In this paper, we propose a framework for energy-efficient (EE) design in reconfigurable intelligent surface (RIS)-assisted multi-satellite Internet of Things (IoT) networks, taking into account the imperfect channel state information (CSI). In this framework, the multi-satellite network is used to enhance communication capabilities, while the RIS is deployed to further improve EE performance. Our objective is to maximize the EE of the proposed network by jointly optimizing the active beamforming and scheduling of the satellites and the phase shifts of RIS under the transmit power constraint for each satellite, the elevation angle constraints, and the phase shift constraints of the RIS. To handle this non-convex and NP-hard optimization problem, we propose two efficient algorithms, i.e., the Dinkelbach-BigM-Successive-Penalty (DBSP) algorithm and the Lagrangian Dual Majorization (LDM) algorithm. The DBSP algorithm is based on the alternating optimization approach, which can effectively solve the formulated non-convex optimization problem with multiple dual and complex optimization variables. Specifically, we first employ the Dinkelbach method, successive convex approximation, big-M formulation, and semidefinite relaxation method to optimize the active beamforming and the scheduling of the satellites. In addition, the penalty convex-concave procedure approach is utilized to design the phase shifts of RIS. To reduce the complexity and improve computational efficiency, we propose the LDM algorithm and derive an analytical solution for active beamforming and phase shifts by exploiting the Lagrangian dual transform, quadratic transform, and majorization-minimization algorithms. Numerical simulations are conducted to demonstrate the efficiency and convergence behavior of the proposed algorithms. Moreover, it is also demonstrated that the proposed algorithms are superior to other benchmarks, corroborating the benefits of deploying an RIS in the multi-satellite network.

Integrated sensing and communication (ISAC) technology endows users with environmental awareness capabilities, which will play a crucial role in future mobile edge computing (MEC) systems. In this paper, we consider the design of sensingassisted beam alignment and investigate resource management for a task-oriented mmWave integrated sensing, communication, and computing (ISCC) system, which can be formulated as a preference-agnostic multi-objective optimization problem. To solve this problem, we first introduce a multi-objective Markov decision process (MOMDP) to reformulate the original problem and innovatively propose a preference-agnostic multi-objective soft actor-critic (PA-MOSAC) algorithm. To demonstrate the effectiveness of our proposed system architecture and resource management algorithm, we also introduce a traditional mmWave MEC (T-MEC) system based on the same set of system parameters as a benchmark. The proximal policy optimization (PPO) algorithm, known for its robustness, is used to address resource management in the T-MEC system. Through a comprehensive comparative analysis of the two systems and algorithms, we discover that our proposed sensing-assisted beam alignment can reduce task execution delay by 25% with only 1% increase in energy consumption. We also verify the convergence of our proposed PA-MOSAC algorithm and demonstrate its superior performance over the benchmark scheme.

In recent years, unmanned aerial vehicles (UAVs) have become a key role in wireless communication networks due to their flexibility and dynamic adaptability. However, the openness of UAV-based communications leads to security and privacy concerns in wireless transmissions. This paper investigates a framework of UAV covert communications which introduces flexible reconfigurable intelligent surfaces (F-RIS) in UAV networks. Unlike traditional RIS, F-RIS provides advanced deployment flexibility by conforming to curved surfaces and dynamically reconfiguring its electromagnetic properties to enhance the covert communication performance. We establish an electromagnetic model for F-RIS and further develop a fitted model that describes the relationship between F-RIS reflection amplitude, reflection phase, and incident angle. To maximize the covert transmission rate among UAVs while meeting the covert constraint and public transmission constraint, we introduce a strategy of jointly optimizing UAV trajectories, F-RIS reflection vectors, F-RIS incident angles, and non-orthogonal multiple access (NOMA) power allocation. Considering this is a complicated non-convex optimization problem, we propose a deep reinforcement learning (DRL) algorithm-based optimization solution. Simulation results demonstrate that our proposed framework and optimization method significantly outperform traditional benchmarks, and highlight the advantages of F-RIS in enhancing covert communication performance within UAV networks.

An optical reconfigurable intelligent surface (ORIS) was expected to offer extra secrecy performance gain in a visible light communication (VLC) system. However, nonlinear impairments involved degrade the confidential signal reception and have not been fully considered in designing physical layer security (PLS). In this paper, a novel PLS approach is proposed for an ORIS-aided VLC system with consideration of practical nonlinear impairments. It is mathematically formulated to be an optimization problem that maximizes the signal-to-interference-plus-distortion-and-noise ratio of the legitimate link, while entirely suppressing that of multiple eavesdroppers by jointly optimizing the beamforming, jamming and clipping at the transmitters, and also the surface configuration in terms of mirror assignments and rotation angles at the ORIS. We decompose this mixed combinatorial and non-convex optimization problem into three sub-problems and elaborately transform them to be conventional convex programming, quadratic programming and nonlinear programming problems, respectively. Moreover, we also develop a time-efficient iterative approach to achieve the suboptimal solution with low-computational complexity. Simulation results demonstrate the improvement of secrecy performance as compared with conventional schemes, and also the robustness to severe nonlinear impairments and spatial correlation, thereby confirming the beneficial insights of this methodology for secure VLC with nonlinear devices.

Physical layer security (PLS) has emerged as a promising technology to protect critical and sensitive information against unauthorized devices. To address the key challenge of acquiring channel state information (CSI) of passive eavesdroppers in PLS implementation, we propose a novel sensing-assisted PLS scheme with the aid of reflecting reconfigurable intelligent surface (STAR-RIS). It employs a self-refine sensing scheme utilizing the artificial noise (AN) signals to iteratively estimate the eavesdroppers’ positions for CSI calculation. We aim to maximize the secrecy capacity based on the sensing-estimated CSI while tracking the eavesdroppers in full-duplex (FD) mode with integrated sensing and communication (ISAC) signals comprising artificial noise (AN). This is achieved by jointly designing the beamforming vector of information signals, the beamforming vector of AN signals, and the coefficients of the STAR-RIS. To optimize these coupled variables, we introduce an alternating optimization (AO) scheme to solve the problem recursively. In particular, we tackle the non-convexity of the beamforming optimizations for information and AN signals with the successive convex approximation (SCA) scheme and adopt a semi-definite relaxation (SDR) scheme to design the reflection and refraction coefficients of the STAR-RIS. The numerical results validate that the proposed scheme ensures secure communications against multiple eavesdroppers without any prior eavesdropper channel information. In addition, the proposed scheme can significantly improve SC performance by up to 66. 7% compared to the benchmarks without the sensing-assisted function.

This paper investigates an energy-efficient beamforming design for a hardware-impaired simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided integrated sensing and communication (ISAC) system, where the base station (BS) concurrently performs target sensing and multi-user communication. Accounting for hardware impairments (HWIs) at the BS, user equipments (UEs) and STAR-RIS, a joint optimization problem is posed to maximize the system energy efficiency, subject to constraints on required sensing and transmission capabilities, and the total power budget. To tackle the intractable conflicts among sensing and transmission metrics introduced by HWIs, we propose a novel learning-based method that integrates a denoising diffusion probabilistic model (DDPM) into a twin-delayed deep deterministic policy gradient (TD3) algorithm enhanced with prioritized experience replay (PER). By leveraging the DDPM and PER for beamforming policy determination, our approach accurately models the complex dynamics, achieving a better balance between sensing and communication performance. Simulation results demonstrate that the proposed PER-DDPM-TD3-based beamforming strategy achieves a 69.3% higher energy efficiency performance than the existing deep reinforcement learning (DRL)-based method.

As satellite communications play an increasingly important role in future wireless networks, the issue of limited link budget in satellite systems has attracted significant attention in current research. Although semantic communications emerge as a promising solution to address these constraints, it introduces the challenge of increased computational resource consumption in wireless communications. To address these challenges, we propose a multi-layer hybrid bit and generative semantic communication framework which can adapt to the dynamic satellite communication networks. Furthermore, to balance the semantic communication efficiency and performance in satellite-to-ground transmissions, we introduce a novel semantic communication efficiency metric (SEM) that evaluates the trade-offs among latency, computational consumption, and semantic reconstruction quality in the proposed framework. Moreover, we utilize a novel deep reinforcement learning (DRL) algorithm group relative policy optimization (GRPO) to optimize the resource allocation in the proposed network. Simulation results demonstrate the flexibility of our proposed transmission framework and the effectiveness of the proposed metric SEM, illustrate the relationships among various semantic communication metrics.

Rydberg atomic (RA) receivers represent a revolutionary quantum technology for wireless communications, offering unprecedented sensitivity beyond conventional radio frequency (RF) antennas. However, these receivers detect only signal amplitude, losing critical phase information. While reference signals generated by a local oscillator (LO) can assist in phase recovery, existing modulation schemes designed for conventional systems perform poorly with this quantum detection mechanism. This paper introduces a breakthrough LO-aware adaptive modulation (LOAM) scheme specifically developed for RA receivers that dynamically adapts to complex fading channel coefficients. LOAM maximizes the minimum amplitude difference between constellation points, ensuring optimal detection performance. The innovation employs an adaptive co-linear constellation architecture aligned with the combined phase of reference signal and channel coefficient. For strong reference signals, LOAM generates symmetric constellation points centered at origin; for weak signals, it adopts non-symmetric distributions. The paper mathematically derives the threshold governing these operational regimes. Simulation results reveal the transformative impact of LOAM, demonstrating performance gains exceeding 45 dB over conventional modulation schemes, including quadrature amplitude modulation (QAM), phase-shift keying (PSK), and pulse-amplitude modulation (PAM).

