-denied environments. This paper investigates a single-input single-output (SISO) system for localizing a receiving drone assisted by two mobile RIS-equipped drones while mitigating interference signal from a jamming drone. The RIS drones dynamically steer reflections to enhance signal quality and enable accurate angle-of-departure (AOD) estimation. An adaptive Least Mean Squares (LMS) algorithm is applied to nullify jamming interference and maximize signal-to-interference-plus-noise ratio (SINR).The simulation results demonstrate that the proposed dual-RIS framework significantly improves localization accuracy, even in the presence of a jammer signal. We evaluate the performance of our approach, demonstrating robust AOD estimation and accurate 3D position of the receiving drone, even under severe jamming conditions. These results validate the effectiveness of our method in enhancing localization accuracy in adversarial environments.

In-body communication is a key enabler for next-generation healthcare applications, allowing seamless networking of implants. Fat tissue, with its lower water content and reduced signal attenuation compared to other body tissues at microwave frequencies, has emerged as a promising medium for radio-based in-body networks. Despite this advantage, signal leakage through the body can compromise privacy, exposing sensitive data and the mere presence of implants to external adversaries. This paper investigates the feasibility of covert communication in fat tissue-based in-body networks by leveraging the previously unexplored signal attenuation properties of human tissue to transmit data undetectable to adversaries, ensuring privacy beyond encryption. We develop a system in which an implanted transmitter communicates discreetly with an implanted receiver, shielded from external passive eavesdroppers. Our theoretical analysis and experimental results demonstrate that the attenuation properties of human tissues enable covert communication at reduced transmit power levels without requiring friendly jamming, unlike over-the-air systems. To further enhance covertness, we explore the use of an external friendly jammer and show its significant benefits. Experimental results show a 500% increase in the maximum channel capacity of covert communication, from 2.86 bps/Hz at -56 dBm transmit power without jamming, to 17 bps/Hz with no bit errors at 0 dBm transmit power with a friendly jammer, using the IEEE 802.15.4 standard for communication in the 2.45 GHz frequency band. These findings highlight that covert communication is achievable in fat tissue-based in-body networks at low data rates without additional infrastructure such as an external jammer. For applications requiring higher data rates, a friendly jammer offers a scalable solution, making this approach practical for a wide range of implant communication scenarios.

The rapid evolution of communication technologies and the emergence of sixth-generation (6G) networks have introduced unprecedented opportunities for ultra-reliable, low-latency, and energy-efficient communication. Integrating technologies like non-orthogonal multiple access (NOMA) and wireless powered communication networks (WPCNs) brings new challenges. These include energy constraints and increased security vulnerabilities. Traditional antenna systems and orthogonal multiple access schemes struggle to meet the increasing demands for performance and security in such environments. To address this gap, this paper investigates the impact of emerging fluid antenna systems (FAS) on the performance of physical layer security (PLS) in WPCNs. Specifically, we consider a scenario in which a transmitter, powered by a power beacon via an energy link, transmits confidential messages to legitimate FAS-aided users over information links while an external eavesdropper attempts to decode the transmitted signals. Additionally, users leverage the NOMA scheme, where the far user may also act as an internal eavesdropper. For the proposed model, we first derive the distributions of the equivalent channels at each node and subsequently obtain compact expressions for the secrecy outage probability (SOP) and average secrecy capacity (ASC), using the Gaussian quadrature methods. Our results reveal that incorporating the FAS for NOMA users, instead of the TAS, enhances the performance of the proposed secure WPCN.

The use of Unmanned Aerial Vehicles (UAVs) as aerial base stations has attracted increasing research interest in recent years. A key challenge in this field is determining how to deploy multiple UAVs in dynamic environments, particularly where mobile user demands fluctuate. To address this challenge, this paper presents an adaptive UAV deployment scheme in a dynamic multi-UAV wireless network, considering the mobility of UAVs and users, state variability, and adjustable UAV transmission power. By jointly optimizing the UAVs’ operational modes, transmission power levels, and movement strategies, our objective is to achieve a trade-off between minimizing power consumption and maximizing ground user coverage. A Deep Reinforcement Learning (DRL) approach is proposed to address these challenges. To capture the dynamic variations of users and UAVs in the environment, a multi-modal feature state space is designed, consisting of both a multi-channel image and vectors. The image component integrates real-time data on user distribution and the UAV coverage area, while the vectors represent UAV operational modes, position data, and system temporal information. These multi-modal features are processed using a combination of Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) for advanced feature extraction. To enhance training stability and efficiency, the proposed approach updates parameters using the Proximal Policy Optimization (PPO) method. Simulation results demonstrate the effectiveness of the proposed scheme in balancing power consumption and coverage while effectively managing system dynamics.

The rapid growth of smartphones, laptops, and IoT devices has created an increasing demand for seamless, secure, and energy-efficient wireless direct communication. Current popular solutions, such as AirDrop and Intel Unison, rely on Bluetooth Low Energy (BLE) for device discovery and pairing, alongside IEEE 802.11 (Wi-Fi) for high-speed data transmission. However, research has identified significant vulnerabilities in BLE, including information leakage and profile manipulation. To address these challenges, we propose an Enhanced Wireless Direct Connectivity (EWDC) framework that integrates Digital Enhanced Cordless Telecommunications (DECT)-2020 New Radio (NR) with Wi-Fi. In this framework, DECT replaces BLE to enable secure and efficient device discovery, while Wi-Fi supports high-bandwidth data transmission. Specifically, DECT utilizes its licensed spectrum to establish a secure communication channel for sharing Wi-Fi configuration information, enabling a secure and reliable setup of high-speed Wi-Fi data transmission links between devices. To validate this approach, we implemented the DECT MAC layer on the Nordic nRF9161-DK platform and evaluated the end-to-end delay between two devices. Our results demonstrate an impressive transmission delay of less than 1 μs for EWDC communication, enabling rapid device discovery and highly efficient operation. Furthermore, to enhance the security of wireless direct connections, the WireGuard, a VPN tunnel, is employed to encrypt data transmission over Wi-Fi providing robust protection for data shared on public frequency bandwidths. The proposed hybrid DECT-2020 NR and Wi-Fi solution significantly improves reliability, security, and communication efficiency, positioning it as a robust candidate for next-generation IoT communication systems.

This paper proposes a novel physical layer authentication (PLA) scheme for backscattering tag-to-tag networks (BTTNs), where aTalker Tag(TT) communicates passively with a Listener Tag (LT) in the presence of a potential adversary. Designed for ultra-low power tags without cryptographic capability, the proposed PLA leverages the unique voltage profiles generated by the tags’ energy harvesting and demodulation circuits to form physical-layer signatures for authentication. In addition, to enhance the reliability of voltage measurements, especially under weak signal conditions inherent within BTTNs, an indoor reconfigurable intelligent surface (RIS) is integrated to improve the received signal quality at LT. The proposed approach maintains a high authentication success rate even as the distance between TT and LT increases. A detailed security analysis demonstrates strong resilience against impersonation, man-in-the-middle, relay, and replay attacks, as long as the RIS controller remains secure. This robustness stems from the adversary’s inability to recreate the exact voltage profiles at LT, due to the inherent location-specific channel characteristics and the RIS-assisted signal shaping that the attacker cannot replicate. Furthermore, the simulation results confirm the effectiveness of the proposed RIS-assisted PLA, showing significant gains in authentication performance and secrecy capacity across diverse deployment scenarios.

The Ambient Internet of Things (AIoT), recently standardized by the 3rd Generation Partnership Project (3GPP), demands a low-power wide-area communication solution. Ambient Backscatter Communication (AmBC) is a promising approach that enables Backscatter Devices (BDs) to communicate by reusing existing signals without additional spectrum or energy cost. This paper investigates the feasibility of AmBC in the uplink of Long Term Evolution (LTE) cellular systems. We model the received BD signal, derive the optimal Maximum A Posteriori (MAP) detector, and propose simplified receiver architectures that achieve near-optimal performance with reduced complexity. A theoretical Bit Error Rate (BER) analysis is developed, including a Gaussian approximation, and the achievable coverage is evaluated. The results show that AmBC can sustain a BER on the order of 10−2at a reading distance of 1.5 m. Over-the-air experiments using commercial LTE uplink Sounding Reference Signals (SRS) further confirm the theoretical and simulation results. Overall, this work demonstrates that LTE cellular systems can natively support AmBC in the uplink, reinforcing its potential as a symbiotic radio paradigm for future low-power, spectrum-efficient AIoT connectivity.

Backscatter communication (BC) enables resource-constrained backscatter devices (BDs) to communicate by reflecting signals from external radio frequency sources (RFSs), thereby avoiding active RF components, making it a cutting-edge technology for the ubiquitous Internet of Things (IoT). However, the open nature of BC makes it vulnerable to passive and active attacks, and existing methods fail to offer robust mutual authentication suitable for mobile BC systems while keeping a low computational overhead. To address this issue, we propose AuthScatter, an accurate, robust, and scalable physical-layer mutual authentication scheme between the RFS and multiple BDs by leveraging channel fading and random numbers as a one-time pad to protect the identity key exchange procedure during the authentication. Specifically, AuthScatter constructs shared identity keys as physical-layer fingerprints for efficient identification and employs a challenge-response authentication mechanism to enable secure key exchange between the RFS and the BD. In the authentication, the one-time pad effectively prevents eavesdropping, spoofing, replay, and counterfeiting attacks, while legitimate devices leverage channel reciprocity and random number knowledge to authenticate efficiently without channel estimation or complex processing. It is tailored for high-mobility scenarios by completing the exchange within the channel coherence time while incorporating a key-update mechanism to ensure sustained security in the long term. Additionally, it includes a re-authentication mechanism to enhance resistance against wireless attacks and a batch authentication framework leveraging time-division duplexing (TDD) to enable scalability in large-scale BC deployments. Comprehensive security analysis demonstrates the resistance of AuthScatter to various threats, including eavesdropping, identity spoofing, replay, and counterfeiting attacks. Extensive simulations further validate its high authentication accuracy across diverse channel conditions, robustness against various attack vectors, and scalability with a large number of BDs, highlighting its superiority over state-of-the-art schemes.

Using Unmanned Aerial Vehicles (UAVs) as aerial base stations for providing services to ground users has received growing research interest in recent years. The dynamic deployment of UAVs represents a significant research direction within UAV network studies. This paper introduces a highly adaptable UAV wireless network that accounts for the mobility of UAVs and users, the variability in their states, and the tunable transmission power of UAVs. The objective is to maximize energy efficiency while ensuring the minimum number of unserved online users. This dual objective is achieved by jointly optimizing the states, transmission powers, and movement strategies of UAVs. To address the variable state challenges posed by the dynamic environment, user and UAV data is encapsulated within a multi-channel map. A Convolutional Neural Network (CNN) then processes this map to extract key features. The deployment and power control strategy are determined by an agent trained by the Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm. Simulation results demonstrate the effectiveness of the proposed strategy in enhancing energy efficiency and reducing the number of unserved online users.

With the increasing number of wireless devices connecting to networks and sharing the same spectrum resources, interference has become a significant obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and mainly focus on suppressing or eliminating its impact on desired transmissions. However, this often comes at the cost of consuming communication resources. Therefore, design of low-cost IM method that can exploit interference is of research importance. To achieve this goal, we leverage the cost-effectiveness and adaptable deployment capabilities of Intelligent Reflecting Surface (IRS) to propose an IRS Empowered Interference Utilization (IRS-IU) method to realize efficient desired data transmission. By appropriately designing the reflection coefficient of the IRS, a phase shift is introduced to the incident interference, allowing the reflecting interference to interact with its direct counterpart at the interfered receiver (Rx). As a result, the interfered Rx can retrieve its desired data from the mixed interference. In this way, IRS-IU can make full use of the interference to enhance the desired data transmission. Our theoretical analysis and simulation results show that the proposed method can significantly improve the spectral efficiency (SE) of the interfered communication-pair.

The Long Term Evolution (LTE) is a promising can-didate to be used as a signal source in the Ambient Backscatter Communications (AmBC). LTE provides ubiquitous coverage, and LTE User Equipment (UE), such as mobile phone, can be used as the reader of a Backscatter Device (BD) signal with small firmware modifications. In this paper we measure a BD signal indoor coverage with commercial LTE base station as the illuminating ambient signal. The reader is synchronized to the LTE BS and estimates the BD signal from the LTE signal pilots. The BD is fully passive powered by a solar panel. The BD locations are distributed spatially along the corridor and for each BD location we measure the AmBC reader signal Bit Error Ratio (BER). The signal is uncoded. For each BD location the BER is measured from different directions with respect to the BD position. The measurement results show that along the corridor in most location, the observed BER is in order one percent. The results shed light on the feasibility of applications where the UE is in close proximity to the BD.

Ambient backscatter communication (AmBC) is a key enabler for green Internet of Things (IoT) employing ambient radio frequency (RF) signals for low-power communication. The AmBC devices discussed in the literature so far harvest light to power their microcontrollers for controlling RF modulation and reflection circuits. The ubiquitous presence of configurable light-emitting diodes (LEDs) based luminaires can also leverage such control functionalities by involving visible light communication (VLC). In this paper, we propose a visible light-enabled AmBC system that integrates VLC and AmBC for future ambient power-enabled IoT. We design LiBD, a backscatter device (BD) that uses visible light signals for both modulation control and energy supply. We demonstrate the real-time end-to-end data transmission with a proof of concept experiment. Evaluation results show that the LiBD supports multiple sub-6 GHz RF bands and can receive modulated light at frequencies up to 250 kHz. The investigations verify the feasibility of integrating the VLC and AmBC for future green IoT systems.

The deployment of fifth-generation (5G) open radio access network (RAN) introduces new opportunities for wireless communication, particularly in the context of x-Haul wireless links. This paper presents a comprehensive demonstration of how photonic-generated sub- THz at 120 GHz was utilized for wireless backhaul within the 5G open RAN platform. The experimental setup highlighted the feasibility and effectiveness of establishing a sub- THz link as the backhaul, marking a significant advancement in this emerging field. Through extensive performance analysis, we evaluated 5G network's throughput, achieving approximately 957 Mbps for downlink with UDP and 269 Mbps with TCP. In the case of uplink, both UDP and TCP exhibited comparable speeds, both exceeding 80 Mbps. This work marks a significant achievement in advancing the development of photonic-generated sub- THz wireless links, holding the potential to enhance wireless transport capabilities, particularly in the context of advanced 5G networks and beyond..

The utilization of millimeter-wave (mm Wave) fre-quencies presents a challenge due to their susceptibility to significant attenuation when encountering obstacles. Providing 5G cellular services using these frequencies has proven to be challenging. However, they exhibit suitability for fixed wireless access (FWA) applications. Fixed wireless links remain relatively stationary, allowing for network design centered around line-of-sight (LoS) coverage. This study delves into the investigation of LoS coverage by making use of existing base transceiver station (BTS) sites in a rural region of Finland. Laser-scanned geospatial data is employed to assess LoS coverage, considering both BTS and customer premise equipment (CPE) antenna heights. Additionally, measured throughput data from commercial 5G devices is utilized to estimate feasible user throughput. The assessment is carried out in the rural area of Pornainen, Finland, covering a total of 764 households and three pre-existing BTS sites. The analysis encompasses BTS tower heights ranging from 30 m to 400 m. Through this analysis, the study presents LoS coverage patterns for various combinations of BTS tower and CPE heights. Notably, the research demonstrates that residences with established LoS connections have the potential to achieve downlink throughput exceeding 3 Gbps at 26 GHz. This finding underscores the feasibility of high-speed connectivity in areas where LoS conditions are favorable, highlighting the potential for effective FWA deployment in rural settings.

Channel charting (CC) is an unsupervised machine learning framework for learning a lower-dimensional representation of Channel State Information (CSI), while preserving spatial relations between CSI samples. In this paper, we consider super-resolution features in the angle-delay domain in massive Multiple-Input Multiple-Output (MIMO) systems. We i) treat the angle and delay separately, ii) present the so-called “Normalized Polar Feature” utilizing the channel statistics of the CSI samples, iii) use the Euclidean distance to compute the dissimilarity matrix, and create the channel chart. Simulation results based on the DeepMIMO data-set show that the proposed super-resolution representation with the Euclidean distance leads to the state-of-the-art quality CC as compared to other CSI features and distances from the literature such as angle-delay-power features with earth mover distance.

In the ambient backscatter communication (AmBC) system, a backscatter device (BD) transmits its messages to receivers by modulating and reflecting incident radio frequency (RF) signals from ambient RF emitters. Most existing studies investigate single-antenna BDs and fabricate them using the conventional printed circuit board (PCB) method. In this paper, we propose flexible inkjet-printed integrated backscatter devices (IBDs) for indoor backscatter radios. We investigate and fabricate both single-antenna and multi-antenna IBDs using the inkjet printing technique. Since the commonly used substrate polyethy-lene terephthalate (PET) for inkjet-printed electronics has highly lossy properties, the printed feeding lines in the RF become inefficient. We propose to overcome this problem by positioning the backscatter switch next to each antenna and controlling them simultaneously with the baseband backscatter signal. Attenuation of the baseband control signal in printed lines is low leading to increase in overall efficiency of the proposed system. We validate the prototypes with received backscatter signal strength measurements in an anechoic chamber. The result shows that the printed four-antenna IBD can benefit approximately 9 dB diversity gain compared with the single-antenna version.

This paper studies the performance of a wire-less powered communication network (WPCN) under the non-orthogonal multiple access (NOMA) scheme, where users take advantage of an emerging fluid antenna system (FAS). More precisely, we consider a scenario where a transmitter is powered by a remote power beacon (PB) to send information to the planar NOMA FAS-equipped users through Rayleigh fading channels. After introducing the distribution of the equivalent channel coefficients to the users, we derive compact analytical expressions for the outage probability (OP) in order to evaluate the system performance. Additionally, we present asymptotic OP in the high signal-to-noise ratio (SNR) regime. Eventually, results reveal that deploying the FAS with only one activated port in NOMA users can significantly enhance the WPCN performance compared with using traditional antenna systems (TAS).

A smart grid (SG) is an advanced electrical grid that enhances the efficiency and reliability of traditional power grids. Reconfigurable intelligent surfaces (RISs) have been recently proposed to enhance communication performance in the SG. However, the communication links between different SG components could suffer from eavesdropping and unauthorized access, which makes physical layer security (PLS) a promising solution for addressing these concerns. In this article, we focus on exploring the effect of applying RIS on enhancing the PLS performance of SG communications. Specifically, we consider an RIS with reflecting elements besides a smart meter, a neighborhood gateway (NG), and an eavesdropper to develop a smart environment in the SG to improve PLS performance in terms of secrecy outage probability (SOP) and average secrecy capacity (ASC). For this purpose, we first derive a probability density function and a cumulative distribution function for the signal-to-noise ratio (SNR) at both the NG and the eavesdropper. Then, we derive closed-form expressions of SOP and ASC to evaluate the impact of various system parameters on the secrecy performance of RIS-aided SG communications. Furthermore, considering the significance of system behavior under high-SNR conditions, we conduct an asymptotic analysis of the SOP and ASC. Finally, we apply the Monte Carlo simulation to validate the analytical results. Our results indicate that using the RIS can significantly enhance the secrecy performance of SG communications compared to the conventional SG scenarios without the RIS.

With the rapid deployment and wide use of mobile services and applications, more and more sensitive user information is being transmitted wirelessly. Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and thus vulnerable to eavesdropping. To counter this vulnerability, we propose a new physical-layer secure transmission scheme, called DDM-Sec, based on decomposed and distributed modulation (DDM). We show that a high-order modulation can be decomposed into multiple quadrature phase shift keying (QPSK) modulations, each of which can be further represented by two mutually orthogonal binary phase shift keying (BPSK) modulations. Therefore, traditional modulation can be realized by two cooperative transmitters (Txs), each generating a BPSK signal, in a distributed manner. The legitimate receiver (Rx) can decode the desired/intended information from the mixed two received BPSK signals while preventing the eavesdropper from accessing the legitimate user's information. DDM-Sec can effectively exploit the randomness of wireless channels to secure data transmission, enrich the spatial signatures of the legitimate user's transmission by employing two cooperative Txs, and then distribute the user's information to two transmissions so that none of the decomposed signals alone carry the legitimate user's full information. Moreover, due to random deployment of the two Txs and Rx, delay difference of the two transmissions is introduced. This can be further utilized to make eavesdropping difficult. Our theoretical analysis and simulation have shown that DDM-Sec can effectively prevent the eavesdropping, and hence guarantee the secrecy of the legitimate user's data transmission.

With the increasing number of wireless devices connecting to networks and sharing spectrum resources, interference has become a major obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and focus on suppressing or eliminating its impact on the transmission of intended signals. However, this often comes at the cost of consuming communication resources and degrading desired transmission performance. Therefore, the design of a cost-effective IM method that “exploits” interference is important. To achieve this goal, we proposeIntelligent Reflecting Surface Assisted Interference Exploitation(IRS-IE) to realize efficient desired data transmission. IRS-IE leverages the low-cost and adaptive deployment capabilities of IRS to gather and reflect interference towards the interfered receiver (Rx). By appropriately designing the reflection coefficient of IRS, a phase shift is introduced to the incident interference, allowing the reflected interference to interact with its direct counterpart at the interfered Rx. As a result, the interfered Rx can retrieve its desired data from the mixed interference. This way, IRS-IE can make use of the interference to facilitate the desired data transmission. Our theoretical analysis and simulation results show that IRS-IE significantly improves the spectral efficiency (SE) of the interfered communication pair over the other IM methods.

Abstract The target of this paper is to show the impact of polarization adaptation on the received signal quality in an outdoor small-cell deployment scenario. The signal-to-interference ratio (SIR) is the key factor in defining the achievable data rate, and the capacity of the cell. At the cell border area, the SIR value is typically low and causes a significant decrease in the system capacity and achievable data rates. These bad SIR areas at the cell border can be improved by using orthogonal polarizations in the neighboring cells rather than using all polarizations over the whole cell coverage area. For the research work of this paper, a series of measurements were carried out to measure a received signal power and a cross polarization discrimination (XPD) ratio while the signal is transmitted and received with a different set of polarizations. In the measurements, we have considered horizontal, vertical, +/− 45° slanted polarizations, and two different environments, urban street and open space, and three frequency bands, 970−1030 MHz, 2000−2030 MHz and 3364−3400 MHz. The measurement results revealed that at a different distance from the transmitter, for horizontal/vertical polarization, the average XPD is around 24.7 dB and 31.7 dB in the urban street and open area environments, respectively. For +/− 45° slanted polarization, the average XPD is around 11.5 dB and 12.1 dB in the urban street and open area environments, respectively. This paper goes on to propose polarization adaptation in each cell, where the primary polarization is valid for the whole service area of the cell, and secondary polarization is only used in close proximity of the base station antenna. Considering the results, it is emphasized that system capacity can be significantly improved by having only one channel with good SIR values compared to multiple channels with bad SIR values. However, MIMO channels with orthogonal polarizations or with spatial multiplexing can be utilized in the closed vicinity of the base station, i.e., in an area with good SIR values. It is shown that the overall cell capacity can be increased by almost 35% by utilizing polarization adaptation compared to MIMO 2 × 2.

Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and thus vulnerable to eavesdropping. To counter this vulnerability, we propose a new physical-layer secure transmission scheme, called DDM-Sec, based on decomposed and distributed modulation (DDM). DDM-Sec realizes traditional QPSK modulation by using two cooperative transmitters (Txs), each generating a BPSK signal, in a distributed manner. The legitimate receiver (Rx) can decode the desired/intended information from the mixed received signal while preventing the eavesdropper from accessing the legitimate user's information. DDM-Sec can effectively exploit the randomness of wireless channels to encrypt data transmission, enrich the spatial signatures of the legitimate transmission by employing two cooperative Txs. Moreover, DDM-Sec distributes user's information to two transmissions so that none of the decomposed signals alone carry the legitimate user's full information. Our theoretical analysis, hardware experiment, and simulation have shown that DDM-Sec can effectively prevent the eavesdropping, and hence guarantee the secrecy of the legitimate user's data transmission.

This paper focuses on secure backscatter transmission in the presence of a passive multi-antenna eavesdropper through a symbiotic radio (SR) network. Specifically, a single-antenna backscatter device (BD) aims to transmit confidential information to a primary receiver (PR) by using a multi-antenna primary transmitter's (PT) signal, where the received symbols are jointly decoded at the PR. Our objective is to achieve confidential communications for BD while ensuring that the primary system's quality of service (QoS) requirements are met. We propose an alternating optimisation algorithm that maximises the achievable secrecy rate of BD by jointly optimising primary transmit beamforming and power sharing between information and artificial noise (AN) signals. Numerical results verify our analytical claims on the optimality of the proposed solution and the proposed methodology's underlying low complexity. Additionally, our simulations provide nontrivial design insights into the critical system parameters and quantify the achievable gains over the relevant benchmark schemes.

This paper addresses the challenging problem of enabling reliable immersive teleoperation in scenarios where an Unmanned Aerial Vehicle (UAV) is remotely controlled by an operator via a cellular network. Such scenarios can be quite critical particularly when the UAV lacks advanced equipment (e.g., Lidar-based auto stop) or when the network is subject to some performance constraints (e.g., delay). To tackle these challenges, we propose a novel architecture leveraging Digital Twin (DT) technology to create a virtual representation of the physical environment. This virtual environment accurately mirrors the physical world, accounting for 3D surroundings, weather constraints, and network limitations. To enhance tele-operation, the UAV in the virtual environment is equipped with advanced features that may be absent in the real UAV. Furthermore, the proposed architecture introduces an intelligent logic that utilizes information from both virtual and physical environments to approve, deny, or correct actions initiated by the UAV operator. This anticipatory approach helps to mitigate potential risks. Through a series of field trials, we demonstrate the effectiveness of the proposed architecture in significantly improving the reliability of UAV teleoperation.

Reconfigurable intelligent surfaces (RISs) are considered as an enabling technology for the upcoming sixth generation of wireless systems, exhibiting significant potential for radio localization and sensing. An RIS is usually treated as an anchor point with known position and orientation when deployed to offer user localization. However, it can also be attached to a user to enable its localization in a semi-passive manner. In this paper, we consider a static user equipped with an RIS and study the RIS localization problem (i.e., joint three-dimensional position and orientation estimation), when operating in a system comprising a single-antenna transmitter and multiple synchronized single-antenna receivers with known locations. We present a multi-stage estimator using time-of-arrival and spatial frequency measurements, and derive the Cramér-Rao lower bounds for the estimated parameters to validate the estimator's performance. Our simulation results demonstrate the efficiency of the proposed RIS state estimation approach under various system operation parameters.

Owing to large spectrum availability, millimeter wave (mmWave) communications are being proposed for 5th generation and beyond networks. However, the mmWaves are easily obstructed by objects, resulting in complete link blockage, which is more common in indoor communication. In this study, we propose to use the existing infrastructure of passive backscatter devices to establish links between the access point and a blocked legacy mobile user, with uncertain coordinates, in an indoor mmWave communication system. We set up backscatter devices in retro-reflective mode for the user to estimate the direction of arrival from them. To estimate the angles, we propose an estimator and derive its Cramer-Rao lower bound. Furthermore, while accounting for the antenna array configuration at both the user and the backscatter devices, we maximize the rate support at the user by optimizing the backscatter device reflection coefficient and the user's steering angle. The simulation results show that, by exploiting the backscatter devices already present in the environment with a sufficient number of antenna elements at the user, higher capacity is achieved as compared to that achieved by using a fixed re-configurable intelligent surface.

Ambient power enabled Internet of Things a.k.a. Ambient IoT is a novel concept connecting battery-free energy autonomous devices to the Internet. In this paper, we demonstrate the feasibility of using the channel estimator at the user equipment (UE) as a receiver for backscatter signals in LTE downlink. Our real-time demonstrator system consists of signal generator that mimics the operation of a base station (BS), a backscatter device (BD) modulating the incidence signal, and a software defined radio receiver that plays the role of the user equipment. The UE channel estimator and BD demodulation software is implemented with C++ and Python, respectively and made open source. Our results indicate that UE can demodulate the BD signal with bit error less than 10-3 when the downlink signal-to-noise ratio is larger than 10 dB when the pathloss between the BD and UE is less than 13 dB.

The cellular-connected Unmanned Aerial Vehicles (UAVs) are emerging as integral components of the 5G and beyond system due to their mobility and flexibility. Compared to a traditional single UAV, a flock of UAVs established as a UAV swarm can implement diverse distributed applications economically and efficiently, such as cooperatively smart agriculture, joint search and rescue, and supplementing temporary network connections. However, the GPS spoofing attack can manipulate UAV swarm locations and distort UAV swarm topology, which threatens the security of swarm communication and control. This paper proposes a Graphic Neural Networks (GNN) based GPS spoofing detection approach for cellular-connected UAV swarms. Especially, we propose a system in which the GNN model is used to detect GPS spoofing attacks by analyzing the similarity between the swarm GPS topology and communications topology. To evaluate the proposed neural networks, we use a bipartite graph and Hungarian algorithm to build a UAV swarm simulator. The results show that GNN can efficiently compute topologies’ similarity and detect GPS spoofing attacks. For instance, for a UAV swarm consisting of 10 UAVs, GNN detects the spoofing with accuracy over 90% and computation time of fewer than 10 milliseconds using Intel Core 1.6 GHz processor.

We implement several low-cost ambient backscatter communication (AmBC) receivers for the first time in practice. Multi-antenna AmBC receivers have been actively investigated theoretically. Practical implementations face a challenge as low-cost antennas are prone to suffer from wideband effect and/or hardware impairments and propagation environment is complicated. For a successful implementation, we propose a signal processing method which avoid the wideband signal but extracts the reference signal which is narrowband. We evaluate the method using the data measured in a complicated environment. Measurement results show that the proposed signal processing tackles the above issues and gives acceptable bit-error-rate (BER) performance of AmBC systems. The implementation of low-cost multi-antenna AmBC receivers paves the way for realizing ubiquitous green communication systems.

This paper presents experimental results of GPS free drone location using two antenna arrays. The experimental platform contains two synchronized stationary 4-by-4 rectangular antenna arrays. The arrays receive the signal emitted from a single antenna mounted on a drone. A Multiple signal classification (MUSIC) algorithm is applied to estimate the angle of arrival (AOA) at each array. The estimated AOA from both arrays is combined by the triangulation technique. We further employed the Extended Kalman Filter (EKF) to mitigate the estimated 3D location errors from the triangulation. To evaluate the performance of the AOA estimation, we conducted several measurements with different types of trajectories using a test-bed. The location estimation results of the proposed method are quantitatively compared and evaluated with the GPS trajectory of the drone. The estimation results have verified that the proposed experimental approach can localize and track the drone with reasonable accuracy.