Deep learning-based implicit channel state information (CSI) feedback has been introduced to enhance spectral efficiency in massive MIMO systems. Existing methods often show performance degradation in ultra-low-rate scenarios and inadaptability across diverse environments. In this paper, we propose Dual-ImRUNet, an efficient uplink-assisted deep implicit CSI feedback framework incorporating two novel plug-in preprocessing modules to achieve ultra-low feedback rates while maintaining high environmental robustness. First, a novel bi-directional correlation enhancement module is proposed to strengthen the correlation between uplink and downlink CSI eigenvector matrices. This module projects highly correlated uplink and downlink channel matrices into their respective eigenspaces, effectively reducing redundancy for ultra-low-rate feedback. Second, an innovative input format alignment module is designed to maintain consistent data distributions at both encoder and decoder sides without extra transmission overhead, thereby enhancing robustness against environmental variations. Finally, we develop an efficient transformer-based implicit CSI feedback network to exploit angular-delay domain sparsity and bi-directional correlation for ultra-low-rate CSI compression. Simulation results demonstrate successful reduction of the feedback overhead by 85% compared with the state-of-the-art method and robustness against unseen environments.

AI/ML utilization in Open RAN plays a crucial role in enhancing the overall network performance, particularly for tasks such as resource and mobility management, quality of service, and security. Integrating AI and ML into the Open RAN framework offers significant improvements in network operations and functionality. However, this integration introduces several security threats, including data poisoning, data reconstruction, and adversarial attacks, which can compromise the integrity and reliability of ML models. To address these challenges and ensure that the final systems use robust, reliable, and private ML models, several techniques can be implemented. These include adversarial training, which helps models become more resilient to malicious inputs, and collaborative learning methodologies like federated learning and split learning, which enhance data privacy by decentralising the training process. By adopting these approaches, it is possible to mitigate ML security vulnerabilities, fostering a more resilient, secure, and trustworthy Open RAN ecosystem.

In this paper, a novel environmental mapping method is proposed to outline the indoor layout utilizing the line-of-sight (LoS) state information of extremely large aperture array (ELAA) channels. It leverages the spatial resolution provided by ELAA and the mobile terminal (MT)'s mobility to infer the presence and location of obstacles in the environment. The LoS state estimation is formulated as a binary hypothesis testing problem, and the optimal decision rule is derived based on the likelihood ratio test. Subsequently, the theoretical error probability of LoS estimation is derived, showing close alignment with simulation results. Then, an environmental mapping method is proposed, which progressively outlines the layout by combining LoS state information from multiple MT locations. It is demonstrated that the proposed method can accurately outline the environment layout, with the mapping accuracy improving as the number of service-antennas and MT locations increases. This paper also investigates the impact of channel estimation error and non-LoS (NLoS) components on the quality of environmental mapping. The proposed method exhibits particularly promising performance in LoS dominated wireless environments characterized by high Rician$K$-factor. Specifically, it achieves an average intersection over union (IoU) exceeding 80% when utilizing 256 service antennas and 18 MT locations.

This paper presents a novel framework for importance-aware adaptive data transmission, designed specifically for real-time computer vision (CV) applications where taskspecific fidelity is critical. An importance-weighted mean square error (IMSE) metric is introduced, assigning data importance based on bit positions within pixels and semantic relevance within visual segments, thus providing a task-oriented measure of reconstruction quality. To minimize IMSE under the total power constraint, a data-importance-aware waterfilling approach is proposed to optimally allocate transmission power according to data importance and channel conditions. Simulation results demonstrate that the proposed approach significantly outperforms margin-adaptive waterfilling and equal power allocation strategies, achieving more than 7 dB and 10 dB gains in normalized IMSE at high SNRs ($>10 ~\text{dB}$), respectively. These results highlight the potential of the proposed framework to enhance data efficiency and robustness in real-time CV applications, especially in bandwidth-limited and resource-constrained environments.

Driven by the rapid advancement of XR technologies, an increasing demand has emerged for enabling interactive holographic real-time applications of multiple users. However, in wireless systems, the challenges of practically delivering a satisfactory experience for such applications arise not only from the high bandwidth and computational demands of holographic content but also from the need to effectively integrate and transmit additional sensory data, such as haptic feedback, to convey accurate and human-perceivable semantics. In this paper, we propose an architecture that leverages the computational capabilities of 5G edge computing to collaborate with multi-user haptic-integrated holographic video applications. We detail the components of this solution architecture, including haptic-integrated holographic video capture and streaming, and a simple reinforcement learning algorithm to synchronise multiuser frames at the MEC server. A prototype of a representative hand-touch social interaction application was designed, implemented, and measured to exemplify the potential of human-perceivable, haptic-integrated holographic video applications.

The parameter size of modern large language models (LLMs) can be scaled up to the trillion-level via the sparsely-activated Mixture-of-Experts (MoE) technique to avoid excessive increase of the computational costs. To further improve training efficiency, pipelining computation and communication has become a promising solution for distributed MoE training. However, existing work primarily focuses on scheduling tasks within the MoE layer, such as expert computing and all-to-all (A2A) communication, while neglecting other key operations including multi-head attention (MHA) computing, gating, and all-reduce communication. In this paper, we propose FlowMoE, a scalable framework for scheduling multi-type task pipelines. First, FlowMoE constructs a unified pipeline to consistently scheduling MHA computing, gating, expert computing, and A2A communication. Second, FlowMoE introduces a tensor chunk-based priority scheduling mechanism to overlap the all-reduce communication with all computing tasks. We implement FlowMoE as an adaptive and generic framework atop PyTorch. Extensive experiments with 675 typical MoE layers and four real-world MoE models across two GPU clusters demonstrate that our proposed FlowMoE framework outperforms state-of-the-art MoE training frameworks, reducing training time by14%-57%, energy consumption by 10%-39%, and memory usage by 7%-32%. FlowMoE’s code is anonymously available at https://anonymous.4open.science/r/FlowMoE.

Low Earth orbit (LEO) satellite networks have become pivotal in modern communication systems, offering ubiquitous connectivity for direct-to-satellite (DtS) service in remote and underserved regions. To overcome the limited power of handheld devices, cooperative communication offers an opportunity to enhance connectivity, e.g., coverage probability, by leveraging multiple satellites to mitigate interference and improve signal quality. To this end, this paper proposes a stochastic geometry-based framework to analyze the performance of large-scale cooperative satellite networks. We model the satellites and interfering ground users as Poisson Point Processes (PPPs) and consider a typical user served by its closest subset of visible satellites. The received uplink signals at the serving satellites are combined using the maximum ratio combining (MRC) technique. We derive the coverage probability, accounting for the correlation among serving distances and interference from neighboring devices. Our analytical results, validated via Monte Carlo simulations, demonstrate a 33% increase in coverage probability when the number of cooperating devices doubles at an SINR threshold of -10 dB, offering key design insights for optimizing cooperative LEO satellite networks.

Rydberg atomic receivers offer a quantum-native alternative to conventional RF front-ends by directly detecting electromagnetic fields via highly excited atomic states. While their quantum-limited sensitivity and hardware simplicity make them promising for future wireless systems, extending their use to scalable multi-antenna and multi-carrier configurations, termed Scalable Atomic-MIMO (SA-MIMO), remains largely unexplored. This paper introduces a novel RF transmitter-atomic receiver architecture that addresses this gap. The core idea lies in a novel modulation technique called Phase-Rotated Symbol Spreading (PRSS), which transforms the nonlinear phase retrieval problem inherent to atomic detection into a tractable linear demultiplexing task. PRSS enables efficient signal processing and supports scalable MUX/DeMUX operations in atomic MIMO systems. Simulation results show that the proposed system achieves up to 2.5 dB gain under optimal maximum-likelihood detection and over 10 dB under suboptimal detection in MIMO settings. These results establish PRSS-assisted SA-MIMO as a promising architecture for realizing high-sensitivity, interference-resilient atomic wireless communication.

Blockchain-based covert communication (BCC) provides high reliability and anonymity by embedding secret data into blockchain transactions. However, existing BCC approaches still face three fundamental limitations: (i) data mining risk, since transactions containing the secret data are permanently recorded on-chain and may be detected perpetually; (ii) limited efficiency, as only small payloads (e.g., 256 bits) can be carried per transaction; and (iii) private key leakage, where receivers often need access to the sender’s private key and may incur private key exposure. To address these issues, we propose a novel covert communication model with transaction withdrawal (BCC-TW) and a multisig-based data embedding scheme (MUL-DE). BCC-TW prevents covert transactions from being confirmed by constructing higher-fee double-spend transactions, thereby ensuring that secret data only exists temporarily in the mempool. MUL-DE encodes data into redundant public keys of Bitcoin multisig addresses, thus enabling higher efficiency and avoiding private key exposure. We implement a prototype on Bitcoin testnet and evaluate its concealment and efficiency. Experimental results demonstrate that the proposed approach achieves strong indistinguishability against statistical and deep-learning-based detectors, improves communication efficiency up to 251 bits per public key, and significantly reduces cost compared with state-of-the-art baselines.

Ensuring robust and uniform network coverage is crucial for mobile network operators, particularly at the cell edges, where interference remains a persistent challenge. Traditional solutions such as network densification are complex, costly, and prone to increased interference. This paper presents the performance of recently proposed Cell-Sweeping base stations deployment in a typical 4G LTE network with Single User-Multiple Input Multiple Output (SU-MIMO) operation, use of higher-order modulation schemes, i.e. 256-Quadrature Amplitude Modulation (256-QAM), as well as using 3D antenna radiation patterns. By dynamically sweeping the antenna radiation patterns, cell-sweeping aims to significantly enhance the cell-edge performance while at the same time also harmonises the distribution of throughput in the whole cell. System-level simulations conducted using the 3rd Generation Partnership Project (3GPP) configurations reveal significant performance gain of cell-sweeping of up to 147% improvement in cell-edge throughput observed under open-loop spatial multiplexing, i.e. Transmission Mode 3 (TM3) in 3GPP LTE compared to conventional (i.e. non cell-sweeping) cellular network deployment. The results demonstrate that integrating cell-sweeping with advanced modulation and MIMO configurations is feasible and significantly improves Signal-to-Interference-plus-Noise Ratio (SINR), throughput, and Channel Quality Indicators (CQI) distribution, particularly in dense urban environments. These findings highlight the potential of cell-sweeping as an effective and simpler deployment strategy for future radio access networks.

This paper proposes a novel shape index modulation (SIM) scheme for fluid antenna (FA) systems, termed as FA-SIM, exploiting the dynamic reconfigurability of FA shapes to introduce an additional index dimension for information encoding. By integrating SIM into FA systems, the proposed FA-SIM scheme enhances transmission efficiency and spectral utilization without increasing hardware complexity, offering improved flexibility and performance in next-generation wireless communications. Furthermore, we derive a closed-form expression for the upper bound on the average bit error probability (ABEP), providing theoretical insights into the system's error performance. Simulation results demonstrate that the proposed FA-SIM scheme achieves higher spectral efficiency (SE) than conventional fixed-position antenna systems while maintaining a cost-effective hardware implementation.

In this paper, we address a physical layer security (PLS) framework for the integrated sensing and semantic communication (ISASC) system, where a multi-antenna dual-function semantic base station serves multiple single-antenna semantic communication users (SCUs) and monitors a malicious sensing target (MST), in the presence of a single-antenna eavesdropper (EVE), with both the MST and EVE aiming to wiretap information from the SCUs' signals. To enhance PLS, we employ joint artificial noise (AN) and dedicated sensing signal (DSS) in addition to wiretap coding. To evaluate the sensing accuracy, we derive the Cramer-Rao bound (CRB) as a function of the communication, sensing, and AN beamforming (BF) vectors. Subsequently, to assess the PLS level of the ISASC system, we determine a closed-form expression for the semantic secrecy rate (SSR). To achieve an optimal trade-off region between these two competing objectives, we formulate a multi-objective optimization problem for the joint design of the BF vectors. We apply semi-definite programming, Gaussian randomization method, and golden-section search techniques to address this problem. Simulation results demonstrate that the proposed scheme outperforms baseline schemes, achieving a superior trade-off between SSR and CRB.

Deep learning (DL)-based channel state information (CSI) feedback has shown great potential in improving spectrum efficiency in massive MIMO systems. However, DL models optimized for specific environments often experience performance degradation in others due to model mismatch. To overcome this barrier in the practical deployment, we propose UniversalNet, an ID-photo-inspired universal CSI feedback framework that enhances model generalizability by standardizing the input format across diverse data distributions. Specifically, Uni-versalNet employs a standardized input format to mitigate the influence of environmental variability, coupled with a lightweight sparsity-aligning operation in the transformed sparse domain and marginal control bits for original format recovery. This enables seamless integration with existing CSI feedback models, requiring minimal modifications in preprocessing and postprocessing without updating neural network weights. Furthermore, we propose an efficient eigenvector joint optimization method to enhance the sparsity of the eigenvector matrix by projecting the spectrum channel correlation into the eigenspace, thus improving the implicit CSI compression efficiency. Test results demonstrate that UniversalNet effectively improves generalization performance and ensures precise CSI feed-back, even in scenarios with limited training diversity and previously unseen CSI environments.

This paper explores the vulnerability of downlink cell-free massive MIMO systems to passive and active eaves-dropping, focusing on a 5G New Radio framework. To enhance the security of downlink transmissions over the Physical Downlink Shared Channel (PDSCH) against eavesdropping threats, we propose two novel methods based on cooperative artificial noise (AN). The first approach, called cooperative artificial noise (CAN), involves all access points (APs) broadcasting AN in the null space of the users' channel matrix to confuse potential eavesdroppers. The second approach, named partial artificial noise (PAN), divides the APs into two groups: one group cooperatively transmits AN, while the other group serves the legitimate users. Additionally, we implement three different precoding schemes for legitimate users: maximum ratio transmission, zero-forcing, and minimum mean square error. We conduct link-level simulations of wiretap channels under various frequency-selective fading scenarios and noise conditions, using tapped delay line channel models as defined by the 3GPP TR 38.901 standard. The system's security performance is evaluated by analyzing the block error rate of legitimate users and the block success rate of eavesdroppers. Despite the limitation of having only one antenna per access point, our findings demonstrate that AN can be strategically designed through the cooperation of APs. By designing appropriate groups of APs specifically for generating AN, our second approach, PAN, significantly reduces the block successive rate of eavesdroppers, lowering it from 0.2 without AN to 0.1 with CAN and further down to 0.025 with PAN.

Accurate signal localization is critical for Internet of Things applications, but precise propagation models are often unavailable due to uncontrollable factors. Simplified models, such as planar and spherical wavefront approximations, are widely used but can cause model mismatches that reduce accuracy. To address this, we propose an expected likelihood framework for model mismatch analysis and online (on-the-fly) model selection. The expected likelihood idea is that under the Gaussian assumption of independent samples, the likelihood ratio of the actual covariance matrix of the received signal is described by a distribution that depends only on the number of samples and receive antennas, not the true covariance itself. This scenario independence allows us to avoid the true model need-to-know requirement for model mismatch analysis. In this paper, we formulate an expected likelihood approach for online model and estimation parameters selection, and demonstrate its applicability and efficiency using the analytical electromagnetic model for data generation and considering different simplified models for source localization in both direct and reconfigurable intelligent surface assisted diverse IoT environments.

Generative foundation models can revolutionize the design of semantic communication (SemCom) systems by enabling high fidelity exchange of semantic information at ultra-low rates. In this work, a generative SemCom framework utilizing pre-trained foundation models is proposed, where both uncoded forward-with-error and coded discard-with-error schemes are developed for the semantic decoder. Using the rate-distortion-perception theory, the relationship between regenerated signal quality and transmission reliability is characterized, which is proven to be non-decreasing. Based on this, semantic values are defined to quantify the semantic similarity between multimodal semantic features and the original source. We also investigate semantic-aware power allocation problems that minimize power consumption for ultra-low rate and high fidelity SemComs. Two semantic-aware power allocation methods are proposed by leveraging the non-decreasing property of the perception-error relationship. Based on the Kodak dataset, perception-error functions and semantic values are obtained for image tasks. Simulation results show that the proposed semantic-aware method significantly outperforms conventional approaches, particularly in the channel-coded case (up to 90% power saving).

Life-transformative applications such as immersive extended reality are revolutionizing wireless communications and computer vision (CV). This paper presents a novel framework for importance-aware adaptive data transmissions, designed specifically for real-time CV applications where task-specific fidelity is critical. A novel importance-weighted mean square error (IMSE) metric is introduced as a task-oriented measure of reconstruction quality, considering sub-pixel-level importance (SP-I) and semantic segment-level importance (SS-I) models. To minimize IMSE under total power constraints, data-importance-aware waterfilling approaches are proposed to optimally allocate transmission power according to data importance and channel conditions, prioritizing sub-streams with high importance. Simulation results demonstrate that the proposed approaches significantly outperform margin-adaptive waterfilling and equal power allocation strategies. The data partitioning that combines both SP-I and SS-I models is shown to achieve the most significant improvements, with normalized IMSE gains exceeding 7 dB and 10 dB over the baselines at high SNRs (> 10 dB). These substantial gains highlight the potential of the proposed framework to enhance data efficiency and robustness in real-time CV applications, especially in bandwidth-limited and resource-constrained environments.

Supporting ubiquitous deployment of built-in Internet service with Software Defined Networking (SDN), Network Function Virtualization (NFV), and Low Earth Orbit (LEO) satellite constellations has been widely accepted as one of the key technologies for the next-generation communication services. By integrating terrestrial and space network capabilities, new design features are introduced to existing network ecosystems. For instance, terrestrial Virtual Network Functions (VNFs) can now be hosted on satellites, utilizing satellite highways. This requires expanding the roles of the control units, originally responsible for terrestrial data planes, to include space-based counterparts. As a result, seamless integration of Service Function Chains (SFCs) across satellite constellations and terrestrial control units becomes a challenge due to the topology dynamics caused by high-speed LEO satellites. In this paper, we propose the Geosynchronous Service Function Chaining (GSFC) scheme to facilitate programmable, Internet SFC operations based on LEO satellite network environments. The key idea is to cluster adjacent LEO satellites to represent logical VNF containers at the fixed positions, where the initial VNFs at the region are continuously filled up by the traversing satellite payload functions in a predictable manner. With this design, the ground-based controllers can maintain the space-terrestrial SFCs without being affected by the constantly shifting satellite VNFs, and thereby large-scale and complex recalculation for the routing policies is avoided. The design principle introduces a ground-breaking approach to space Internet protocol stacks, facilitating robust routing for SFC operations across integrated space and terrestrial networks. Our simulation results verify the feasibility of the proposed GSFC-based VNF orchestration mechanism and reveal the trade-offs in both data and control plane performance.