Internet of Things (IoT) aims to create a vast network with billions of things that can seamlessly create and exchange data, establishing intelligent interactions between people and objects around them. It is characterized with openness, heterogeneity, and dynamicity, which inevitably introduce severe security, privacy, and trust issues that hinder the widespread application of IoT. Trust management (TM) holds great promise in identifying malicious nodes, maintaining trust relationships, and enhancing system security. Traditional TM systems (TMSs) can be classified into centralized, semi-centralized, and distributed ones, all three of which suffer from critical challenges and thus are not sufficient for facilitating IoT development. Blockchain, as a disruptive technology, can help addressing the challenges of TM in IoT, thanks to its advanced features, such as decentralization, consistency, and tamper-proofing. As a result, blockchain-based TM (BC-TM) has been extensively studied in recent years to achieve decentralized TM in IoT. However, it still lacks a comprehensive survey on the current state of the arts. To fill this gap, in this article, we conduct a serious survey on BC-TM in IoT. We first propose a set of evaluation criteria that should be met by a TMS in IoT. Then, we propose a taxonomy of TMSs and continue with a thorough review on BC-TM in IoT by employing the proposed criteria. In the end, based on the review, a series of open issues are identified, and future research directions are suggested.

Ambient backscatter communication (AmBC) is becoming increasingly popular as a green Internet-of-things technology by enabling ultra-low-power data exchanges among simple tags. The bit-error-rate (BER) performance of the AmBC receivers is hampered by the low signal-to-interference-plus-noise ratio (SINR), which limits either the range or the data rate that can be supported by these systems. Among the alternatives, the solutions provided by a multiantenna receiver enable several practical methods to improve the SINR using array signal processing. In this paper, the optimum multiantenna AmBC receiver for any binary modulated tag signal is presented. Two receivers are derived from the maximum-a-posterior probability criterion for deterministic-unknown and for Gaussian distributed ambient signals. The closed-form cumulative distribution functions are derived for both of the receivers for performance evaluation purposes. It is discussed that these two receivers are equivalent under practical conditions, and the optimum receiver is a generalization of the multiantenna receivers available in the literature. Several implementation details are discussed, and numerical evaluation is provided to validate the development. Therefore, the presented receiver accommodates different tag modulations and achieves the best possible BER performance, which opens up new application possibilities by improving AmBC system flexibility.

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation. However, with increased array size in extra-large multiple-input-multiple-output (XL-MIMO) systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviating from the true model is adopted. In this work, we employ the misspecified Cramer-Rao lower bound to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size.

We address the localization of a reconfigurable intelligent surface (RIS) for a single-input single-output multi-carrier system using bi-static sensing between a fixed transmitter and a fixed receiver. Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work. We first derive the Cramér-Rao bounds (CRBs) on the estimation error of the RIS position and orientation and the time of arrival (TOA) for the path transmitter-RIS-receiver. We propose a multi-stage low-complexity estimator for RIS localization purposes. In this proposed estimator, we first perform a line search to estimate the TOA. Then, we use the far-field approximation of the NF signal model to implicitly estimate the angle of arrival and the angle of departure at the RIS center. Finally, the RIS position and orientation estimate are refined via a quasi-Newton method. Simulation results reveal that the proposed estimator can attain the CRBs. We also investigate the effects of several influential factors on the accuracy of the proposed estimator like the RIS size, transmitted power, system bandwidth, and RIS position and orientation.

This paper presents a method for three dimension (3D) drone location estimation based on measured signals transmitted from a flying drone. During the experiment, we considered a single antenna mounted on the drone for signal transmission and a 4-by-4 rectangular array positioned at a known stationary location for receiving the incoming signal. Once the signal strength from the source is measured, the 3D position of the drone is estimated using the MUltiple SIgnal Classification (MUSIC) algorithm. The estimated values are then fed into an Extended Kalman Filter (EKF) as a measurement model, and the movement of the drone is formulated in a 3D trajectory plane. In order to evaluate the performance of our approach, we considered a histogram distribution and probability density function (pdf) of the drone position estimation error during its trajectory. The estimated 3D position of the drone is compared with the GPS values, and we ensure that the drone is localized based on the received signal from the experimental setup by first estimating the direction of the signal using MUSIC, and then tracking it using EKF in the predefined drone trajectory area.

Ultra-wideband terahertz (THz) wireless communication systems are anticipated as a promising solution for applications with high bandwidth requirements in an indoor environment. To design the communication system operating at THz or sub-THz frequencies, it is important to acquire fundamental knowledge about the radio propagation characteristics at those frequencies. The main target of this paper is to conduct power angular spectrum (PAS) measurements at sub-THz frequencies of 90, 95, and 100 GHz in a line-of-sight (LOS) office corridor environment, and validate the ray tracing (RT) simulation results with the measurements. A sophisticated photonics-based measurement setup with high accuracy is utilized for measurement, whereas, an in-house built RT tool is used for simulation. The PAS is measured and simulated at eight different locations and a decent agreement is achieved between both. Whereas the other channel characteristics like root mean square delay spread (RMS-DS) and angular spread (RMS-AS) are only investigated through RT simulations. In the case of direct orientation, the root mean square error of about 1.5, 0.9, and 3.7 dB is found between the measured and simulated received signal power level at 90, 95, and 100 GHz, respectively.

Ambient backscatter communication (AmBC) is an emerging communication technology enabling green Internet of Things deployments. The widespread acceptance of this paradigm is limited by the low signal-to-interference-plus-noise ratio (SINR) of the signal impinging on the receiver antenna due to the strong direct path interference and unknown ambient signal. The adverse impact of these two factors can be mitigated by using noncoherent multiantenna receivers, which are known to require a higher SINR to reach the bit-error-rate (BER) performance of coherent receivers. However, considering the unknown ambient signal, unknown location of AmBC tags, and varying channel conditions, coherent receivers for AmBC systems are rarely studied in the literature. In this article, a coherent multiantenna receiver, which requires no prior information of the ambient signal for decoding the binary-phase-shift-keying (BPSK) modulated signal, is presented. The performance of the proposed receiver is compared with an ideal coherent receiver that has perfect phase information, and also with the performance of a noncoherent receiver, which assumes distributions for ambient signal and phase offset caused by the excess length of the backscatter path. Comparative simulation results show the designed receiver can achieve the same BER performance of the ideal coherent receiver with 1-dB more SINR, which corresponds to 5-dB or more gain with respect to noncoherent reception of on-off-keying modulated signals. Variation in the detection performance with the tag location shows that the coverage area is in the close vicinity of the transmitter and a larger region around the receiver, which is consistent with the theoretical results.

Cellular vehicle-to-everything (V2X) communication is emerging as a feasible and cost-effective solution to support applications such as vehicle platooning, blind spot detection, parking assistance, and traffic management. To support these features, an increasing number of sensors are being deployed along the road in the form of roadside objects. However, despite the hype surrounding cellular V2X networks, the practical realization of such networks is still hampered by under-developed physical security solutions. To solve the issue of wireless link security, we propose a deep Q-learning-based strategy to secure V2X links. Since one of the main responsibilities of the base station (BS) is to manage interference in the network, the link security is ensured without compromising the interference level in the network. The formulated problem considers both the power and interference constraints while maximizing the secrecy rate of the vehicles. Subsequently, we develop the reward function of the deep Q-learning network for performing efficient power allocation. The simulation results obtained demonstrate the effectiveness of our proposed learning approach. The results provided here will provide a strong basis for future research efforts in the domain of vehicular communications.

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.

This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental properties of generalized sparsity still hold. As we work through the various ways of demonstrating the advantageous properties of sparsity, we illustrate its meaning from geometrical and operational perspectives. Thereafter, we construct specific measures of sparsity which are successfully qualified in complexity analysis and sparse optimization scenarios. Overall, our main objective is to construct measures of sparsity that will facilitate and enhance the design of the next innovative sensing technologies.

Multi-antenna receivers are becoming popular in ambient backscatter communication (AmBC) because they help to improve the detection performance by improving the signal-to-interference-plus-noise ratio (SINR) of the backscatter signal. Recent solutions, however, suffer from high implementation complexity and they tend to require the receiver to have high dynamic range. In this paper, a simplified receiver is proposed, which is able to overcome the above limitations. The receivers for the deterministic-unknown ambient signal and the Gaussian signal are derived using maximum-a-posteriori (MAP) criterion. Then, their detection performances are derived for bit-error-rate analysis. It is shown that the test statistic of the receiver is independent of the ambient signal type. The components for implementation are halved compared with the optimum AmBC receiver at the cost of an SINR penalty as low as 1-dB to achieve the same detection performance for the deterministic ambient signal and 3.6-dB for the Gaussian ambient signal. The dynamic range problem can be mitigated by implementing it fully in the analog domain. By providing attractive practical advantages mentioned above, the proposed simplified receiver, therefore, enables cost-effective AmBC receiver implementations.

The ambient backscatter communication (AmBC) system utilizes the existing ambient RF signals present in the atmosphere for backscattering the signal. One of the challenges for AmBC system is the interference at the receiver module caused by the direct path signal from the ambient source. The purpose of this paper is to study the coverage aspects of the bi-static backscatter communication system in a typical urban environment at sub-1GHz frequencies using simulations in MATLAB. For the simulation, 3rdgeneration partnership project (3GPP) urban microcellular and international telecommunication union (ITU) device-to-device (D2D) propagation models are used. Moreover, the dynamic range i.e., the difference in the received power level of the direct path and the backscatter path is investigated. For correctly decoding the backscatter signal at the reader, the target value set for the dynamic range is less than 30 dB. This paper studies the importance of direct path interference suppression for the successful deployment of a bi-static backscatter communication system.

A better and accurate knowledge about the channel propagation characteristics is required to fully exploit the commercial frequency band of 26 GHz for Fifth Generation (5G) of cellular systems. Little is known about suitability of current urban macrocellular sites for serving millimeter wave frequency e.g., 26 GHz for 5G base stations. Therefore, in order to efficiently utilize the existing macro sites infrastructure an extensive millimeter wave outdoor measurement campaign is needed. Hence, in this paper we carry out a wide band continuous wave measurement spanning over a bandwidth of 800 MHz. Measurements are done at eighty-one equally separated frequency points in 26 GHz band. The measurements are performed with 76 m and 44 m transmitter antenna height in urban environments, with sufficient number of measurement locations. Mainly, we target the non-line of sight and obstructed-line of sight conditions. We carefully compute the path loss based on the measurement data, and also present the path loss exponent based large scale path loss model for two different considered scenarios. This paper also we discuss the impact of transmitter antenna height on the signal propagation.

Industrial Internet of Things (IIoT) and Industry 4.0 are used interchangeably while talking about the revolution in the field of manufacturing and logistic industries. IIoT is a digital ecosystem of connected devices, machines, equipment, and robots that communicate with each other without or with minimum human intervention. The 28 GHz band and 60 GHz band are two lucrative Millimeter Wave (mmWave) bands for 5G, and are getting popular since they enable standalone private network for IIoT. To ensure the successful deployment of IIoT in practice, we need to revisit our understanding of the radio channel characteristics at target frequencies in Indoor Factory (InF) environments. In this paper, we have considered three operating frequencies i.e., 3.5 GHz, 28 GHz and 60 GHz, and we investigate the performance of several Distributed Antenna System (DAS) and non-DAS layouts in 3GPP specified InF environment that is characterized by high density of blockers. The performance of the system is quantified in terms of received power, SINR, outage ratio, application layer throughput, and system capacity. The system performance is analyzed with Three-Dimensional (3D) Ray Tracing (RT) simulations. The acquired simulation results and related discussion provided in this paper gives insightful information about the communication properties in an indoor industrial environment.

Propagation characteristics of different frequency bands have a fundamental impact on the network planning aspects of practical cellular systems. A reasonable understanding of the radio propagation characteristics is already established and well-known large-scale propagation models are available for sub-6 GHz frequencies of operation. However, it is not true for a commercially available band of 26 GHz for fifth-generation (5G) of mobile systems in Europe, and better and accurate path loss models are required. In this research work, we carried out comprehensive continuous wave (CW) channel measurements at three potential 5G frequencies i.e., 1.9 GHz, 3.8 GHz, and 26 GHz in the city of Porvoo, Finland. The existing macrocellular base station (BS) site location is utilized during the measurement campaign. The large bandwidth available at 26 GHz makes it a feasible solution for fixed wireless access (FWA). The coverage achievable by these three bands was evaluated by fitting the alpha-beta-gamma (ABG) and close-in (CI) free space model to the measured path loss. This paper presents a link budget with practical considerations for different frequencies. In the light of acquired results, uplink (UL) is found coverage limited at all considered frequencies, and the attained results highlight the possibility of using 26 GHz band for FWA. Although, the outdoor-to-indoor service provisioning is challenging, however, interestingly it is found that around 1 kilometer and half kilometer coverage can be achieved in downlink (DL) at 26 GHz frequency for the use case of FWA and mobile users, respectively, in an outdoor environment.

Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique.

The research efforts on cellular vehicle-to-everything (V2X) communications are gaining momentum with each passing year. It is considered as a paradigm-altering approach to connect a large number of vehicles with minimal cost of deployment and maintenance. This article aims to further push the state-of-the-art of cellular V2X communications by providing an optimization framework for wireless charging, power allocation, and resource block assignment. Specifically, we design a network model where roadside objects use wireless power from RF signals of electric vehicles for charging and information processing. Moreover, due to the resource-constraint nature of cellular V2X, the power allocation and resource block assignment are performed to efficiently use the resources. The proposed optimization framework shows an improvement in terms of the overall energy efficiency of the network when compared with the baseline technique. The performance gains of the proposed solution clearly demonstrate its feasibility and utility for cellular V2X communications.

The main target of this article is to propose a simple Received Signal Strength Indicator‐ (RSSI‐) based adaptive Physical Layer (PHY) selection approach for Bluetooth Low Energy 5.0 (BLE 5) and compare the performance of adaptive PHY selection with the performance of individual PHY. This article also validates the simulation results acquired from the in‐house developed Ray Tracing (RT) tool with the BLE 5 measurements conducted in a corridor of the TUAS building, Aalto University. The performance metrics considered for the analysis are RSSI, throughput, and outage ratio. The impact of different transmission power on BLE 5 is also analyzed. The obtained simulation results indicate a Root Mean Square Error (RMSE) of about 4.6 dB between the measured and the simulated RSSI at 2.4 GHz frequency. By investigating these results, an adaptive PHY selection algorithm is proposed in this article. The adaptive PHY selection approach enhances the mean throughput of the system and improves the outage ratio as well. In the light of acquired results, the adaptive PHY selection scheme shows significant performance gain and is found more beneficial in the case of an environment with mixed RSSI samples, i.e., a mix of low and high RSSI values.