This paper presents GBSense, an innovative compressed spectrum sensing system designed for GHz-bandwidth signals in dynamic spectrum access (DSA) applications. GBSense introduces an efficient approach to periodic non-uniform sampling, capturing wideband signals using significantly lower sampling rates compared to traditional Nyquist sampling. By integrating time-interleaved analog-to-digital conversion, GBSense overcomes the hardware complexity typically associated with traditional multicoset sampling, providing precise, adjustable sampling patterns without the need for analog delay circuits. The system’s ability to process signals with a 2 GHz radio frequency bandwidth using only a 400 MHz average sampling rate enables more efficient spectrum monitoring and access in wideband cognitive radios. Lab tests demonstrate 100% accurate spectrum detection when the spectrum occupancy is below 100 MHz and over 80% accuracy for occupancy up to 200 MHz. Additionally, an integrated system utilizing a low-power Raspberry Pi processor achieves a low processing latency of around 30 ms per frame, demonstrating the system’s potential for DSA applications in next-generation wireless networks.

In this paper, we analyse the reliability performance for the orthogonal time frequency space (OTFS) based low earth orbit (LEO)-satellite-air-terrestrial (LSAT) communication system. To facilitate the downlink transmission from the LEO satellite to the terrestrial node, a group of randomly distributed mobile unmanned aerial vehicles (UAVs) are employed to serve as the relays with decode-and-forward (DF) scheme. With the aid of stochastic geometry approach, the distribution of mobile UAVs is modeled by Poisson point processes (PPP) process with two motion modes: user dependent model (UDM) and user independent model (UIM). We derive the approximate closed-form expressions for the outage probabilities of the LSAT system under two UAV motion modes. Finally, the simulation results demonstrate that the reliability of the LSAT system can be significantly enhanced by using OTFS scheme, and by properly adjusting the UAV deployment parameters, corroborating the theoretical derivation.

This paper aims to analyze the stochastic performance of a multiple input multiple output (MIMO) integrated sensing and communication (ISAC) system in a downlink scenario, where a base station (BS) transmits a dual-functional radar-communication (DFRC) signal matrix, serving the purpose of transmitting communication data to the user while simultaneously sensing the angular location of a target. The channel between the BS and the user is modeled as a random channel with Rayleigh fading distribution, and the azimuth angle of the target is assumed to follow a uniform distribution. Due to the randomness inherent in the network, the challenge is to consider suitable performance metrics for this randomness. To address this issue, for users, we employ the user’s rate outage probability (OP) and ergodic rate, while for target, we propose using the OP of the Cramér-Rao lower bound (CRB) for the angle of arrival and the ergodic CRB. We have obtained the expressions of these metrics for scenarios where the BS employs two different beamforming methods. Our approach to deriving these metrics involves computing the probability density function (PDF) of the signal-to-noise ratio for users and the CRB for the target. We have demonstrated that the central limit theorem provides a viable approach for deriving these PDFs. In our numerical results, we demonstrate the trade-off between sensing and communication (S & C) by characterizing the region of S & C metrics and by obtaining the Pareto optimal boundary points, confirmed with simulations.

To enable improved spectral efficiency and uniform service of cell-free massive multiple-input multiple-output (CF-mMIMO) systems, challenges such as excessive communication overheads for gathering global CSI at the central processor (CP) and high computational complexity for centralized interference management should be addressed. Deep learning (DL)-based decentralized optimization approaches have been proposed to tackle these challenges. However, conventional black-box deep neural network (DNN)-based fronthaul coordination methods require large DNNs and are less adaptable to changes in wireless channel statistics and environments. In this paper, we develop a model-based neural collaboration (NC) framework, which leverages DNN-assisted signal processing built upon our proposed low-complexity distributed weighted minimum mean squared error (L-D-WMMSE) algorithm, for downlink CF-mMIMO beamforming. DNN components for neural initialization (NI) and neural parameterization (NP) reduce the fronthaul overheads while improving the sum-rate of user equipments (UEs). Through our model-based approach, these DNN components are designed to be compact, thereby reducing computational complexity. We derive gradients for DNN updates that each AP can compute using its local CSI and the fronthaul messages exchanged for downlink beamforming. This enables a distributed online DNN adaptation that runs during normal operation of the CF-mMIMO system at zero additional fronthaul overheads.

Millimeter wave (mmW) is a promising technology for the next generation of mobile communications. However, the transmission efficiency and communication reliability of heterogeneous mobility user networks are limited by the frequent mmW beam alignment and the severe Doppler shift in the time-varying channel, respectively. To address this challenge, a joint resource allocation in frequency domain, time domain and power domain is investigated for the mmW communication network based on non-orthogonal multiple access (NOMA) and orthogonal time-frequency space (OTFS) techniques. The average spectral efficiency of high-mobility user equipment (H-UE) is maximized by the joint optimization of UE scheduling, beamwidth and transmit power. In order to solve the non-convex mixed integer problem of rate maximization, we propose the multi-dimensional resource allocation scheme based on alternate optimization method (AO-MRA), which decouples the initial intractable problem into two solvable sub-problems. In particular, based on the majorization-minimization approach, the scheduling algorithm is proposed to find the best UE scheduling for the NOMA groupings of heterogeneous mobility UEs, and the joint beamwidth and transmit power (JBP) algorithm is further designed for the optimal transmission time and power of base station in the mmW communication network. The proposed AO-MRA scheme can obtain effective suboptimal solutions of the initial problem. Simulation results demonstrate that the AO-MRA scheme is superior to other benchmark schemes in maximizing transmission efficiency. Moreover, the AO-MRA scheme is more suitable for low-power and high-bandwidth situations, and has superior spectral efficiency in terms of delay and Doppler high-resolution.

In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly found, though, that accuracy gains diminished as we added layers to the RNN. To investigate this, in this article, we build a parallel model to illuminate and understand the internal operation of neural networks (NNs), such as the RNN, which store their internal state to process sequential inputs. This model is widely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looking at RNN processing from a probability density function (pdf) perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection accuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. To demonstrate the fidelity of the model, we validate it against each stage of RNN processing and output predictions. As a result, we have been able to explain the reasons for RNN performance limits with useful insights for future designs for RNNs and similar types of NN.

Reconfigurable Intelligent Surface (RIS) technology is designed to improve channel propagation and, in turn, the performance of wireless communication systems. The extent of the improvement heavily relies on the phase shift optimization, a.k.a. passive beamforming design, at the RIS. This paper proposes novel approaches for simplifying this process in large RIS-aided multiple-input multiple-output (MIMO), a.k.a. MIMO-RIS, systems. More specifically, we derive two novel simplified closed-form approximations (CFAs) of the MIMO-RIS capacity expression in the large number of elements/antennas asymptotic regime and identify various scenarios for which they tightly approximate the classic MIMO-RIS capacity expression. We then design two novel simplified RIS phase optimization algorithms for lossless and practical metasurfaces with reduced computational complexity. We also demonstrate that, in certain practical scenarios, very simple guidelines can be used for setting the phases of MIMO-RIS systems. Numerical evaluations validate the accuracy of our CFAs and simplified RIS phase-shift optimization algorithms. They also show that our simplified approaches can deliver either similar or even better capacity performance than existing methods, especially for practical metasurfaces, in various scenarios.

Sparse code multiple access (SCMA) and multiple input multiple output (MIMO) are considered as two efficient techniques to provide both massive connectivity and high spectrum efficiency for future machine-type wireless networks. This paper proposes a single sparse graph (SSG) enhanced expectation propagation algorithm (EPA) receiver, referred to as SSG-EPA, for uplink MIMO-SCMA systems. Firstly, we reformulate the sparse codebook mapping process using a linear encoding model, which transforms the variable nodes (VNs) of SCMA from symbol-level to bit-level VNs. Such transformation facilitates the integration of the VNs of SCMA and low-density parity-check (LDPC), thereby emerging the SCMA and LDPC graphs into a SSG. Subsequently, to further reduce the detection complexity, the message propagation between SCMA VNs and function nodes (FNs) are designed based on EPA principles. Different from the existing iterative detection and decoding (IDD) structure, the proposed EPA-SSG allows a simultaneously detection and decoding at each iteration, and eliminates the use of interleavers, de-interleavers, symbol-to-bit, and bit-to-symbol LLR transformations. Simulation results show that the proposed SSG-EPA achieves better error rate performance compared to the state-of-the-art schemes.

Volumetric video streaming, an innovative media application, facilitates the real-time and immersive teleportation of individuals or objects into the virtual environment of the audience. Unlike conventional video streaming applications, volumetric content is particularly vulnerable to network fluctuations, which can lead to performance degradation such as reduced FPS, delayed frame delivery. In this research, we introduce an eBPF-based network function that duplicates packets along the pathways between network nodes, ensuring timely packet delivery amid network instability. Furthermore, we propose a path elimination algorithm to discard paths incapable of delivering frames within the target latency. Our implementation and evaluation validate the rapid and robust performance achieved across various resolution levels.

Semantic communication is a new paradigm for information transmission that integrates the essential meaning (semantics) of the message into the communication process. However, like in classic wireless communications, the open nature of wireless channels poses security risks for semantic communications. In this paper, we characterize information-theoretic limits for the secure transmission of a semantic source over a wiretap channel. Under separate secrecy and distortion constrains for semantics and observed data, we present general inner and outer bounds on the rate-distortion-equivocation region. We also reduce the general region to the case of Gaussian source and Gaussian wiretap channel and provide numerical evaluations.

Extremely large aperture array (ELAA) is a promising multiple-input multiple-output (MIMO) technique for next generation mobile networks. In this paper, we propose two novel approaches to accelerate the convergence of current iterative MIMO detectors in ELAA channels. Our approaches exploit the static components of the ELAA channel, which include line of sight (LoS) paths and deterministic non-LoS (NLoS) components due to channel hardening effects. This paper proposes novel convergence acceleration techniques for fast iterative ELAA-MIMO detection by leveraging the static channel component, including the LoS paths and deterministic NLoS components that arise due to channel hardening. Specifically, these static channel components are utilized in two ways: as preconditioning matrices for general iterative algorithms, and as initialization for quasi-Newton (QN) methods. Simulation results show that the proposed approaches converge significantly faster compared to current iterative MIMO detectors, especially under strong LoS conditions with high Rician K-factor. Furthermore, QN methods with the proposed initialization matrix consistently achieve the best convergence performance while maintaining low complexity.

Efficient resource management and orchestration are essential to maximise the benefits of network slicing in 5G and beyond networks. This paper presents NFScaler, an Artificial Intelligence (AI)-powered 5G network function scaler that can perform a zero-shot sim2real transfer. The proposed NFScaler automatically adjusts the number of 5G network function instances running in a network slice based on changes in user traffic to meet the QoS requirement of the slice while reducing the resource consumption (i.e. CPU and power). The NFScaler consists of two main parts: a domain randomiser and a deepreinforcement learning (DRL) agent. The domain randomiser randomises the dynamic parameters of a simulation to provide the DRL agent with a range of simulation environments. The DRL agent interacts with both the original simulation and the randomised simulation to learn a generalisable autoscaling policy. The performance of the proposed NFScaler is evaluated in a live 5G network and compared with the threshold-based KEDA scaler, which is the industry standard event-based autoscaler used in cloud native environments, as well as a DRL method that does not involve sim2real transfer. The experimental findings demonstrate that NFScaler effectively guarantees the QoS requirements of the network slice, outperforming the benchmark methods. In particular, NFScaler achieves an improvement of almost 40% in QoS (throughput) performance compared to KEDA Scaler. Furthermore, the proposed NFScaler allocates a significantly lower number of CPU cores to the user plane of the network slice, with an average of 75% fewer cores than when the user plane is hosted on a dedicated bare metal server. In addition, the NFScaler reduces the power consumption of the user plane of the network slice by an average of 45% compared to hosting the user plane on a dedicated bare metal server.

Emerging dual-functional radar communication (RadCom) systems promise to revolutionize wireless systems by enabling radar sensing and communication on a shared platform, thereby enhancing spectral efficiency. However, the high transmit power required for efficient radar operation poses risks by potentially exceeding the electromagnetic field (EMF) exposure limits enforced by the regulations. To address this challenge, we propose an EMF-aware signalling design that enhances RadCom system performance while complying with EMF constraints. Our approach considers exposure levels not only experienced by network users but also in sensitive areas such as schools and hospitals, where the exposure must be further reduced. First, we model the exposure metric for the users and the sectors that encounter sensitive areas. Then, we design the waveform by exploiting the trade-off between radar and communication while satisfying the exposure constraints. We reformulate the problem as a convex optimization program and solve it in closed form using Karush-Kuhn-Tucker (KKT) conditions. The numerical results demonstrate the feasibility of developing a robust RadCom system with low electromagnetic (EM) radiations.

Terabits per second (Tbps) wireless links are essen-tial for realizing emerging mobile immersive experiences such as holographic telepresence. A promising approach to achieve such data rates is to exploit ultra-wide bandwidths. However, the need for ultra-fast, high-precision analogue-to-digital converters (ADCs) in digital systems to exploit such ultra-wide bandwidths becomes impractical in terms of power consumption. In addition, the increased sampling rates that challenge the speed of modern digital processors and the algorithmic complexity make performing ultra-fast digital signal processing challenging in terms of processing latency and power consumption. A promising approach to overcome these bottlenecks is DigiLogue processing, which suggests to remove ADCs and to perform signal processing directly in the analogue domain. However, practical DigiLogue receivers need to support fading channels, in which accurate channel estimation becomes crucial for reliable signal detection. In addition, systems operating in high-frequency bands (e.g., mm-Wave and THz), where ultra-wide bandwidths are available, experience a small number of channel taps, in which analogue-based channel estimation approaches can be realized with low complexity. However, existing analogue-based channel estimation approaches target hard detection/decoding and they are not able to provide the channel information required to perform “soft” processing in DigiLogue receivers. In this work, we propose an analogue-based channel estimation approach based on utilizing Golay sequences employed in recent high-speed standards, which is suitable for DigiLogue receivers. Our approach achieves a 1 dB better signal-to-noise ratio (SNR) in error performance compared to existing analogue methods and saves more than 29 x power than the digital counterpart.

In order to overcome the vicious competition between different high-volume services, we study the wireless resource sharing problem in the transmission process of the MEC-caching-coexist (MCCe) system with the capability of mmWave communications. The multiobjective Markov decision process (MOMDP) is introduced to model the task scheduling and resource allocation problem for the mmWave links, which aims to minimize the transmission delay and energy consumption simultaneously. Note that, for practical consideration, the exact channel information of all links are not known. We propose a novel multiobjective deep reinforcement learning with discrete-continuous hybrid action space (MODRL/HA) algorithm. In particular, the envelope updated design (EUD) is designed to realize the multiobjective optimization from the perspective of the Bellman operator. On the other hand, the parameterized network design (PND) is developed to deal with the hybrid action space of discrete task scheduling and continuous beamwidth and power variables. Our simulations show that, the MODRL/HA algorithm can improve 22% performance in terms of the tradeoff between delay and energy consumption compared with the benchmark schemes, which are original deep deterministic policy gradient (DDPG) and multiobjective DDPG (MODDPG) algorithms.

Physical layer security (PLS) technologies are expected to play an important role in the next-generation wireless networks, by providing secure communication to protect critical and sensitive information from illegitimate devices. In this paper, we propose a novel secure communication scheme where the legitimate receiver use full-duplex (FD) technology to transmit jamming signals with the assistance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) which can operate under the energy splitting (ES) model and the mode switching (MS) model, to interfere with the undesired reception by the eavesdropper. We aim to maximize the secrecy capacity by jointly optimizing the FD beamforming vectors, amplitudes and phase shift coefficients for the ES-RIS, and mode selection and phase shift coefficients for the MS-RIS. With above optimization, the proposed scheme can concentrate the jamming signals on the eavesdropper while simultaneously eliminating the self-interference (SI) in the desired receiver. To tackle the coupling effect of multiple variables, we propose an alternating optimization algorithm to solve the problem iteratively. Furthermore, we handle the non-convexity of the problem by the the successive convex approximation (SCA) scheme for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-RIS, as well as the phase shifts optimizations for the MS-RIS. In addition, we adopt a semi-definite relaxation (SDR) and Gaussian randomization process to overcome the difficulty introduced by the binary nature of mode optimization of the MS-RIS. Simulation results validate the performance of our proposed schemes as well as the efficacy of adapting both two types of STAR-RISs in enhancing secure communications when compared to the traditional self-interference cancellation technology.

This paper focuses on trigger-based (TB) random access (RA) strategies for a multiple-packet reception channel with channel capability$M$($M$-MPR channel), where up to$M$packets can be received simultaneously, while more than$M$concurrent packet transmissions result in collisions and are considered lost. We model the contention for the TB MPR framework and derive the optimal RA strategies that maximize two metrics:i)the normalized saturation throughput, andii)the number of stations successfully occupying the MPR channel within each access round. We generalize the$p$-persistent carrier sense multiple access (CSMA) by enabling it to explore both the MPR dimension and the time dimension to adapt the access probabilities. We also propose suboptimal strategies to reduce the complexity, customized for the considered TB framework. Comprehensive performance evaluations and comparisons with respect to a wide range of system parameters and metrics are provided.

Open Radio Access Networks (Open-RAN) trigger a shift from conventional monolithic RAN architectures to disaggregated designs with open interfaces, diversifying the 5G supply chain and boosting innovation. It is envisaged that Open-RAN deployments will be heavily software-based, allowing for higher flexibility and faster integration of new features. However, existing software-based solutions, seem to be unable to realize practical and standard-compliant Multiple-Input, Multiple-Output (MIMO) designs with a large number of concurrently transmitted information streams, as the 5G New Radio standard requires in order to substantially improve connectivity and throughput. In this context, we introduce MIMO-SoftiPHY, the first 3GPP and Open-RAN compliant, software-based physical layer (PHY) design and implementation framework that can practically realize MIMO designs with large numbers of information streams in a power-efficient manner. MIMO-SoftiPHY is inherently integrated with OpenAirInterface, enabling practical, software-based MIMO deployments with commercial-off-the-shelf user equipment. Specifically, MIMO-SoftiPHY achieves real-time performance for 12 MU-MIMO streams at a 10 MHz bandwidth and 8 streams at a 20 MHz. In addition, and in contrast to existing designs, MIMO-SoftiPHY can also support non-linear base-station processing in real-time that, as we show, can result in substantial power savings at the radio side, by obviating the need for a “massive” number of base-station antennas.

Compressed spectrum sensing (CSS) plays a pivotal role in dynamic spectrum access within mobile cognitive radio networks by offering reduced power consumption and lower hardware costs. The multicoset sampler, a well-known implementation for periodic nonuniform sampling, has been widely studied and is considered a promising architecture for realizing CSS. This paper focuses on the design of the multicoset sampling pattern, aiming at enhancing the isometry property of the sensing matrix. Unlike previous studies which assume a noise-free setup, our work considers the problem in a real-world environment with noise. First, we propose a deterministic algorithm for sampling pattern generation, particularly for specific hardware setup parameters. This algorithm offers strict mutual-coherence control in the multicoset sensing matrix. To address more general hardware configurations, we propose two optimization algorithms. One of them searches for nearly optimal sampling patterns through a random search strategy, while the other employs a greedy pursuit strategy to find a local optimizer. Furthermore, we propose an algorithm to iteratively optimize the sampling pattern between consecutive spectrum sensing windows by minimizing a restricted version of mutual coherence. The excellent performance of our proposed algorithms has been demonstrated through numerical experiments and has been verified on a self-developed hardware platform.