Abstract In this paper, we present a general Gaussian N-relay network by allowing relays to communicate to each other and allowing a direct channel between source and destination as compared to the standard diamond network in Nazaroğlu et al. (IEEE Trans Inf Theory 60:6329–6341, 2014) at the cost of extra channel uses. Our main focus is to examine the min-cut bound capacities of the relay network. Very recently, the results in Uykan (IEEE Trans Neural Netw Learn Syst 31:3294–3304, 2020) imply that the GADIA in Babadi and Tarokh (IEEE Trans Inf Theory 56:6228–6252, 2010), a pioneering algorithm in the interference avoidance literature, actually performs max-cut of a given power-domain (nonnegative) link gain matrix in the 2-channel case. Using the results of the diamond network in Nazaroğlu et al. (2014) and the results in Uykan (2020), in this paper, we (i) turn the mutual information maximization problem in the Gaussian N-relay network into an upper bound minimization problem, (ii) propose a modified GADIA-based algorithm to find the min-cut capacity bound and (iii) present an upper and a lower bound to its min-cut capacity bound using the modified GADIA as applied to the defined “squared channel gain matrix/graph”. Some advantages of the proposed modified GADIA-based simple algorithm are as follows: (1) The Gaussian N-relay network can determine the relay clusters in a distributed fashion and (2) the presented upper bound gives an insight into whether allowing the relays to communicate to each other pays off the extra channel uses or not as far as the min-cut capacity bound is concerned. The simulation results confirm the findings. Furthermore, the min-cut upper bound found by the proposed modified-GADIA is verified by the cut-set bounds found by the spectral clustering based solutions as well.

In today's cellular networks, an increasing number of connected devices on-board in fast-moving vehicles would require more efficient handover (HO) procedures. To this end, we investigate the utilization of mobile relay nodes (MRNs) in vehicles to facilitate efficient HO and HO-related power consumption reductions for all on-board user equipments (UEs). In particular, the potential gains in terms of HO rate, HO failure ratio (HOFR), ping-pong (PP) rate, and total power consumption are studied for different UE cluster sizes. To eliminate the measurement power-consuming procedure, uplink (UL) reference signals (RS) transmitted by UEs are exploited. Four different case scenarios are simulated utilizing both the DL and UL RS based HO procedure, with and without deploying MRNs on the buses traveling along the cell edges of surrounding macro BSs. Simulation results indicate that the UL RS based HO procedure can improve HO performance significantly because it reduces the air-interface signaling messages, namely the measurement report (MeasReport) transmission and reception. Also, in terms of power consumption, deploying MRNs is a more attractive solution with substantial power reduction for onboard UEs of higher cluster size.

Internet of Things (IoT) has revolutionized the concept of connectivity from only humans to almost everything capable of being connected. Wireless communication technologies are considered as the vital building blocks of IoT. Bluetooth Low Energy (BLE) with the highest penetration rate in consumer products has an important role in enabling IoT applications. BLE version 5 has recently been developed to meet the versatile requirements of IoT use cases. In this paper, we investigate the practical performance of BLE 5 in indoor office environment, and study the performance of its novel features. Our evaluation metrics in this study are throughput and Received Signal Strength Indicator (RSSI). In this study, BLE 5 measurements are conducted by using nRF52840 chip from Nordic Semiconductor. The acquired measurement results indicate that new Physical Layers (PHYs) of BLE 5 provide extended coverage and improves the throughput in indoor scenarios. The 2 Mbps uncoded PHY significantly improves the throughput in good RSSI zones. However, the throughput drastically drops in case of zones with moderate to weak RSSI. In particular, the feature of the coded physical layer helps extending the Bluetooth coverage, especially in Non-Line of Sight (NLOS) scenarios.

Cellular connectivity for drones comes with a wide set of challenges as well as opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve HRRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions for different altitudes/speeds of drones have been presented, which promote a revision of the legacy handover schemes and boundaries of cells in the sky.

Low latency is a critical requirement of beyond 5G services. Previously, the aspect of latency has been extensively analyzed in conventional and modern wireless networks. With the rapidly growing research interest in wireless-powered ambient backscatter communications, it has become ever more important to meet the delay constraints, while maximizing the achievable data rate. Therefore, to address the issue of latency in backscatter networks, this paper provides a deep Q-learning based framework for delay constrained ambient backscatter networks. To do so, a Q-learning model for ambient backscatter scenario has been developed. In addition, an algorithm has been proposed that employ deep neural networks to solve the complex Q-network. The simulation results show that the proposed approach not only improves the network performance but also meets the delay constraints for a dense backscatter network.

Collaborative and autonomous communications among devices are expected to transform the digital world in the coming years. The device-to-device (D2D) sidelink communication has a great impetus to enable industrial Internet-of-things (IoT). However, the rapid growth of such networks and dynamic quality of service demands of user equipments require robust and efficient resource management. This paper addresses the issue of mode selection in D2D sidelink communications for industrial IoT applications. Specifically, we consider a finite channel blocklength transmission in order to cater for delay-sensitive industrial IoT applications. To improve the performance of the network, we employ a graph-theoretic approach for efficient mode selection. The simulation results of the proposed approach are compared with conventional heuristic approaches. The results demonstrate the feasibility of the proposed approach against the conventional approaches especially in terms of complexity and throughput.

Channel models are routinely used for evaluating the performance of wireless technologies and cellular networks. An appropriate channel model is necessary for a credible system analysis, and the shortcomings in the channel model may lead to erroneous conclusions. In this paper we characterize the impact of Interference Suppression (IS), Interference Cancellation (IC) and interference management on the system performance when using a) the 3rd Generation Partnership Project (3GPP) street canyon model (TR 38.901, Release 14), b) Shoot and Bouncing Ray (SBR) method based Ray Tracing (RT) model, and c) a proposed large scale path loss model in an urban, so-called Manhattan building grid environment. Simulations are performed using the 28 GHz carrier frequency that has been recently considered for the 5thGeneration (5G) networks. Simulation results indicate that the 3GPP channel model provides slightly pessimistic path loss values than RT in Line of Sight (LOS) conditions, whereas in Non-LOS (NLOS) situation it gives a considerable pessimistic path loss estimation as compared with the deterministic RT approach. The difference between channel models is notable especially for the estimation of Signal to Interference plus Noise Ratio (SINR). Since RT provides realistic results due to accurate radio environment and signal modeling and, on the other hand, the correct SINR estimation is crucial for the wireless system evaluation. Our proposed path loss model is based on RT simulations. The performance of the proposed model for different performance metrics matches well with the RT results.

Mobile relay node (MRN) is one of the cheaper options for reliable communication when users are moving by public transport (i.e. bus, tram, train, subway, etc.), especially in urban areas. Critically, MRNs need to maintain a backhaul connection with the fixed infrastructure via a donor eNB, (DeNB). If the MRN fails to successfully handover (HO) from one DeNB to another, it will create a single point of failure, i.e. the connection of all UEs connected to MRN will be dropped. In this paper, we address the HO performance of a MRN including a power consumption analysis thereof. We investigate the potential gains in terms of HO rate, HO failure ratio (HOFR), ping-pong (PP) rate and power consumption (both at UE and eNB) when a MRN is deployed on a bus traveling along the cell edges of surrounding macro BSs. We also look over the MRN HO failure cases to identify the causes of HO failures that are more critical for the UEs onboard. Numerical results indicate that deploying a MRN on the roof-top of a bus improves the HO rates 15%, HOFR 8%, PP rate 17%, UE power consumption 21%, and eNB power consumption 14% on average for all simulated cases. We have also established that UL transmission errors are the most dominant causes of turning MRN to a single point of failure during a HO.

Backscatter communication offers a reliable and energy-efficient alternative to conventional radio systems. With the additional capability of wireless power transmission, the backscatter tags can work in a completely battery-less manner. Due to these exciting features, the researchers from academia and industry are extensively investigating its utility as an enabler of massive Internet of Things (IoT) networks. However, before reaping the benefits of ambient backscatter communications, it is necessary to evaluate its feasibility and operability for large scale networks. To do so, this research paper provides an empirical study of the real city wide deployment of monostatic wireless powered backscatter tags in Helsinki region. The coverage and outage performance has been assessed for both indoor and outdoor conditions using a sophisticated 3D ray tracing simulations. Whereby, the indoor scenario consists of several story buildings with low and high loss materials. Moreover, an indepth evaluation of energy shortage at backscatter tags has also been provided which sheds the light on the importance of wireless power transmission for such networks. The results provided here would be helpful in upscaling the practical deployment of backscatter tags.

Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-to-noise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The model is numerically and empirically evaluated, and its properties are discussed in depth. The most important model implications are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model-based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.

In this paper, we examine the use of autoencoders as an optimization tool for the construction of noncoherent space-time MIMO codes. In particular, we consider the quasi-static block fading channel, where the channel state information is not available at either the transmitter or the receiver, and changes independently between transmissions. Different from traditional constructions which aim to maximize an approximation of the minimum pairwise distance of the constellation, we use the autoencoder to directly target minimizing the probability of error. We show that this different optimization goal leads to constellations with more favorable pairwise distances' distribution and better error performance at low to medium signal to noise ratios where the minimum distance is not the limiting factor. Finally, we present simulation results showing that the constructed codes outperform traditional Grassmannian codes up to a signal-to-noise ratio of 20 dB using the traditional generalized likelihood ratio test detector.

Ambient backscatter communication (AmBC), a green communication technology, is hampered by the continuously and extremely fast varying, strong and unknown ambient radio frequency (RF) signals. This paper presents a machine learning-assisted method for extracting the information of the AmBC device. The information is modulated on top of the unknown Gaussian-distributed ambient RF signals. The proposed approach can decode the binary phase shift keying backscatter signals encoded using Hadamard codes. This method extracts the learnable features for the tag signal by first eliminating the direct path signal and then correlating the residual signal with the coarse estimate of ambient signal. Thereafter, the tag signals are recovered by using the k-nearest neighbors classification algorithm. The recovered signals are decoded by a Hadamard decoder to retrieve the original information bits. We validate the performance using simulations to corroborate the proposed approach.

In this paper, we investigate binary frequency shift keying (BFSK) over ambient OFDM signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient spectrum either up or down in frequency allowing the implementation of BFSK. We exploit the guard band and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques which suffer from asymmetric error probabilities, the proposed scheme has symmetric error probabilities. Furthermore, we analyze the error performance of the optimal noncoherent detector and obtain an exact expression for the average probability of error. Finally, simulation results corroborate our analysis and show that the proposed scheme outperforms energy detection based schemes available in the literature by up to 3 dB.

Ultra-reliable low-latency communication (URLLC) differentiates the fifth generation mobile network (5G) by allowing it to natively address the needs of mission-critical machine type communication, thereby enabling a wide range of use cases. This work selects industrial automation as a target use case for very strict URLLC requirements. By means of simulation, we study how reliability and availability of the 5G connectivity can be improved using multiple transmission/reception points (TRxPs) in an interference limited scenario. A factory scenario with mobile robots connected via a 5G network with two indoor cells is selected, as mobility is one of the main drivers of the need for wireless connectivity, and is also a major challenge to reliability. The results indicate that differences in design alternatives have a significant impact. It is evident that the best design choices for the TRxPs in a given factory significantly enhance communication service reliability and availability, even when no retransmissions are allowed due to very tight latency requirements. The solution can additionally increase capacity in terms of the number of mobile robots supported in the factory, for a desired level of reliability. We conclude that first, the number of TRxPs that simultaneously transmit to a user (referred to as a cluster) cannot be increased indefinitely, as this could increase interference and worsen signal-to-interference-and-noise ratio (SINR). We observe that increasing the number of TRxPs deployed in a cell, while keeping the cluster size small, has a positive effect. Finally, we demonstrate that the positioning of the TRxPs around the factory plays an import role. The best observed design choice is to place many TRxPs around each cell, with only one or two TRxPs transmitting simultaneously to a user. Placing the TRxPs at a large distance from each other increases spatial diversity and the likelihood of improving the received signal, and distancing the TRxPs of one cell from the other cell's TRxPs limits interference.

The application of backscatter communications is expanding from Radio Frequency Identification (RFID) to ultra-low power connectivity solution for Internet of Things (IoT). Unlike typical short range monostatic (transmitter and receiver are collocated at the reader) RFID applications, the IoT aims for long-range and bistatic (transmitter and receiver are dislocated) applications. This paper validates the bistatic dislocated Backscatter Communication link budget calculation with the measured results at 915 MHz and 2.4 GHz frequencies using RFID and Long Range (LoRAN) technologies in an outdoor and an indoor environment, respectively. It is recommended in this paper that an appropriate Slow Fading Margin (SFM) needs to be included in the backscatter link budget for large TX and RX separation. However, for smaller distances between the RX and backscatter device, only a fast fading margin should be sufficient with reasonable root mean square error. The post analysis of the simulation data revealed a closed match between the simulated and the measured results at both considered frequencies and technologies. It is also acquired that the proposed link budget calculation equation is independent of technology, and is valid for both RFID and LoRAN technology, given that the correct values of the relevant parameters are appropriately included in the link budget calculation.

The main target of this paper is to propose a preferred set of features from a cellular network for using as predictors to do the classification between the rogue flying drone User Equipments (UEs) and regular UEs for different Machine Learning (ML) models. Furthermore, the target is to study four different machine learning models i.e. Decision Tree (DT), Logistic Regression (LR). Discriminant Analysis (DA) and K-Nearest Neighbour (KNN) in this paper, and evaluate/compare their performance in terms of identifying the flying drone UE using three performance metrics i.e. True Positive Rate (TPR), False Positive Rate (FPR) and area under Receiver Operating Characteristic (ROC) curve. The simulations are performed using an agreed 3GPP scenario, and a MATLAB machine learning tool box. All considered ML models provide high drone detection probability for drones flying at 60 m and above height. However, the true drone detection probability degrades for drones at lower altitude. Whereas, the fine DT method and the coarse KNN model performs relatively better compared with LR and DA at low altitude, and therefore can be considered as a preferable choice for a drone classification problem.

The future 5th Generation (5G) wireless networks are expected to support a variety of Bandwidth (BW) hungry applications. Millimeter wave (mmWave) communications, and Ultra Dense Network (UDN) deployment along with smart Distributed Antenna System (DAS) can be considered as a tempting solution for cellular networks. The aim of this paper is to analyze the performance of different UDN and DAS configurations in an indoor environment i.e. the real university office building at 3.5 GHz, 28 GHz, and 60 GHz frequency. System performance is analyzed by performing ray launching simulations using a three dimensional floor plan. The obtained results show the incapability of basic indoor solution in providing the ubiquitous Quality of Service (QoS) in an indoor environment at higher frequencies. Simulation results show that it is inevitable to have a dedicated indoor network with UDN or DAS configuration to provide homogeneous coverage in an indoor condition. It is found that in spite of more interference the UDN deployment provides a higher system capacity compared with DAS due to more number of cells. However, the gain of cell densification saturates with the increasing number of cells.

Ambient backscatter communications system (ABCS) has recently been introduced as a cutting edge technology in which devices communicate in wireless mode by exploiting ambient radio frequency (RF) signals instead of actively generating them. ABCS is a promising technology for Internet of Things (IoT) use cases in which the power efficiency is a major challenge yet to be addressed. ABCS is in its early development stages from theoretical and practical perspectives. In this regard, it is highly important to understand the multi-aspects of the link budget of ABCS. Hence, in this paper we conducted a comprehensive study including measurements in different propagation environment and thorough simulation. The measurements are preformed in sub-1 GHz band and particularly in 590 MHz with the tags designed in the house. The results confirm the match between measurements and the simulations with trivial error.