Open Radio Access Networks (O-RANs) have revolutionized the telecom ecosystem by bringing intelligence into disaggregated RAN and implementing functionalities as Virtual Network Functions (VNF) through open interfaces. However, dynamic traffic conditions in real-life O-RAN environments may require necessary VNF reconfigurations during run-time, which introduce additional overhead costs and traffic instability. To address this challenge, we propose a multi-objective optimization problem that minimizes VNF computational costs and overhead of periodical reconfigurations simultaneously. Our solution uses constrained combinatorial optimization with deep reinforcement learning, where an agent minimizes a penalized cost function calculated by the proposed optimization problem. The evaluation of our proposed solution demonstrates significant enhancements, achieving up to 76% reduction in VNF reconfiguration overhead, with only a slight increase of up to 23% in computational costs. In addition, when compared to the most robust O-RAN system that doesn’t require VNF reconfigurations, which is Centralized RAN (C-RAN), our solution offers up to 76% savings in bandwidth while showing up to 27% overprovisioning of CPU.

Integrating Low Earth Orbit (LEO) satellites with terrestrial network infrastructures to support ubiquitous Internet service coverage has recently received increasing research momentum. One fundamental challenge is the frequent topology change caused by the constellation behaviour of LEO satellites. In the context of Software Defined Networking (SDN), the controller function that is originally required to control the conventional data plane fulfilled by terrestrial SDN switches will need to expand its responsibility to cover their counterparts in the space, namely LEO satellites that are used for data forwarding. As such, seamless integration of the fixed control plane on the ground and the mobile data plane fulfilled by constellation LEO satellites will become a distinct challenge. For the very first time in the literature, we propose in this paper the Virtual Data-Plane Addressing (VDPA) scheme by leveraging IP addresses to represent virtual switches at the fixed space locations which are periodically instantiated by the nested LEO satellites traversing them in a predictable manner. With such a scheme the changing data-plane network topology incurred by LEO satellite constellations can be made completely agnostic to the control plane on the ground, thus enabling a native approach to supporting seamless communication between the two planes. Our simulation results prove the superiority of the proposed VDPA based flow rule manipulation mechanism in terms of control plane performance.

Adequate and uniform network coverage provision is one of the main objectives of cellular service providers. Additionally, the densification of cells exacerbates coverage and service provision challenges, particularly at the cell-edges. In this paper, we present a new approach of cell-sweeping-based Base Stations (BSs) deployments in cellular Radio Access Networks (RANs) where the coverage is improved by enhancing the cell-edge performance. In essence, the concept of cell-sweeping rotates/sweeps the sectors of a site in azimuth continuously/discretely resulting in near-uniform distribution of the signal-to-interference-plus-noise ratio (SINR) around the sweeping site. This paper investigates the proposed concept analytically by deriving expressions for the PDF/CDF of SINR and achievable rate; and with the help of system-level simulations, it shows that the proposed concept can provide throughput gains of up to 125% at the cell-edge. Then, using a link-budget analysis, it is shown that the maximum allowable path loss (MAPL) increases by 2.1 dB to 4.1 dB corresponding to the gains in wideband SINR and post-equalized SINR, respectively. This increase in MAPL can be translated to cell-radius/area with the help of the Okumura-Hata propagation model and results in cell-coverage area enhancement by 30% to 66% in a Typical Urban cell deployment scenario.

The full-duplex (FD) communication can achieve higher spectrum efficiency than conventional half-duplex (HD) communication; however, self-interference (SI) is the key hurdle. This paper is the first work to propose the intelligent omni surface (IOS)-assisted FD multi-input single-output (MISO) FD communication systems to mitigate SI, which solves the frequency-selectivity issue. In particular, two types of IOS are proposed, energy splitting (ES)-IOS and mode switching (MS)-IOS. We aim to maximize data rate and minimize SI power by optimizing the beamforming vectors, amplitudes and phase shifts for the ES-IOS and the mode selection and phase shifts for the MS-IOS. However, the formulated problems are non-convex and challenging to tackle directly. Thus, we design alternative optimization algorithms to solve the problems iteratively. Specifically, the quadratic constraint quadratic programming (QCQP) is employed for the beamforming optimizations, amplitudes and phase shifts optimizations for the ES-IOS and phase shifts optimizations for the MS-IOS. Nevertheless, the binary variables of the MS-IOS render the mode selection optimization intractable, and then we resort to semidefinite relaxation (SDR) and Gaussian randomization procedures to solve it. Simulation results validate the proposed algorithms’ efficacy and show the effectiveness of both the IOSs in mitigating SI compared to the case without an IOS.

In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management (IM) schemes to effectively mitigate interference. To address this, a novel UAV-RIS-aided IM scheme is proposed for SAGIN, where different types of channel state information (CSI) including no CSI, instantaneous CSI, and delayed CSI, are considered. According to the types of CSI, interference alignment, beamforming, and space-time precoding are designed at the satellite and terrestrial transmitter side, and meanwhile, the UAV-RIS is introduced for the cooperating interference elimination process. Additionally, the degrees of freedom (DoF) obtained by the proposed IM scheme are discussed in depth when the number of antennas on the satellite side is insufficient. Simulation results show that the proposed IM scheme improves the system capacity in different CSI scenarios, and the performance is better than the existing IM benchmarks without UAV-RIS, but the performance improvement is at the cost of the requirement on the elements of UAV-RIS.

In this paper, a novel spatially non-stationary fading channel model is proposed for multiple-input multiple-output (MIMO) system with extremely-large aperture service-array (ELAA). The proposed model incorporates three key factors which cause the channel spatial non-stationarity:1) link-wise path-loss;2) shadowing effect;3) line-of-sight (LoS)/non-LoS state. With appropriate parameter configurations, the proposed model can be used to generate computer-simulated channel data that matches the published measurement data from practical ELAA-MIMO channels. Given such appealing results, the proposed fading channel model is employed to study the cumulative distribution function (CDF) of ELAA-MIMO channel capacity. For all of our studied scenarios, it is unveiled that the ELAA-MIMO channel capacity obeys the skew normal distribution. Moreover, the channel capacity is also found close to the Gaussian or Weibull distribution, depending on users’ geo-location and distribution. More specifically, for single-user equivalent scenarios or multiuser scenarios with short user-to-ELAA distances (e.g., 1 m), the channel capacity is close to the Gaussian distribution; and for others, it is close to the Weibull distribution. Finally, the proposed channel model is also employed to study the impact of channel spatial non-stationarity on linear MIMO receivers through computer simulations. The proposed fading channel model is available at https://github.com/ELAA-MIMO/non-stationary-fading-channel-model.

Current iterative multiple-input multiple-output (MIMO) detectors suffer from slow convergence when the wireless channel is ill-conditioned. The ill-conditioning is mainly caused by spatial correlation between channel columns corresponding to the same user equipment, known as intra-user interference. In addition, in the emerging MIMO systems using an extremely large aperture array (ELAA), spatial non-stationarity can make the channel even more ill-conditioned. In this paper, user-wise singular value decomposition (UW-SVD) is proposed to accelerate the convergence of iterative MIMO detectors. Its basic principle is to perform SVD on each user’s sub-channel matrix to eliminate intra-user interference. Then, the MIMO signal model is effectively transformed into an equivalent signal (e-signal) model, comprising an e-channel matrix and an e-signal vector. Existing iterative algorithms can be used to recover the e-signal vector, which undergoes post-processing to obtain the signal vector. It is proven that the e-channel matrix is better conditioned than the original MIMO channel for spatially correlated (ELAA-)MIMO channels. This implies that UW-SVD can accelerate current iterative algorithms, which is confirmed by our simulation results. Specifically, it can speed up convergence by up to 10 times in both uncoded and coded systems.

The cumulative distribution function (CDF) of a non-central$\chi ^{2}$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For ultra-reliable low-latency communication (URLLC), it is important but mathematically challenging to determine the outage threshold for an extremely small outage target. This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most effective lower bound. This finding is associated with three rigorously established properties of the Cher-LB with respect to the mean, variance, reliability requirement, and degrees of freedom of the non-central$\chi ^{2}$-distributed RV. The Cher-LB is then employed to predict the beamforming gain in URLLC for both conventional multi-antenna systems (i.e., MIMO) under first-order Markov time-varying channel and reconfigurable intellgent surface (RIS) systems. It is exhibited that, with the proposed Cher-LB, the pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.

In this paper, we investigate the fairness of integrated sensing and communication (ISAC) systems assisted by simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) for eliminating the interference of the sensing signal before decoding the signals of communication users. We formulate the problem of maximizing the fairness between communication users and the sensing target by jointly designing the transmit beamforming vectors of the base station (BS) and the coefficient matrices of the STAR-RIS. For tackling the challenging optimization problem, a low-complexity algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP) techniques is proposed for obtaining the transmit beamforming vectors and the STAR-RIS coefficient matrices. For the ISAC system with a single user, we further derive the closed-form expression of the BS transmit beamforming vector for reducing the complexity of the algorithm. Then, the non-convex optimization problem of the STAR-RIS coefficient matrices can be solved efficiently by transforming it into a convex problem. Simulation results show that the fairness of the proposed STAR-RIS-NOMA assisted ISAC system outperforms the conventional RIS-NOMA assisted ISAC system and the conventional RIS and orthogonal multiple access (RIS-OMA) assisted ISAC system.