The main target of this paper is to evaluate the performance of a switched beam antenna in a macro-cellular environment at 28 GHz frequency. Another objective of this study is to compare the performance of a cellular system equipped with 7-beams switched beam antenna with conventional system implemented with wide 65° horizontal HPBW antenna. A campaign of 3D ray tracing simulations was carried out by using realistic 3D building data. Performance metrics considered for this study are RX level, SINR, system spectral efficiency, and the relative gain. Different configurations of 7-beams antenna is studied and the advantages and the disadvantages of considered configurations are highlighted in this paper. It is learned from the acquired results that an optimal configuration of 7-beams antenna can provide upto only 209.72 % of relative gain with respect to a traditional 3-sector site deployment. However, the relative performance gain of a 7-beam antenna can be negative under certain circumstances.

In this paper, we address the signaling overhead and the power consumption that results from the transmission of handover-related signaling in a Long Term Evolution (LTE) cellular network. Specifically, we analyze the contribution of the different signaling messages over the air-interface, and their impact on power consumption both at the eNB and at the User Equipment (UE) during handover (HO). A quantitative analysis is provided using system level simulations. We observe that, within the HO process, the largest contributor to air-interface signaling overhead is the transmission of the measurement report by the UE. This uplink (UL) transmission suffers from different channel impairments, due to interference and transmission range, for particular cell sizes. As a consequence, uplink signaling retransmissions are triggered causing higher signaling load and consequently higher power consumption, this being especially detrimental to battery-powered devices.

Recently, an active discussion on the feasibility of Millimeter Wave (mmWave) frequencies for the Vehicle-to-Vehicle (V2V) communication have been carried out in research community. We contribute to this discussion by providing a comparison between explicit three-dimensional ray-tracing simulations and field trial measurements on 39 GHz frequency. Three basic practical and relevant cases for V2V communications are considered covering several important scenarios of daily life traffic. A close match between the measured and simulated results is found through explicit ray tracing simulations; thus validating the feasibility of the simulation model and underlying assumptions. Moreover, these outcomes also shed light on the potential and challenges of using mmWave frequencies for V2V communication. The acquired results indicate that the Reference Signal Received Power (RSRP) levels are sufficiently above the noise level even up to 100 m distance between TX and RX in case of a single obstructing car. Results also reveal the impact of moving vehicle intersecting the LOS between the TX and RX vehicle at road intersection, and they indicate a notable blockage loss in case of short TX-RX separation.

In recent years, ambient backscatter communications have gained a lot of interests as a promising enabling technology for the Internet-of-Things and green communications. In ambient backscatter communication systems, ultra-low power devices are able to transmit information by backscattering ambient radio-frequency signals generated by legacy communication systems such as Wi-Fi and cellular networks. This paper is concerned with ambient backscatter communications over legacy orthogonal frequency division multiplexing (OFDM) signals. We propose a backscatter modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. The proposed modulation scheme allows both binary and higher-order modulation using noncoherent energy detection. We investigate the detector design and analyze the error performance of the proposed scheme. We provide an exact expression for the error probability for the binary case, whereas accurate approximate expressions for the error probability are derived for the M-ary case. We corroborate our analysis using Monte-Carlo simulation and investigate the effects of varying the OFDM symbol size, maximum channel delay spread, and the number of receive antennas on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in this paper for backscatter communication over ambient OFDM signals in different scenarios.

In recent years, ambient backscatter communications have gained a lot of interest as a promising enabling technology for Internet-of-Things and green communications. In ambient backscatter communication systems, battery-less devices are able to transmit information by backscattering ambient RF signals generated by legacy communication systems such as digital TV broadcasting, Wi-Fi, or cellular. This paper is concerned with ambient backscatter communications over legacy cellular OFDM signals. We propose a novel modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. We analyze the error performance of the proposed scheme, provide an exact expression for the error probability, and validate our analysis using Monte-Carlo simulation. We investigate the effects of varying the OFDM symbol size and maximum channel delay spread on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in the literature for backscatter communication over ambient OFDM signals in different scenarios.

Received signal strength (RSS)-based device-free localization applications utilize the communication between wireless devices for locating people within the monitored area. The technology is based on the fact that humans cause changes in properties of the wireless channel which is observed in the RSS, enabling localization of people without requiring them to carry any sensor, tag or device. Typically this inverse problem is solved using an empirical model that relates the RSS to location of the sensors and person, and utilizing either an imaging method or a particle filter (PF) for positioning. In this paper, we present an extended Kalman filtering (EKF) solution that incorporates some of the beneficial properties of the PF but has a lower computational overhead. In order to make the EKF work, we also need to reconsider how the measurements are sampled and processed, and a new processing scheme is proposed. The developments are validated using simulations and experimental data, and the results imply: i) the non-linear filters outperform a popular imaging method; ii) the robustness of the EKF and PF is improved using the proposed processing scheme; and iii) the EKF achieves similar performance as the PF as long as the new processing scheme is used.

Millimeter wave (mmW) communication has been viewed as a way of offloading users from lower frequencies, where currently deployed cellular networks operate. The short wavelength of mmW frequencies allows for electrically large arrays to be deployed in small mobile devices. Electrically large arrays (in terms of wavelength) are needed in order to compensate for the high signal attenuation in mmW frequencies by means of transmit-receive beamforming. Two beamforming architectures are typically considered in practice, namely a fully digital solution in which every antenna has a dedicated radio frequency (RF) chain, and a so-called hybrid solution that consists of an analog beamforming stage prior to digital beamforming. Hybrid beamforming trades-off the versatility of a fully digital architecture for lower power-consumption and cost. In this paper we focus on fully analog beamforming architectures for circular arrays comprised of patch antennas. In particular, we propose two architectures for analog beamforming with circular arrays, and provide a detailed study regarding the number of simultaneously active beams (i.e, number of RF chains), the amount of antennas per beam, as well as the number of required phase-shifters. The proposed analog beamforming scheme is compared to a fully digital beamforming approach on a realistic 5G system level platform simulating an urban environment. Results show that the performance of the proposed analog beamforming architecture is comparable to that of a fully digital beamforming scheme in terms of user throughput.

Received signal strength based radio tomographic imaging is a popular device-free indoor localization method which reconstructs the spatial loss field of the environment using measurements from a dense wireless network. Existing methods achieve high accuracy localization using a complex system with many sophisticated components. In this work, we propose an alternative and simpler imaging system based on link level occupancy detection. First, we introduce a single-bounce reflection based received signal strength model, which allows relating received signal strength variations to a large region around the link-lines. Then, based on the model, we present methods for all system components including a classifier, a detector, a back-projection based reconstruction algorithm, and a localization method. The introduced system has the following advantages over the other imaging based methods: i) a simple image reconstruction method that is straightforward to implement; ii) significantly lower computational complexity such that no floating point multiplication is required; iii) each link's measured data are compressed to a single bit, providing improved scalability; and iv) physically significant and repeatable parameters. The proposed method is validated using measurement data. Results show that the proposed method achieves the above advantages without loss of accuracy compared to the other available methods.

As the larger share of total energy consumed by mobile network operators (MNOs) is wasted in order to ensure coverage, three to five MNOs covering the same geographical area results in enormous energy waste. In order to cater for the data tsunami with almost zero marginal revenue, required densification of cells are not sustainable from both Capex and Opex perspective. Even with densification of networks, it is hard to satisfy the performance requirement of the cell edge users due to interference. However, the performance can be upgraded as well as energy can be saved by the offloading of the cell edge users to other MNOs if regulator and MNOs resort to appropriate mechanism. In our previous work, we proposed a double auction based energy saving market mechanism where MNOs participate in bidding to share coverage and capacity in order to save energy, especially during low to medium load. Unlike previous work, in this paper we use this mechanism that involve cell level bidding, i.e., cells bid for each user which not only allows total offloading of the cells at the low load but also offloading of cell edge users among the MNOs during high load. As a result, the energy saving potential becomes very high also at high load conditions along with improvement in performance of cell edge users.

Recently opened spectrum within 3550-3700 MHz provides more accessing opportunities to secondary users (SUs), while it also raises concerns on the operational privacy of primary users (PUs), especially for military and government. In this paper, we propose to study the tradeoff between PUs' temporal privacy and SUs' network performance using the data-driven approach. To preserve PUs' temporal operational privacy, we develop an obfuscation strategy for PUs, which allows PUs to intentionally add dummy signals to change the distribution of temporal spectrum availability, and confuse the adversary. While generating the dummy signals for privacy, the PUs have to consider the utility of SUs and try their best to satisfy SUs' uncertain traffic demands. Based on the historical data, we employ a data-driven risk-averse model to characterize the uncertainty of SUs' demands. With joint consideration of PUs' privacy and uncertain SUs' demands, we formulate the data-driven risk- averse stochastic optimization, and provide corresponding solutions. Through numerical simulations, we show that the proposed scheme is effective in preserving PUs' temporal operational privacy while offering good enough spectrum resources to satisfy SUs' traffic demands.

This paper proposes an approach for receiving small uplink data transmissions from a massive amount of machine type communication (MTC) devices in ultra-dense networks (UDNs). In particular, we propose using a beamforming based solution for distributing (broadcasting) uplink grants for small data transmissions. Receive beamforming is also employed in order to receive the subsequent uplink small data transmissions. The proposed approach is particularly useful in reaching high transmission success probability of MTC uplink traffic with low consumption of physical resources. Extensive numerical results are provided based on detailed system level simulations in an ultra-dense 5G context. Results show that low collision and decoding failure probabilities can be achieved by employing beamforming and orthogonal codes for connectionless uplink grant distribution. Additionally, it is shown that uplink transmissions can be received efficiently by utilizing receive filters designed to maximize the received power towards the directions where grants were broadcasted. The performance of the proposed scheme is analyzed with a typical uniformly distributed massive MTC (mMTC) traffic as well as with a peak access profile as an extreme case.

In this paper, big Radio Frequency (RF) data assisted optimization is considered for future wireless networks employing cognitive radio technology with machine learning capability. A cognitive radio network (CRN) with multiple Secondary Users (SUs) may coexist with other wireless systems such as Small Cells (SC) and Radar systems, both Primary Users (PUs) with different level of priorities. Traditional spectrum sensing typically only gives information about the presence or absence of a PU. However, when multiple heterogeneous systems coexist, it becomes imperative to acquire the knowledge of the systems operating in a specific band at a particular time so as to choose an appropriate transmission strategy. In this work, we take advantage of the learning capability of a Neural Network Predictor (NNP) to obtain the statistics of the coexisted wireless systems from the RF traces collected in our Universal Software Radio Peripheral (USRP) based test bed. The NNP is able to learn the features of the RF traces and make accurate prediction of the signals prevalent in the wireless environment. Because of the augmented information learned from the RF traces, a novel optimization problem incorporating the outputs from the NNP is formulated to maximize the throughput of the CRN. The solution is derived using Karush- Kuhn-Tucker (KKT) and extensive simulations using the real RF traces are carried out. It is demonstrated that the NNP can detect the type and number of coexisted users reliably and the proposed scheme will improve the performance of the coexisted CRN.

Narrow-Band Internet of Things (NB-IoT) is 3GPP's cellular technology designed for narrow-band and Low-Power Wide Area Network (LPWN). NB-IoT provides deep indoor coverage for thousands of low-data-rate and low-powered devices. Coverage enhancement for devices in weak coverage condition is enabled by having signal repetitions over extended period of time in order to boost the signal quality. Performance gain from repetitions of signals is limited by channel estimation quality. In this paper we analyze impact of channel coherence time on the uplink coverage. In addition to analytical performance bound and simulation results, we also present coverage performance results from practical measurements using a NB-IoT prototype. It is reported how the NB-IoT coverage improvement is limited by the channel estimation quality and coherence time of the channel.

The business potential of device-to-device (D2D) communication including public safety and vehicular communications will be realized only if direct communication between devices subscribed to different mobile operators (OPs) is supported. One possible way to implement inter-operator D2D communication may use the licensed spectrum of the OPs, i.e., OPs agree to share spectrum in a co-primary manner, and inter-operator D2D communication is allocated over spectral resources contributed from both parties. In this paper, we consider a spectrum sharing scenario, where a number of OPs construct a spectrum pool dedicated to support inter-operator D2D communication. OPs negotiate in the form of a non-cooperative game about how much spectrum each OP contributes to the spectrum pool. OPs submit proposals to each other in parallel until a consensus is reached. When every OP has a concave utility function on the box-constrained region, we identify the conditions guaranteeing the existence of a unique equilibrium point. We show that the iterative algorithm based on the OP's best response might not converge to the equilibrium point due to myopically overreacting to the response of the other OPs, while the Jacobi-play strategy update algorithm can converge with an appropriate selection of update parameter. Using the Jacobi-play update algorithm, we illustrate that asymmetric OPs contribute an unequal amount of resources to the spectrum pool; however, all participating OPs may experience significant performance gains compared with the scheme without spectrum sharing.

A growing set of environmental sensing applications use received signal strength measurements of a static wireless network for unobtrusive monitoring purposes. The success of these systems, which typically process low-amplitude signals, depend strongly on the distribution of the measurements when there are no changes in the channel. In this letter, a statistical model for signal strength measurements acquired when the environment is static is studied. As previously empirically verified, it is shown that the measurements have log-normal distribution even in idealistic environments, which cannot be explained using log-normal shadow fading arguments. Quantization and round-off errors induced by different measurement system components are also considered, and their impact are analyzed. As a result, it is shown that the logarithmic received signal strength measurements under static channel conditions are samples from stationary Gaussian process independent of the environment.

Machine-type communication (MTC) devices usually have low-data rate, and in some applications, latency is critical. In long-term evolution, establishing a connection requires a relatively complex handshaking procedure. Such an approach is suitable for a system serving only a few high-activity users, but it becomes cumbersome for MTC traffic, where large amounts of low-activity users intermittently transmit a small number of packets. We propose a random access channel (RACH)-based scheme for the future 5G system that allows MTC users to send small packets within the random access burst. Device activity detection, channel estimation, authentication, and data decoding are performed simultaneously from the same access burst. In addition to the inherent reduction in energy consumption, the scheme eliminates the signaling overhead and creates more resources for data transmission. We have constructed an analytical framework for the detection performance of a multiple-input-multiple-output receiver in a frequency-selective channel. This has been validated with simulations, and results show that a one-shot correlate-and-cancel algorithm is sufficient for activity detection and channel estimation when the base station either employs multiple receive antennas or schedules multiple resource blocks for MTC RACH.

In this paper, we maximize the energy efficiency (EE) of full-duplex (FD) two-way relay (TWR) systems under non-ideal power amplifiers (PAs) and non-negligible transmission-dependent circuit power. We start with the case where only the relay operates full duplex and two timeslots are required for TWR. Then, we extend to the advanced case, where the relay and the two nodes all operate full duplex, and accomplish TWR in a single timeslot. In both cases, we establish the intrinsic connections between the optimal transmit powers and durations, based on which the original non-convex EE maximization can be convexified and optimally solved. Simulations show the superiority of FD-TWR in terms of EE, especially when traffic demand is high. The simulations also reveal that the maximum EE of FD-TWR is more sensitive to the PA efficiency, than it is to self-cancellation. The full FD design of FD-TWR is susceptible to traffic imbalance, while the design with only the relay operating in the FD mode exhibits strong tolerance.

The fifth generation (5G) of cellular networks is expected to provide connectivity for a wide range of services. This requires the network to encounter novel features. Ultra-reliable communications (URC) is one of the considered features, which provides a certain level of communication service almost all the time. This is essential in order to support mission-critical applications, such as industrial automation, public safety, and vehicular communications. This paper studies link adaptation optimization for URC, considering errors in both data and feedback channels. As the implementation of optimal link adaptation is challenging, particularly, for downlink transmissions due to the limited feedback channel, a simple link adaption scheme is also proposed. Results reveal that the performances of the proposed and optimal link adaptation schemes are close. Hence, the proposed scheme can be utilized to efficiently support URC in cellular networks.

Cellular systems are gaining more attention as a means of connectivity solutions for machine-type communications (MTC). The future cellular systems need to encounter new features in order to meet the diverse requirements of MTC applications. Ultra-reliable with low-latency communications (URLLC) is one of the considered features, required for mission-critical applications. This paper attempts to provide a better understanding of URLLC in the cellular systems. It considers different transmission modes and presents the required signaling and data transmissions. The optimal resource allocations are formulated for data transmissions. In addition, simplified solutions are given in order to reduce the computation complexity of resource allocations.

Fifth generation (5G) wireless systems will provide connectivity for a wide range of new applications with diverse requirements. In part, the network needs to support ultra-reliable communications with low-latency (URLLC) for mission-critical applications. For these applications, the generated data should be delivered with a limited number of transmission attempts with high success probability. This paper considers the optimal transmission and resource allocations for URLLC in cellular systems. The resource allocations are derived for the fixed and adaptive transmission attempt assignments. The analysis results reveal that both fixed and adaptive transmission assignments, applicable to automatic repeat request (ARQ) and hybrid ARQ (HARQ) schemes, can reduce the required resources compared to the equal transmission assignment.

In this paper we consider transmit (Tx) and receive (Rx) beamforming schemes based on the location of the device. In particular, we propose a design methodology for the Tx/Rx beamforming weight-vectors that is based on the departure and arrival angles of the line-of-sight (LoS) path between access-nodes (ANds) and user-nodes (UNds). A network-centric extended Kalman filter (EKF) is also proposed for estimating and tracking the directional parameters needed for designing the Tx and Rx beamforming weights. The proposed approach is particularly useful in 5G ultra-dense networks (UDNs) since the high-probability of LoS condition makes it possible to design geometric beams at both Tx and Rx in order to increase the signal-to-interference-plus-noise ratio (SINR). Moreover, relying on the location of the UNd relative to the ANds makes it possible to replace fullband uplink (UL) reference signals, commonly employed for acquiring the channel-state-information-at-transmitter (CSIT) in time-division-duplex (TDD) systems, by narrowband UL pilots. Also, employing the EKF for tracking the double-directional parameters of the LoS-path allows one to reduce the rate at which UL reference signals are transmitted. Consequently, savings in terms of time-frequency resources are achieved compared to beamforming schemes based on full-band CSI. Extensive numerical results are included using a realistic ray-tracing based system-level simulator in ultra-dense 5G network context. Results show that position based beamforming schemes outperform those based on full-band CSI in terms of mean user-throughput even for highly mobile users.

Recent studies foresee that there would be roughly 50 billion of machine type communication (MTC) devices by 2020. Coping with the massive signaling overhead expected from these devices in 5G network is an important hurdle to tackle. In this paper, we have proposed two optimal solutions that use Device-to-Device (D2D) communications to lightweight the overhead of MTC devices on 5G network. Each scheme has a specific objective, and aims to manage the communications between devices and eNodeBs to achieve its objective. The proposed solutions nominate the devices that should communicate through D2D communication fashion and those that should directly communicate with eNodeBs. The first solution aims to reduce the energy consumption, whereas the second one aims to reduce the data transfer delay at the eNodeBs. The performance of the proposed schemes is evaluated via simulations and the obtained results demonstrate their feasibility and ability in achieving their design goals.

Device-to-device (D2D) communications underlaying cellular networks is a promising concept that has several advantages over the traditional cellular networks. In the TDD system, the frame structure defines the order of uplink and downlink transmission slots. Typically, a TDD system is synchronized, and the same transmission order (TO) is used in all cells. In a direct D2D link, we have the freedom of selecting the TO of the devices freely. To our best knowledge, no paper has explicitly examined the TO optimization problem in D2D communications underlaying cellular network so far. In this paper, we focus exactly on this problem: once the proper co-channel D2D pairs are determined in the network, how do we minimize the network interference by optimally determining the TOs in all D2D links (together with co-channel cellular links) in the network, which is an NP-complete problem. In this paper, we formulate the TO optimization problem from a graph theoretic point of view: 1) we show that the TO optimization problem is equal to a constraint balanced min-cut graph partitioning problem of our defined augmented graph; and 2) we propose and analyze a distributed and a centralized efficient asynchronous clustering algorithm for solving the TO optimization problem equivalently for the min-cut of our proposed augmented graph. Computer simulations for the TDD-based D2D underlaying cellular network show that the proposed distributed and centralized algorithms, called ABCAMiC and CABCAMiC, respectively, 1) remarkably outperform the reference case where all TOs are fixed, 2) converge within a relatively small number of steps and generally converge in only a few epochs even for a large number of cellular and D2D users, and 3) the expected performance of the (partly/fully) distributed ABCAMiC is almost equal to that of the centralized solution CABCAMiC, which generally gives near-global optimal solution to the TO optimization problem.

Heavy-tailed distributions are present in the characterization of different modern systems such as high-resolution imaging, cloud computing, and cognitive radio networks. Commonly, the cumulants of these distributions cannot be defined from a certain order, and this restricts the applicability of traditional methods. To fill this gap, the present letter extends the traditional Edgeworth and Cornish-Fisher expansions, which are based on the cumulants, to analogous asymptotic expansions based on the log-cumulants. The proposed expansions inherit the capability of log-cumulants to characterize heavy-tailed distributions and parallel traditional expansions. Thus, they are readily implemented. Interestingly, the proposed expansions are applicable for light-tailed distributions as well.

User generated content (UGC)-based applications are gaining lots of popularity among the community of mobile internet users. They are populating video platforms and are shared through different online social services, giving rise to the so-called mobile social media applications. These applications are characterized by communication sessions that frequently and dynamically update content, shared with a potential number of mobile users, sharing the same location or being dispersed over a wide geographical area. Since most of UGC content of mobile social media applications are exchanged through mobile devices, it is expected that along with online social applications, these content will cause severe congestion to mobile networks, impacting both their core and radio access networks. In this paper, we address the challenges introduced by these applications devising a complete framework that 1) identifies such applications/sessions and 2) initiates multicast-based delivery (or offload through WiFi) of the relevant content. The proposed framework leverages the network function virtualization (NFV) paradigm to dynamically integrate its functionalities to the operators' service function chaining (SFC) process, allowing fast deployment and lowering both capital and operational expenditures (CAPEX and OPEX) of the mobile operators. The performance of the proposed framework is evaluated through mathematical analysis and computer simulations, taking Twitter-like social applications as an example.

Energy efficiency is of paramount importance for studying spectrum-sharing green communications. We provide some understanding on the energy efficiency of spectrum-sharing systems which are subject to an average interference power constraint. The results present some interesting insight for designing spectrum-sharing systems. The secondary user transmission strategies are different from the well studied scenarios to maximize the ergodic capacity. The energy efficiency of the secondary user could be considered as a factor for control interference to the primary systems.

In order to harvest the business potential of deviceto-device (D2D) communication, direct communication between devices subscribed to different mobile operators should be supported. This would also support meeting requirements resulting from D2D relevant scenarios, like vehicle-to-vehicle communication. In this paper, we propose to allocate the multi-operator D2D communication over dedicated cellular spectral resources contributed from both operators. Ideally, the operators should negotiate about the amount of spectrum to contribute, without revealing proprietary information to each other and/or to other parties. One possible way to do that is to use the sequence of operators' best responses, i.e., the operators make offers about the amount of spectrum to contribute using a sequential updating procedure until reaching consensus. Besides spectrum allocation, we need a mode selection scheme for the multi-operator D2D users. We use a stochastic geometry framework to capture the impact of mode selection on the distribution of D2D users and assess the performance of the best response iteration algorithm. With the performance metrics considered in the paper, we show that the best response iteration has a unique Nash equilibrium that can be reached from any initial strategy. In general, asymmetric operators would contribute unequal amounts of spectrum for multi-operator D2D communication. Provided that the multi-operator D2D density is not negligible, we show that both operators may experience significant performance gains as compared to the scheme without spectrum sharing.

The Method of Moments (MoM) and Method of Log-cumulants (MoLC) estimate the distribution parameters in terms of First Kind Statistics (FKS) and Second Kind Statistics (SKS), respectively. Although SKS offer a suitable framework to analyze heavy-tailed (and asymmetric) distributions, which are commonly-found in aggregate interference modeling, statistical methods developed within this framework has been understudied. For networks following point processes of varying regularity, this paper evaluates the MoM and MoLC methods to estimate the distribution parameters of interference under Rayleigh fading and log-normal shadowing. The results confirm that the gamma and log-normal models offer accurate approximations only when the interference does not present a heavy-tail. For heavy-tailed interference, the MoLC allows an accurate and fast estimation for the α-stable model.

Mobile Converged Heterogeneous Network (MCN) is seen as a viable solution to the exponentially increasing mobile traffic demand of near-future mobile services. Recently, a new dimension for improving the system performance of TDD based wireless systems has been introduced to the 3GPP standards: Dynamic TDD, which allows cell-specific frame configurations and cell-specific control of the Transmit Order (TO). In this paper, we propose unleashing the TO options in the context of MCNs where base stations are coordinated via a central controller for effective base-station-to-base-station interference cancellation (B2B-IC), and we investigate the corresponding performance improvements. Simulation results for an MCN including macro, micro, pico, femto and small cell networks operating in the same spectrum show that the TO with B2B-IC in MCN provide remarkable performance improvements at the expense of an increase in the signal exchange among the centralized controller and the base stations.

Fifth generation of cellular systems is expected to widely enable machine-type communications (MTC). The envisioned applications and services for MTC have diverse requirements which are not fully supported with current wireless systems. Ultra-reliable communications (URC) with low-latency is an essential feature for mission-critical applications, such as industrial automation, public safety, and vehicular safety applications. This feature guarantees a communication service with a high level of reliability. This paper investigates the feasibility and efficiency of URC over wireless links. It also analyzes the effectiveness of different transmission methods, including spatial diversity and support of hybrid automatic repeat request (HARQ). Finally, the importance of reliable feedback information is highlighted.

As machine-to-machine applications using cellular systems become pervasive, it is an important concern that their deployment does not jeopardize the performance of the cellular systems. Support for a massive number of machines brings technical challenges affecting the performance of the random access channel and efficiency of radio resource allocation. Capillary networks are considered as an extensions to the cellular systems for providing large-scale connectivity. This paper proposes an aggregation scheme for capillary networks connected to the LTE network to improve their communication efficiency. A gateway, an intermediate unit between machines and the base station, aggregates packets from the machines during a predefined time, and then delivers them to the LTE network. In addition, this paper analyzes the trade-offs between random access interaction, resource allocation, and communication latency. Results reveals that accepting the extra latency for accumulating packets can significantly reduce the random access requests and the required resources for the data transmissions.

The relative significance of available processing/memory resources and cost/power constraints for wireless sensor-actuator nodes depends on the application and use-case scenario. As an example, wireless automation applications usually have diverse processing/memory resource demands and energy constraints, and they bring forth development feasibility as one of the important decision criteria. For similar applications, adaptable platforms, supporting scaling between processing-memory resources and power consumption, and providing flexibility in use-case dependent hardware modifications are preferable. To the best of our knowledge, the available node platforms cannot be easily modified to support resource scaling and hardware flexibility simultaneously. In this paper, an adaptable node hardware architecture is introduced. The proposed platform can be utilized in several of roles for applications with diverse requirements, lowering the development-effort compared to alternative implementations. The practical problems of the architecture are elaborated and the details of their solutions are given. The feasibility of the introduced architecture is shown through an example implementation using off-the-shelf components, the pRoot node.

In environments with non-uniform user density and correlated slow fading, the Poisson Point Process (PPP) model fails to capture the distribution of aggregate interference. One way to overcome this issue is to divide the deployment area into multiple disjoint clusters and describe the interference from each cluster using a PPP with cluster-specific PPP intensity and pathloss model. Obviously, too few clusters do not describe the distribution of aggregate interference accurately, while on the other hand, too many clusters incur high computational and storage overheads. In this direction, we propose to divide the area according to a hierarchical clustering algorithm which uses the Akaike Information Criterion to balance between complexity and accuracy. The algorithm can be useful for database-assisted spectrum access. It allows a database to assess the interference between systems (overlapping in spectrum but not in space) in a low-complexity manner.

This is the second in a two-part series of papers on transmission order (TO) optimization in the presence of channel allocation (CA), i.e., joint optimization of the TO selection and CA problem, for interfering bidirectional wireless links. Part I of this paper thoroughly analyzes the joint optimization problem from a game theoretic perspective for a general deterministic setting. Here in Part II, we present novel distributed and centralized CA-TO algorithms, together with their performance analysis, for Device-to-Device (D2D) communications underlaying cellular networks based on the findings in Part I of this paper. Here, TO is a novel dimension for optimization. In Part II, we propose and analyze novel two distributed and one centralized joint CA-TO algorithms. Our investigations show that: i) our algorithms contain many of the existing TO algorithms and CA algorithms as its special cases and can thus be considered as a general framework for the joint CA and TO optimization. The computer simulations for TDD-based D2D communications underlaying cellular network show that the proposed distributed and centralized joint CA-TO algorithms remarkably outperform the reference algorithms.

In this two-part paper, we consider a system consisting of bidirectional wireless links that interfere with each other, which has been the of focus of intensive research recently in emerging wireless systems like Device-to-Device (D2D) communications underlaying cellular networks, heterogeneous networks, and small-cell networks. The problem is allocating the time resources (transmission orders) and frequency resources (channels) among the transmitters such that the overall network interference is minimized. Here, transmission order (TO) is a novel dimension for optimization. In Part I, we analyze the TO optimization problem in the presence of channel allocation (CA), i.e., joint CA and the TO optimization problem from a game theoretic perspective, and prove that the joint optimization problem can be formulated as an exact potential game, which has at least one “pure strategy Nash Equilibrium” for any subset of the complete CA-TO action set and for any initial CA and TO conditions. We also show that the proposed joint CA and TO game is equal to the max-cut of a novel TO-dependent holistic system interference graph. In Part II, we present novel joint CA-TO algorithms and their performance analysis in D2D communications underlays.

Uncertainties arise in the actual transmission rates achievable over various channels available on wireless links and in the interference characteristics of those links. In this work, we examine a dynamical learning approach to link scheduling for coping with this uncertainty in multihop cognitive relay networks. We formalize a scheduling with uncertainty problem (SWUP) in which the power received by nodes from a transmitting node may not be known with certainty. The schedule simultaneously should optimize transmissions in the current frame as well as perform measurements to reduce the uncertainty in network parameters. We propose a greedy algorithm to solve the SWUP and demonstrate that it is able to learn network parameters over time in order to improve network efficiency. We also formulate the SWUP as a mixed integer linear program (MILP) in order to assess the optimality of SWUP-Greedy. Extensive numerical simulations demonstrate the effectiveness of our algorithms as compared to non-learning methods.