In this paper, a correlation matching pursuit (CMP) procedure is proposed to handle the vector perturbation (VP) problem for nonlinear precoding (NLP) in downlink multiuser multi-antenna (i.e., MU-MIMO) systems. Basically, CMP consists of two sub-procedures, i.e., correlation matching and correlation pursuit. The former takes the charge of direct calculation of the perturbation integers through a set of established convex optimization problems; the latter is responsible for selecting the perturbation vector for updating the precoded vector. Iterative execution of both procedures renders CMP being modelled as a searching tree which consists of multiple nodes and paths. The sequence correlation between the precoded vector and perturbation vectors is revealed crucial to the performance optimality and thus used as metric of the path search. Given that the single-path search is suboptimal, we propose multi-path schemes to enable exploitation on the path diversity and thus further improve the performance. Complexity analysis and computer simulations demonstrate that CMP-based NLP algorithms serve as low-cost VP solutions with significantly lowered processing latency and meanwhile, comparable performance compared to prior arts.

Signal detection in large multiple-input multiple-output (large-MIMO) systems presents greater challenges compared to conventional massive-MIMO for two primary reasons. First, large-MIMO systems lack favorable propagation conditions as they do not require a substantially greater number of service antennas relative to user antennas. Second, the wireless channel may exhibit spatial non-stationarity when an extremely large aperture array (ELAA) is deployed in a large-MIMO system. In this paper, we propose a scalable iterative large-MIMO detector named ANPID, which simultaneously delivers 1) close to maximum-likelihood detection performance, 2) low computational-complexity (i.e., square-order of transmit antennas), 3) fast convergence, and 4) robustness to the spatial non-stationarity in ELAA channels. ANPID incorporates a damping demodulation step into stationary iterative (SI) methods and alternates between two distinct demodulated SI methods. Simulation results demonstrate that ANPID fulfills all the four features concurrently and outperforms existing low-complexity MIMO detectors, especially in highly-loaded large-MIMO systems.

Service Function Chaining (SFC) is an emerging network technology based on network function virtualisation (NFV), which facilitates in-network data processing during traffic steering. SFC optimisations can be broadly categorised into centralised and distributed solutions, each with advantages and disadvantages. In this paper, we propose a flexible SFC optimisation scheme based on network cluster partitioning, where cluster-based information sharing and decision-making are applied for building optimised SFCs in real-time. Such a scheme can be flexibly adapted based on cluster numbers and sizes. The key challenge is how necessary intra-cluster information is shared across clusters for performance optimisation while retaining privacy and low signalling complexity. According to our simulation experiments based on real network topologies, our proposed solution outperforms the state-of-the-art benchmark by about 20%.

This paper examines the uplink transmission of a single-antenna handsheld user to a cluster of satellites, with a focus on utilizing the inter-satellite links to enable cooperative signal detection. Two cases are studied: one with full CSI and the other with partial CSI between satellites. The two cases are compared in terms of capacity, overhead, and bit error rate. Additionally, the impact of channel estimation error is analyzed in both designs, and robust detection techniques are proposed to handle channel uncertainty up to a certain level. The performance of each case is demonstrated, and a comparison is made with conventional satellite communication schemes where only one satellite can connect to a user. The results of our study reveal that the proposed constellation with a total of 3168 satellites in orbit can enable a capacity of 800 Mbits/sec through cooperation of 12 satellites with and occupied bandwidth of 500 MHz. In contrast, conventional satellite communication approaches with the same system parameters yield a significantly lower capacity of less than 150 Mbits/sec for the nearest satellite.

The cumulative distribution function (CDF) of a non-central$\chi^{2}$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For adaptive transmitters, it is important but mathematically challenging to determine the outage threshold for an extreme target outage probability (e.g., 10−5or less). This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most effective lower bound. The Cher-LB is then employed to predict the multi-antenna transmitter beamforming-gain in ultra-reliable and low-latency communication, concerning the first-order Markov time-varying channel. It is exhibited that, with the proposed Cher-LB, pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.

This paper presents an innovative Intrusion Detection System (IDS) architecture using Deep Reinforcement Learning (DRL). To accomplish this, we started by analysing the DRL issue for IoT devices, followed by designing intruder attacks using Label Flipping Attack (LFA). We propose an artificial intelligence DRL model to imitate IoT attack detection, along with two defence strategies: Label-based Semi-supervised Defence (LSD) and Clustering-based Semi-supervised Defence (CSD). Finally, we provide the evaluation results of the adaptive attack and defence models on multiple IoT scenarios with the NSL-KDD, IoT-23, and NBaIoT datasets. The research proves that DRL functions effectively with dynamically produced traffic in contrast to existing conventional techniques.

Integrating information and communication technologies into the power generation, transmission and distribution system provides a new concept called Smart Grid (SG). The wide variety of devices connected to the SG communication infrastructure generates heterogeneous data with different Quality of Service (QoS) requirements and communication technologies. An intrusion Detection System (IDS) is a surveillance system monitoring the traffic flow over the network, seeking any abnormal behaviour to detect possible intrusions or attacks against the SG system. Distributed fashion of power and data in SG leads to an increase in the complexity of analysing the QoS and user requirements. Thus, we require a Big Data-aware distributed IDS dealing with the malicious behaviour of the network. Motivated by this, we design a distributed IDS dealing with anomaly big data and impose the proper defence algorithm to alert the SG. This paper proposes a new smart meter (SM) architecture, including a distributed IDS model (SM-IDS). Secondly, we implement SM-IDS using supervised ML algorithms. Finally, a distributed IDS model is introduced using federated learning. Numerical results approve that Neighbourhood Area Network IDS (NAN-IDS) can help decrease smart meters' energy and resource consumption. Thus, SM-IDS achieves an accuracy of 84.31% with a detection rate of 74.69%. Also, NAN-IDS provides an accuracy of 87.40% and a detection rate of 86.73%.

An important issue for cellular network operators is how to maintain radio coverage so that any issues can be addressed before they impact user services. This is particularly important in dense small cell network deployment scenarios such as vehicular networks. Antenna electrical tilt is a key factor in this, as unintended deviations from the planned value can adversely affect coverage and service reliability. We propose a novel method to detect antenna tilt anomalies using existing data sources without the need for additional hardware to be deployed in the radio access network. Our approach goes beyond previous techniques by using federated unsupervised learning based on polar coordinates, together with a geometrical transformation to normalise data across multiple sites. By using this approach to combine scarce training data from multiple cells, we can achieve detection accuracy in excess of 95% in a way that minimises training data size as well as computing power and memory usage.

The design of iterative linear precoding is recently challenged by extremely large aperture array (ELAA) systems, where conventional preconditioning techniques could hardly improve the channel condition. In this paper, it is proposed to regularize the extreme singular values to improve the channel condition by deducting a rank-one matrix from the Wishart matrix of the channel. Our analysis proves the feasibility to reduce the largest singular value or to increase multiple small singular values with a rank-one matrix when the singular value decomposition of the channel is available. Knowing the feasibility, we propose a low-complexity approach where an approximation of the regularization matrix can be obtained based on the statistical property of the channel. It is demonstrated, through simulation results, that the proposed low-complexity approach significantly outperforms current preconditioning techniques in terms of reduced iteration number for more than 10% in both ELAA systems as well as symmetric multi-antenna (i.e., MIMO) systems when the channel is i.i.d. Rayleigh fading.

This paper investigates a learning-based approach autonomously and jointly optimizing the trajectory of unmanned aerial vehicle (UAV), phase shifts of reconfigurable intelligent surfaces (RIS), and aggregation weights for federated learning (FL) in wireless communications, forming an autonomous RIS-assisted UAV-enabled network. The proposed network considers practical RIS reflection models and FL transmission errors in wireless communications. To optimize the RIS phase shifts, a double cascade correlation network (DCCN) is introduced. Additionally, the deep deterministic policy gradient (DDPG) algorithm is employed to address the optimization problem of UAV trajectory and FL aggregation weights based on the results obtained from DCCN. Simulation results demonstrate the substantial improvement in FL performance within the autonomous RIS-assisted UAV-enabled network setting achieved by the proposed algorithms compared to the benchmarks.

We focus on the signal detection for large quasi-symmetric (LQS) multiple-input multiple-output (MIMO) systems, where the numbers of both service (M) and user (N) antennas are large and N/M → 1. It is challenging to achieve maximum-likelihood detection (MLD) performance with square-order complexity due to the ill-conditioned channel matrix. In the emerging MIMO paradigm termed with an extremely large aperture array, the channel matrix can be more ill-conditioned due to spatial non-stationarity. In this paper, projected-Jacobi (PJ) is proposed for signal detection in (non-) stationary LQS-MIMO systems. It is theoretically and empirically demonstrated that PJ can achieve MLD performance, even when N/M = 1. Moreover, PJ has square-order complexity of N and supports parallel computation. The main idea of PJ is to add a projection step and to set a (quasi-) orthogonal initialization for the classical Jacobi iteration. Moreover, the symbol error rate (SER) of PJ is mathematically derived and it is tight to the simulation results.

Multi-user multiple-input, multiple-output (MU-MIMO) designs can substantially increase the achievable throughput and connectivity capabilities of wireless systems. However, existing MU-MIMO deployments typically employ linear processing that, despite its practical benefits, can leave capacity and connectivity gains unexploited. On the other hand, traditional non-linear processing solutions (e.g., sphere decoders) promise improved throughput and connectivity capabilities, but can be impractical in terms of processing complexity and latency, and with questionable practical benefits that have not been validated in actual system realizations. At the same time, emerging new Open Radio Access Network (Open-RAN) designs call for physical layer (PHY) processing solutions that are also practical in terms of realization, even when implemented purely on software. This work demonstrates the gains that our highly efficient, massively parallelizable, non-linear processing (MPNL) framework can provide, both in the uplink and downlink, when running in real-time and over-the-air, using our new 5G-New Radio (5G-NR) and Open-RAN compliant, software-based PHY. We showcase that our MPNL framework can provide substantial throughput and connectivity gains, compared to traditional, linear approaches, including increased throughput, the ability to halve the number of base-station antennas without any performance loss compared to linear approaches, as well as the ability to support a much larger number of users than base-station antennas, without the need for any traditional Non-Orthogonal Multiple Access (NOMA) techniques, and with overloading factors that can be up to 300%.