The packet delivery ratio (PDR) performance of a wireless communication network interfered by a coexisting network is determined by the collision-time. In this paper, we propose a stochastic collision-time model for coexisting wireless networks, and analyze it in particular for coexisting low-rate wireless personal area network (LR-WPAN) and WLAN. Although, the packet collision-time of the interfered network is a complex process, as it depends on the packet size and packet inter-arrival time distributions of both networks, its properties can be inferred by modeling the interference as an alternating renewal process, leading to a stochastic collision-time model. The proposed collision-time model is utilized to derive the theoretical collision-time distributions for periodic, exponential and gamma inter-arrivals. The comparison of the theoretical and simulation based collision-time distributions for the studied inter-arrivals suggests that the proposed collision-time model can be used for performance analysis of coexisting wireless networks. In order to investigate the effects on the collision-time distribution in a realistic multi-terminal WLAN traffic shaped by the CSMA/CA mechanisms, heavy tailed hyper-Erlang and gamma distributions are fitted to an experimental inter-arrival times data-set. The goodness-of-fit tests of both distributions show that the gamma distributed inter-arrivals model the observed interfering traffic good enough, which allows analytical evaluation of LR-WPAN link quality using the derived collision-time model.

Received signal strength based device-free localization (RSS-based DFL) is recently gaining momentum as an indoor localization technology, since it enables locating people that are not cooperating with the system by carrying a device. The technology is based on monitoring the signal strength measurements of the many wireless transceivers that are deployed in the monitored area. The measurement modality can be used to accurately localize people and recent works have shown that it can be used e.g. in residential monitoring. Despite the recent advances in enhancing the accuracy of RSS-based DFL, real-world requirements such as energy efficiency and adaptation to the changing communication conditions are often neglected in the related literature. In this paper we present a management framework for RSS-based DFL which enables not only monitoring the environment and network, but to also interact with the dynamic environment and varying wireless channel. With the proposed framework, it is possible to make a considerable step forward so that RSS-based DFL can be used in long-term and real-world deployments.

In this paper, we study the strategic coexistence of macro and pico cells and investigate a utility based resource allocation technique for heterogeneous networks. Aiming at finding an objective driven allocation of resources, we focus on time domain inter-cell interference coordination (ICIC) techniques. Macro and pico cells optimally select their almost blank subframe (ABS) muting rate and cell range bias values, so that inter-cell offloading and enhanced ICIC (e-ICIC) are jointly performed. A theoretical closed form solution is provided for our utility based resource allocation algorithm as well as a heuristic solution. To substantiate our theoretical findings, system level simulations are carried out in which the proposed heuristic solution is compared to a baseline ICIC technique. Interestingly, our proposed solution is shown to yield significant gains of up to 36% compared to the baseline approach.

Exponential growth of capacity demand is making energy efficient cellular network densification an important issue. As a network is dimensioned based on the busy hour capacity demand, a large portion of network capacity remains idle for a big part of the day. Unfortunately, the power consumption of a base station does not scale down proportionally when load decreases. In this paper, a heterogeneous network (HetNet) consisting of macro and micro base station (BS) is considered. In order to save energy, we vary the density of micro BS based on the offered load and divide the load between the two layers in an energy efficient manner. With this scheme, 30% energy saving is achievable. In our analysis, we incorporate the efficiency characteristics of a realistic power amplifier (PA) and energy-delay trade-off. The analysis suggests that efficient power amplifiers and small additional flow transfer delay not only reduce the overall energy consumption but also impact the densification and load sharing in the cellular network. The results of this paper indicate that for energy efficient operation, the configuration of HetNet needs to be dynamic to cope with the temporal variation of capacity demands as well as the load needs to be shared between different layers properly.

Wireless sensor networks (WSNs) operating in 2.4 GHz unlicensed bands must explore favorable channels in order to mitigate the effects of induced interference by co-existing wireless systems and frequency selective fading. In this context, we develop a packet delivery ratio (PDR) estimation method for channel ranking in WSNs. The PDR, in general, is defined as a function of signal-to-noise ratio (SNR) and signal-to-interferenceplus-noise ratio (SINR) at the sensor and the packet collision-time distribution of the sensor link. The collision-time distribution depends on the packet size and packet inter-arrival time distributions of both networks. Under limited channel measurements, the collision-time cannot be estimated satisfactorily. In order to bypass the collision-time estimation process, the proposed PDR estimation method utilizes signal level, interference and noise characteristics identified by spectrum measurements adjusted to the intended traffic pattern of the sensor link. The proposed method is validated against the empirical PDR using off-the-shelf sensor platform in emulated multi path wireless fading channels. The results reveal that the method is accurate in modeling the empirical PDR with limited channel energy measurements. In addition, we used the estimated PDR as a metric for channel ranking and verified its effectiveness by ranking the available channels to a WSN under interference from multiple WLANs in a real environment.

In 2020, mobile access networks will experience significant challenges as compared to the situation of today. Traffic volumes are expected to increase 1000 times, and the number of connected devices will be 10-100 times higher than today in a networked society with unconstrained access to information and sharing of data available anywhere and anytime to anyone and anything. One of the big challenges is to provide this 1000-fold capacity increase to billions of devices in an affordable and sustainable way. Low energy consumption is the key to achieve this. This paper takes as starting point the situation of today, and tries to pinpoint important focus areas and potential solutions when designing an energy efficient 5G mobile network architecture. These include system architecture, where a logical separation of data and control planes is seen as a promising solution; network deployment, where (heterogeneous) ultra dense layouts will have a positive effect; radio transmission, where the introduction of massive antenna configurations is identified as an important enabler; and, finally, backhauling solutions that need to be more energy efficient than today.

In this paper, we study the energy-delay trade-off in the downlink of a proportional-fair cellular system with inter-cell interference and fading, while incorporating the efficiency characteristics of realistic amplifiers(PA). We study the potential for energy saving by lowering the transmission power level at different user locations in a cell at the cost of additional flow level delay. We identify the transmission power levels at different locations so that the energy consumption at the base station is minimized and the flow level delay is controlled. We formulate the energy minimization problem in a game theoretic framework and prove the convergence of the best response iteration algorithm. The energy saving potential is found to depend on the network load and the efficiency characteristics of the PAs. There is a significant potential for energy saving when the network operates under low to medium loads and efficient PAs are utilized. For example, with envelope tracking PA, around 90% of energy can be saved by introducing small additional flow level delay when the network load is less than 75%. Motivated by the fact that the network load keeps varying throughout the day, we take a daily traffic model and demonstrate the potential for energy saving by this scheme.

With the introduction of IEEE 802.11 power save mode (PSM), a lot of work has been done to enhance the energy saving ability of the nodes. The ultimate goal of the research is to make the networking equipments carbon neutral and prolong the lifetime of the energy limited devices for various applications; in some cases it is a trade-off between energy efficiency and delay. However, few studies have been made until now in the area of IEEE 802.11s based link specific power save mode. The link specific power save mode is a totally new concept. The essence of this method is the ability of a node to maintain different power save modes with its peer nodes. In this paper, the performance of the link specific PSM for FTP-TCP traffic is studied from the energy efficiency point of view. The throughput, the percentage of energy saving and the flow level fairness are examined in this study. Our results indicate that for a suitable combination of link specific PSM, the network not only achieves the same throughput as the active mode operation offers but also saves a significant amount of energy. The study also suggests that there is a trade-off among throughput, percentage of energy saving and fairness.

Main purpose of this paper is to investigate the frequency allocation schemes combined with a downlink Coordinated Multi-Point (CoMP) joint transmission system. We suggest 6-sector directional antenna and according sector based frequency reuse scheme for CoMP system, and compare the edge user performance of conventional Fractional Frequency Reuse (FFR) systems and suggested CoMP system. The numerical results show that the suggested scheme is better than the conventional FFR systems in the performance and the energy efficiency perspectives.

Several future wireless communication systems will exploit multiple frequency channels simultaneously to reduce delay, enhance throughput and/or improve robustness. Nevertheless, the impact of multi-channel communications on different applications has not been comprehensively studied even though it is of significant importance to understand the interdependencies between the communication and application parameters. In this paper we study the performance of networked estimation in contention-based multi-channel wireless sensor networks by focusing especially on the delay introduced by the Media Access Control (MAC) layer. We derive the required theoretical results and analyze the relationship between communication and estimation parameters in a target tracking application. In addition, we show how to optimize estimation performance by choosing appropriate estimation parameters with certain communication parameters and vice versa.

In the link layer frame retransmission strategies, the short-term variation of physical channel properties has not been fully exploited. To combat the negative impact of fading, channel memory can be used to make intelligent predictions about the upcoming channel state and frame retransmission timings can be adapted based on these predictions. In this study, two variants of simple yet efficient channel-adaptive stop-and-wait (SW) retransmission schemes are proposed for short-range wireless networks, wherein the transmitter node learns about the channel condition from the immediate past response of the receiver to adapt the next frame retransmission timing. Using Markov channel model, performance of the competitive SW protocols are captured. It is shown that, with a suitably chosen frame retransmission timing, the proposed schemes fair better than the existing SW retransmission schemes in terms of reduced energy consumption with a nominal data delivery rate trade-off. The performance gain is significant particularly in the harsh fading conditions.

The authors consider a short-range cognitive network with secondary users (SUs) generating voice calls. The SUs measure the TV spectrum and classify the measured bands as either idle or occupied. When most of the measured bands are detected to be occupied, there might not be enough capacity to serve all the SUs' calls. In this study the authors identify the minimum required number of measured bands such that the probability to serve all the secondary calls is controlled. The detection performance requirements imposed by the primary systems are also satisfied. The authors formulate this problem as an optimisation problem with optimisation parameters the number of users measuring each band and the measurement time. For a small number of SUs the authors identify the minimum required number of measured bands by using exhaustive search. For a large number of users the authors propose to solve the optimisation problem by means of a greedy algorithm. The algorithm is verified against the exhaustive search results and it is found to perform remarkably well. Therefore it is suitable for practical secondary systems searching for sharing opportunities over the TV spectrum.

In this paper we propose an inter-cell coordination scheme that coordinates the transmission time and mode of users in neighboring cells. The scheme is designed for 3G LTE systems and therefore takes into account interface constraints especially for the uplink. The objective of the scheme is to assign physical resource blocks together with the transmission mode to users in a way that minimizes interference by maximizing a utility function subject to a certain quality of service. Numerical results show that interference coordination with Dynamic-TDD actually provides significant gains when compared to Static-TDD for noticeable differences of traffic between the downlink and uplink.

The main requirement for secondary operation over the TV white spaces is interference control. In this paper, we propose an interference control scheme that allows multiple secondary systems to maintain their aggregate interference at the TV receivers under specific protection limits. The main characteristic of the proposed scheme is the small signaling overhead required for the cooperation between the secondary systems. The secondary systems need only to communicate their locations to a central network controller. The controller can then allocate the transmission power levels to the different systems such that the TV receivers are sufficiently protected. At the same time, the sum of the transmission power levels allocated to the secondary systems is maximized.

In this paper, we establish a channel ranking strategy for wireless sensor networks (WSN) in the presence of WLAN interference. The packet delivery ratio (PDR) of a sensor link is the performance metric used for channel ranking. The PDR is defined as a function of the signal-to-interference-and-noise-ratio (SINR) at the sensor and the time domain transmission characteristics of the interferer that is, the activity factor and the traffic pattern. The PDR is estimated at the sensor by using spectrum measurements. When the channel bandwidth of the interferer is large and the measurement time is also limited, the traffic pattern cannot be satisfactorily predicted for each measured channel. Because of that it is proposed to utilize Poisson and periodic traffic patterns to obtain bounds on the performance of channel ranking. Given the channel measurement time upper and lower bounds on the ranking error probability are calculated by using Poisson and periodic traffic patterns respectively. Even though the traffic pattern of WLAN interference is usually modeled with phase-type (PH) distributions, the periodic and the Poisson traffic patterns allow us to bypass the traffic pattern estimation process and relatively rank the channels based on their SINR and activity factor estimates.

The estimation of the total interference generated from a secondary system is essential for protecting the primary receivers. However, interference level estimation from a large number of secondary transmitters is a challenging problem. In this paper, we estimate the aggregate interference as an integration over the power spatial density in the secondary system's deployment area. We modify such integration-based model to contain the correlation in shadow fading. We apply the model on a cellular system downlink and study how well the proposed analytical model describes the interference level in correlated and non-correlated slow fading environment. The analysis indicates that for cell size less than five km the integration based model describes interference relatively well. For larger cell sizes, the interference is dominated by a few strong sources and has to be computed by summing over the power of all individual transmitters. It is also illustrated that the full correlated and independent slow fading provide two extremes of the amount of generated interference. The complex estimation of the impact of the fading cross correlation can be avoided by using those two extreme models as bounds to the interference level.

Most of the current wireless sensor networks operate on crowded unlicensed frequency bands. Especially in industrial wireless sensor applications this causes problems because of the received interference from other systems and coexistence problems with other wireless sensor networks. Multi-channel communications can be utilized to improve the performance under interference and enable coexistence of different applications. In this paper we propose a novel multi-channel Media Access Control (MAC) approach designed especially for industrial wireless sensor networks, named Generic Multi-channel MAC protocol (G-McMAC). G-McMAC performs well in terms of delay and throughput while it is also robust to interference and enables coexistence. In addition, G-McMAC is flexible, scalable and easily implementable to various applications since it does not use predetermined frame structures. Our theoretical results imply that G-McMAC outperforms other existing solutions. In addition, simulation results from a real-world industrial scenario show that G-McMAC is applicable for industrial wireless sensor applications.

We consider a short range cognitive network searching for spectrum holes from very wide bandwidth. In practice, one cognitive user can sense only a small portion of spectrum. Unfortunately, in fading environment a reliable detection scheme requires measurements collected by multiple users. Because of that, it is unreasonable to expect a small-sized network to sense the complete candidate bandwidth. In this paper we propose an algorithm for optimal sensing of multiple spectrum bands by multiple cognitive users. The user allocation is optimized so that the expected opportunistic throughput is maximized and the total power spent for spectrum measurements is controlled. As a constraint we use the detection performance requirements imposed by the primary systems. For a small number of spectrum bands the optimal solution can be found by exhaustive search. For a large number of spectrum bands we view the spectrum sensing as a multiple choice knapsack problem. By using algorithms for this class of problems we propose two heuristics that are suitable for optimizing spectrum sensing in multiband cognitive networks. These algorithms provide quick, near optimal solutions and are therefore suitable for practical spectrum sensing systems.

In this paper we study the capacity of spectrum sharing cognitive radios (CR) with Maximal Ratio Combining (MRC) diversity at the secondary receiver under asymmetric fading, where the channel from secondary transmitter to primary receiver suffer Nakagami-m fading while the one from secondary transmitter to its receiver follows Rayleigh Multipath fading. Our mathematical analysis and numerical results show that with MRC combining diversity at the secondary receiver, more capacity is achieved. In addition, when the SU$_{tx}$-PU$_{rx}$ channel has less severe fading which strongly affects the capacity of CR channel, utilizing MRC combining technique for CR system could reduce the effects.

In this paper we study the capacity of spectrum sharing cognitive radios (CR) with Maximal Ratio Combining (MRC) diversity at the secondary receiver under Rayleigh fading. The secondary user does not have perfect channel information of SUtx-PUrxlink, where the estimation error is considered. Our mathematical analysis and numerical results show not only that with MRC combining diversity at the secondary receiver more capacity is achieved, but also that the estimation error could be compensated. For instance, we show that even the estimation error variance is large, for example σ2= 0.8, to increase the degrees of MRC combining diversity, for instance L = 8, is able to achieve more capacity than in estimation error free case σ2= 0.

In this paper, we propose the Nash bargaining solution for the interference problem in cellular networks. The base-stations of neighboring cells negotiate with each other to determine the best number of channels that should be allocated to their edge users. While cells optimize the allocated resources on a local level, the bargaining process acts as a common framework that combines the optimization results of different cells. The bargaining can be viewed as a dynamic reuse scheme where the number of edge bands are changed according to the optimization process. The result was a noticeable gain when compared to a static reuse of frequencies. We also introduce a joint scheme that associates the Nash bargaining with load balancing handovers.

In this paper, a novel synchronization protocol is proposed especially for Cognitive Radio (CR) networks called CR-Sync. In a CR network, time synchronization is indispensable because of the requirements for coordinated and simultaneous quiet periods for spectrum sensing, as well as the common understanding of time frame/slot in many CR MAC designs. The proposed CR-Sync achieves network-wide time synchronization in a fully distributed manner, i.e., each node performs synchronization individually using CR-Sync. Contrary to many existing synchronization protocols that do not exploit CR attributes, the proposed protocol takes advantage of the potential multiple spectrum holes that are discovered by CR and distributes the synchronization of different pairs of nodes to distinct channels and thus reduces the synchronization time significantly. Detailed analysis of synchronization error and convergence time are provided. Results show that the proposed CR-Sync out-performs other protocols such as TPSN in CR networks.

Widespread proliferation of competitive technologies in licence free Industrial, Scientific and Medical (ISM) radio band is squeezing the room in frequency, temporal and spatial domain for the reliable operation of low power, low cost, IEEE 802.15.4 devices. In this context, providing some intelligence to IEEE 802.15.4 devices to analyze the environment and find the least interfered channel can result in significant improvement in performance and reliability. In this paper we prototyped a test bed that emulate wireless channels in order to evaluate the performance of IEEE 802.15.4 devices under varying IEEE 802.11 activities in different environments. The limiting thresholds for the sustainable operation are determined. Based on the thresholds, two algorithms have been proposed to rank the channels according to interference signal strength and activity level. The effectiveness of the algorithms is also verified by ranking both emulated and real channels.

In this paper we investigate the detection of OFDM primary signals using autocorrelation. The conventional model for signal detection in cognitive radio assumes that the primary signal is either absent or present with a fixed level. We show that the conventional model is the source of significant discrepancy between the analytical prediction of the detection algorithm and the simulations. We propose to design the autocorrelation-based detector using the uniform approximation for the distribution of the primary signal power. The uniform approximation allows to derive the optimal decision threshold by using the Lagrangian optimization. Finally, we are able to design the autocorrelation-based detector such that the spectrum reuse is enhanced and the analytical prediction of the detection algorithm achieves a better match with the simulation results.

The autocorrelation-based detection of OFDM signals with cyclic prefix is an appealing method for cognitive radio because it has low complexity and it allows to separate between different OFDM-based legacy systems. Unfortunately, it is also sensitive to noise estimation errors provided that a single estimate of the autocorrelation function is calculated. In this paper we design an autocorrelation-based detector for OFDM signals with cyclic prefix that is also robust in the low SNR under noise uncertainty. The proposed decision statistic allows us to calculate the error probabilities in a closed-form.

The primary signal detection is an essential operation for the secondary spectrum usage. In this paper we extend the covariance based detection for multiple-antenna receiver. The proposed method uses the noise power estimation and does not suffer from the noise level uncertainty. We analyze the detection algorithm and compute the distribution of the decision variable not only for the pure noise case but also for the jointly presence of primary signal and noise. By deriving the distribution of the primary signal in noise we are able to use the detection probability as the detector design constraint. Our analysis takes advantage of the high amount of samples available at the detector. The proposed method sets the foundation for the analysis of any detector that involves cross correlation, summing and squaring operations of samples.

Most algorithms for secondary spectrum usage are solely based on signal detection. The frequencies where the primary signal is detected with level comparable to the noise are considered occupied. As a result vast areas in the proximity of primary cells become illegal for spectrum reuse. In this paper we propose an algorithm that enhances spatial spectrum reuse by allowing secondary users to communicate in the presence of primary signal. We study how much the spectrum utilization is increased by using the proposed algorithm. The algorithm has multiple decision stages and it is based both on signal detection and generated interference estimation. It allows to minimize the decision errors and control the generated interference to primary user receivers. The algorithm is modeled by using Bayesian hypothesis testing approach. The required prior probabilities are related to the geographical interpretation of the studied system. The constructed model allows to find the optimal decision levels through the Lagrangian optimization. The algorithm performance is evaluated as a function of the number of measured power samples. Comparison to a simple detection based scheme indicates recognizable improvement especially for a low number of samples.

In this paper we study the performance of the heuristic localized gradient algorithm (HLGA) that was designed for uplink scheduling in 3G long term evolution (LTE) systems. The performance of the algorithm is examined when dynamic traffic models are assumed. With dynamic traffic, users will have different buffer occupancies. The amount of resources granted to a user need to be mapped to the size of those occupancies. We suggest for that, a procedure called dasiapruningpsila where resources are given to users who maximize a certain criteria and later on prune the extra resources and allocate them to other users. We also study the performance loss caused by more realistic assumptions on base station knowledge of the terminalpsilas buffer state. We first assume no delay in the reporting of the buffer occupancy but later evaluate the effect of buffer reporting delays and quantification of data used to report the status of a buffer.

In this paper we examine base station controlled handover based load balancing in the mobile WiMAX system and study the framework that mobile WiMAX offers for conducting load balancing and directed handovers. We also propose and evaluate a BS initiated load balancing scheme for mobile WiMAX. The simulations show that the proposed scheme can balance load efficiently within the system but also avoid the so called handover ldquoping-pongrdquo effect especially harmful e.g. for VoIP connections. We also propose an addition to the mobile WiMAX specification to distinguish load balancing based directed handovers and signal quality based rescue handovers conducted by moving terminals. This way the more important rescue handovers can be prioritized in a target Base Station.

In this paper we analyze the performance of the distributed power detection algorithm operating in a shadowing environment and with communication constraints. A well performing detection algorithm is critical for detecting a primary user's signal for dynamic spectrum allocation purposes. Dynamic spectrum allocation allows a secondary user to use a spectrum chunk provided that the primary user is not disturbed. In a shadowing environment one secondary user could not guarantee a reliable detection of primary user's signal with acceptable false detection probability. The detection performance can be greatly enhanced by combining measurements from secondary users at a fusion centre. Such combining requires communication bandwidth. We provide equations and numerically compute the bounds for two extremes of communication requirements. In the first scheme the secondary users send a full loglikelihood ratio, which in principle would require infinite amount of communication capacity. In the second scheme the secondary users send to the fusion centre only one bit, describing the hard decision made based on the loglikelihood ratio. These two cases provide performance bounds for possible practical implementations.

In this letter we study the performance of wireless sensor networks in Rayleigh fading channel, transmitted power of every sensor is assumed to be fixed during the packet transmission. The probability of packet loss (or instantaneous outage) and the accessibility are used as the performance measures. The accessibility is defined in this letter as the probability that the sensor having the weakest link gets successful access. Closed form mathematical representation of the performance is given without Gaussian approximation of the interference. Monte Carlo simulations are provided to validate the results.

Channel state information (CSI) is an important part of the adaptation process in orthogonal frequency division modulation (OFDM) where the modulation and coding schemes are adapted according to the channel state of each subcarrier. However, reporting the CSI for each subcarrier can result in considerable overhead. Besides coding and modulation, another scheme that affects the overall throughput of the system is the utilized automatic repeat request (ARQ) process. If hybrid ARQ (HARQ), such as chase combining, is utilized, all the transmitted bit energy can be harnessed by the receiver by combining the erroneously received code block with the consecutive copies transmitted by the ARQ process. The feedback information can be reduced by grouping the subcarriers into subbands with one feedback per subband. The subbands can further be grouped into one or several blocks with a joint CSI feedback per block. The subbands still have their own ARQ process to cope with the possible mismatch between the joint CSI and the actual subbands state. However, this raised the question of what is the best basis on which the joint CSI should be determined since there are subbands with different channel states in a block. In this paper we study the impact of different decision variables for the feedback information. We propose the use of rank ordering to find the subband state that maximizes the total throughput. Our results suggest that the use of the maximum subband CSI as the decision variable is desirable in case of fast mobility and low signal-to-interference + noise ratio (SINR) while the minimum value subband provides better performance in a high SINR region. However, in practical ranges of SINR and mobility speed, some value in between should be chosen. We also show that using block feedback achieves higher throughput than individual subband feedback when the number of feedback bits is small.

This paper discusses a platform for prototyping control schemes for networked control systems (NCS). The platform provides tools for simulating various networks in connection with real processes. The paper focuses on an extension to the MoCoNet platform [8], which is a unique environment for monitoring and controlling processes over the Internet in real-time. MoCoNet also provides capability of emulating different networks via network simulation. The platform is extremely valuable for control design in NCS, because the real processes can be first run over simulated networks and different network congestion scenarios can be experimented. This paper extends the MoCoNet system by integrating it with the well known and widely accepted network simulator [9], Ns2. The paper discusses various aspects of this novel integration of Ns2 with realtime operated control systems. The extension allows users of the MoCoNet system to test the control algorithms for NCS with numerous wired/wireless technologies available in Ns2. Furthermore, the paper discusses the development of a simple Java based GUI that is used to create virtual networks in Ns2.

In this paper we suggest a best-effort scheduler for high speed downlink packet access (HSPDA) systems that schedules resources in a way that achieves certain target QoS levels requested by users. Scheduling is based on adjusting the activity of a user such that the farther it is from the target the higher its activity becomes and vice versa. The objective of the algorithm will be to find the activity probabilities that achieve and maintain that target QoS level. The scheduler is based on a best-effort paradigm in a sense that when a requested target cannot be reached for instance in the case of congestion or limited resources in the system, then the algorithm would fall back to some fair scheduler allocating the resources in a fair manner

In this paper we propose a link-state clustering algorithm for both homogeneous and heterogeneous large scale wireless ad-hoc/sensor networks. The algorithm is based on IEEE 802.15.4 MAC layer protocol in which the link state of the radio channel can be monitored and traced. The algorithm forms a wireless ad-hoc/sensor network into two layers and routing problem only occurs amongst cluster heads, thus presents an efficient energy saving method to cope with the scalability problem. We consider the stability and connectivity issues of clustering algorithms when mobility of nodes is involved. Simulations show that our scheme presents a flexible clustering method with rather stable number of cluster heads, and high connectivity

In this paper, we consider the following problem in the wireless ad hoc network: Given a set of paths between source and destination, how to divide the data flow among the paths and how to control the transmission rates, times, and powers of the individual links in order to maximize the operation time of the worst network node. If all nodes are of equal importance, the operation time of the worst node is also the lifetime of the network. By solving the problem, our aim is to investigate how the network lifetime is affected by link conditions such as the maximum transmission power of a node and the peak data rate of a link. For the purpose, we start from a system model that incorporates the carrier to interference ratio (CIR) into a variable data rate of a link. With this, we can develop an iterative algorithm for the lifetime maximization, which resembles to the distributed power control in cellular systems. Numerical examples on the iterative algorithm are included to illustrate the network lifetime as a function of the maximum transmission power and the peak data rate.

The problem of scheduling delay insensitive data over a direct sequence code-division multiple-access downlink fading channel is considered. This work considers the case where only one user at a time is allowed to transmit. As the channels can be considered to vary asynchronously among the users, multiuser diversity gains can be obtained by exploiting channel adaptive scheduling. For this, schedulers of different adaptation rate are suggested and compared. In particular, the authors analyze a simple fast scheduler that schedules a user to transmit when its channel is good relative to its mean. This scheduler is resource fair in the sense it can provide the users with the same asymptotic channel access. They show that the achievable scheduling gain compared to round robin is equal to the gain of a selection diversity scheme. The scheduling gain is determined, both on closed-form and by numerical integration, for several scenarios, including multicellular systems and the effect of time delayed channel estimates. The results show that the scheduling gain is larger for channels with low average quality.

Forthcoming wideband wireless systems are supposed to support services with loose delay requirements, which allows for scheduling of data. In this work we investigate fast transmission scheduling for downlink CDMA over a Rayleigh fading channel. For a logarithmic relation between the channel quality and the effective transmission rate, merely scheduling one user at a time may not result in maximum channel utilization. Therefore, we analyze the benefits of scheduling several users at a time with a suggested scheduler, containing regular CDMA as a special case. We consider a resource fair scheduler with the objective to asymptotically allocate the same amount of energy to all users. We derive the scheduling gain on closed-form and determine its limit values for both high and low average channel quality. The results show that the better the average channel quality becomes, the more users should be allowed to transmit simultaneously.

In power control, convergence rate is one of the most important criteria that can determine the practical applicability of a given algorithm. The convergence rate of power control is especially important when propagation and traffic conditions are changing rapidly. To track these changes, the power control algorithm must converge quickly. The purpose of this paper is to generalized the existing power control framework such that we can utilize partially known link gain information in improving the convergence speed. For this purpose, block power control (BPC) is suggested with its convergence properties. BPC is centralized within each block in the sense that it exchanges link gain information within the same block. However, it is distributed in a block-wise manner and no information is exchanged between different blocks. Depending on availability of link gain information, a block can be any set of users, and even consist of a single user. Computational experiments are carried out on a DS-CDMA system, illustrating how BPC utilizes available link gain information in increasing the convergence speed of the power control.

The performance of DS-CDMA systems depends on the success in managing interference arising from both intercell and intracell transmissions. Interference management in terms of power control for real time data services like voice has been widely studied and shown to be a crucial component for the functionality of such systems. In this work we consider the problem of supporting downlink nonreal time data services, where in addition to power control, there is also the possibility of controlling the interference by means of transmission scheduling. One such decentralized schedule is to use time division so that users transmit in a one-by-one fashion within each cell. We show that this has merits in terms of saving energy and increasing system capacity. We combine this form of intracell scheduling with a suggested distributed power control algorithm for the intercell interference management. We address its rate of convergence and show that the algorithm converges to a power allocation that supports the nonreal time data users, using the minimum required power while meeting requirements on average data rate. Numerical results indicate a big potential of increased capacity and that a significant amount of energy can be saved with the proposed transmission scheme.

This paper proposes a distributed power control algorithm that uses power levels of both current and previous iterations for power update. The algorithm is developed by applying the successive overrelaxation method to the power control problem. The gain from such a second-order algorithm is in faster convergence. Convergence analysis of the algorithm in case of feasible systems is provided in this paper. Using the distributed constrained power control (DCPC) as a reference algorithm, we carried out computational experiments on a DS-CDMA system. The results indicate that our algorithm significantly enhances the convergence speed of power control. A practical version of the proposed algorithm is provided and compared with the bang-bang type algorithm used in the IS-95 and the WCDMA systems. The results show that our algorithm also has a high potential for increasing the radio network capacity. Our analysis assumes that the system is feasible in the sense that we can support every active user by an optimal power control. When the system becomes infeasible because of high traffic load, it calls for other actions such as transmitter removal, which is beyond the scope of the present paper.

A set of optimisation algorithms are studied for high density wideband code division multiple access (WCDMA) mobile networks. The method to be introduced is a radio resource knapsack packing, as packet traffic is the main consideration. The method includes centralised packet scheduling (PS) and power control (PC) algorithms that will maximise a utility function defined by the operator. The goal of the proposed algorithms is to maximise either the throughput of the system (a cluster of cells) or the value of the correctly received packets.