With the huge number of broadband users, automated network management becomes of huge interest to service providers. A major challenge is automated monitoring of user Quality of Experience (QoE), where Artificial Intelligence (AI) and Machine Learning (ML) models provide powerful tools to predict user QoE from basic protocol indicators such as Round Trip Time (RTT), retransmission rate, etc. In this paper, we introduce an effective feature selection method along with the corresponding classification algorithms to address this challenge. The simulation results show a prediction accuracy of 78% on the benchmark ITU ML5G-PS-012 dataset, improving 11% over the state-of-the-art result whilst reducing the model complexity at the same time. Moreover, we show that the local area network round trip time (LAN RTT) value during daytime and midweek plays the most prominent factor affecting the user QoE.

High mobility scenarios may be typical for different applications such as low earth orbit (LEO) satellite and vehicle-to-everything (V2X) communications. A standardized approach to dealing with high mobility scenarios is using flexible sub-frame structures including a higher pilot density in the time domain, which leads to reduced spectrum efficiency. We propose a supplementary algorithm to improve multiple antenna receiver performance in high mobility scenarios for the given sub-frame structure compared to the conventional 3GPP pilot and data based interference rejection receivers. The main feature of high mobility (non-stationary) scenarios is that different symbols in the desired signal sub-frame may be received under different propagation and/or interference conditions. Recently, we have addressed a non-stationary interference rejection scenario in slowly varying propagation environment with asynchronous (intermittent) interference by means of developing an interference rejection combining algorithm, where the pilot based estimate of the interference plus noise covariance matrix is regularized by the data based estimate of the covariance matrix. In this paper, we: 1) extend the data regularized solution to the general high mobility scenarios, and 2) demonstrate its efficiency compared to the conventional pilot and data based receivers for different sub-frame formats in the uplink transmissions in the LEO satellite scenario with high residual Doppler frequency with and without hardware impairments.

In this paper, we investigate the uplink transmission of a single-antenna handheld user to a cluster of satellites. Taking advantage of the inter-satellite links, the satellites can cooperate which each other to jointly detect the received signal. We examine a scenario in which the satellite cluster lacks access to the instantaneous channel state information (CSI). Thus, using only statistical CSI, we design the joint detection by minimizing the mean square error (MSE). We calculate the ergodic capacity using the properties of the Wishart matrix, and then for low-SNR scenarios we provide a closed-form approximation for it. Our numerical results demonstrate the effectiveness of the detection scheme, along with the proximity of the approximation to the actual ergodic capacity. Considering a mega constellation with 3168 satellites in low earth orbit (LEO), we show that a capacity of more than 10 MB/sec can be achieved even if only the statistical CSI is known to the receiver, and a capacity of up to 38 MB/sec can be achieved with perfect instantaneous CSI.

In the next generation of communication networks (i.e. 6G), metamaterial-based antenna designs, such as Reconfigurable Intelligent Surface (RIS), will be critical for improving wireless communication systems. This paper investigates the ergodic capacity of RIS-aided multiple input multiple outputs (MIMO) systems in the presence of a direct link between the transmitter and receiver. We obtain an exact expression for the ergodic capacity of the cooperative MIMO-RIS systems (along with the corresponding probability density function of the cooperative MIMO-RIS channel) assuming that the receiver is capable of treating the RIS and direct link contributions in the received signal separately. Furthermore, we demonstrate that in the absence of this capability, the resulting formula is a tight upper bound that becomes increasingly tighter with greater numbers of RIS elements. In addition, we pose a simplified capacity expression for large numbers of RIS elements, which provides further insights into the behaviour of the cooperative MIMO-RIS capacity. To gain more insights, we also include a high SNR approximation. Our simulation results confirm the correctness of our expressions and illustrate how the SNR and the number of RIS elements impact the ergodic capacity.

Microgrids are industrial technologies that can provide energy resources for the Internet of Things (IoT) demands in smart grids. Hybrid microgrids supply quality power to the IoT devices and ensure high resiliency in supply and demand for PV-based grid-tied microgrids. In this system, the usage of predictive energy management systems (EMS) is essential to dispatch power from different resources, while the battery energy storage system (BESS) is feeding the loads. In this article, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is calledSIEMS. The main role of theSIEMSis to maintain the SOC at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this article is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g., smart grid and microgrids, in order to produce perturbation for training data. To secure the microgrid’sSIEMS, we proposetwoDefence algorithms based on defensive distillation and adversarial training strategies for the first time in EMSs. We apply and evaluate these benchmark adversarial attack and Defence methods against the proposed machine learning models to increase the robustness of the models in the system against adversarial attacks.

Live holographic teleportation is an emerging media application that allows Internet users to communicate in a fully immersive environment. One distinguishing feature of such an application is the ability to teleport multiple objects from different network locations into the receiver's field of view at the same time, mimicking the effect of group-based communications in a common physical space. In this case, live teleportation frames originated from different sources must be precisely synchronised at the receiver side to ensure user experiences with eliminated perception of motion misalignment effect. For the very first time in the literature, we quantify the motion misalignment between remote sources with different network contexts in order to justify the necessity of such frame synchronisation operations. Based on this motivation, we propose HoloSync, a novel edge-computing-based scheme capable of achieving controllable frame synchronisation performances for multi-source holographic teleportation applications. We carry out systematic experiments on a real system with the HoloSync scheme in terms of frame synchronisation performances in specific network scenarios, and their sensitivity to different control parameters.

Software-defined networking (SDN) has been widely adopted in different networks, such as datacenter and service providers. The SDN controller has the entire network view and is responsible for managing it. To obtain such a view of the network, the controller employs link discovery protocols, which are vulnerable to attacks such as link fabrication attacks (LFAs). TopoGuard and TopoGuard + are two major systems detecting LFAs. This paper introduces a link latency attack (LLA) that can bypass the defence mechanism of both systems. LLA can poison the view of the SDN controller from the network topology and causes outages, resulting in poor quality of service (QoS) or quality of experience (QoE). To mitigate this, we develop two machine learning-based defence systems, namely machine learning-based link guard (MLLG) and real-time link verification (RLV), to preserve the required defence for LLA. The MLLG works when the network topology rarely updates, while RLV can support frequent updates. Furthermore, RLV trains itself over a link latency dataset (LLD)– including latency data of fabricated and normal links– that is captured from the ongoing packets in the network. It also implements outlier detection techniques to identify a dynamic threshold for link latency. We test both systems on different scenarios using Mininet and show that they achieve reasonable results compared with current defence algorithms. Specifically, RLV presents the highest detection performance (F1-score) to 70% at less than 0.2% false-positive rate. The system also supports the robustness features when the attack rates vary from 3% to 7% in our simulated network.

The integrated terrestrial satellite networks (ITSNs) have been deemed as a promising solution to ubiquitous Internet access anytime and anywhere. In this paper, we investigate the spectrum sharing problem in ITSN, in particular focusing on the downlink transmission of the satellite network, which adopts the framing structure for the satellite users (SUs). We model the interference from both terrestrial downlink and uplink transmissions to SUs according to the beamforming techniques. For the terrestrial downlink transmission, we assume that terrestrial users (TUs) are served cooperatively by multiple small base stations (SBSs) via joint beamforming, while the virtual multiple access channel (VMAC) scheme is adopted for the terrestrial uplink transmission. We propose the optimization framework by jointly considering the terrestrial beamformer design and satellite user scheduling to maximize the sum rate of all users. The optimization problems are decomposed into three sub-problems: satellite user scheduling, terrestrial beamformer design, and time slot allocation, which are solved by deep clustering, second-order cone programming (SOCP) (or fractional programming (FP)), and linear programming, respectively. Then, an alternating iterative algorithm is designed to obtain the optimal solution. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm in multiple cases.

Intelligent reflecting surface (IRS) is a novel technology to manipulate wireless propagation channels via smart and controllable signal reflection. In this paper, we investigate an IRS-aided integrated terrestrial-satellite network (ITSN) system, where the IRS is deployed to assist the co-existing transmissions of the terrestrial small base stations (SBSs) and the satellite. Because of the spectrum sharing in the ITSN, the interference between the two systems should be carefully mitigated. Our objective is to maximize the weighted sum rate (WSR) of all users by jointly optimizing the frame-based coordinated transmit beamforming vectors at the SBSs, the phase shift matrix at the IRS, and the frame user scheduling, subject to SBSs’ individual power constraints and unit modulus constraints of phase shifters. To this end, we first adopt the agglomerative hierarchical clustering (AHC) method to schedule the satellite users to different frames. Then the block coordinate descent (BCD) algorithm is proposed, which alternately optimizes the transmit beamforming vectors and the reflective phase shift matrix. In particular, the optimal transmit beamforming vectors are obtained via the fractional programming (FP) technique. Meanwhile, two efficient algorithms, i.e., the Riemannian manifold (RM) and the successive convex approximation (SCA), are proposed for the phase shift optimization. Finally, simulation results are provided to demonstrate the performance gain of our schemes over other benchmark schemes.

With exploiting massive spectrum resources, millimeter wave (mmWave) communications significantly improve the offloading capability for future mobile edge computing (MEC) techniques, which however is constrained by blockage problem in dynamic environments. In this paper, we study the resource allocation problem for the conceived mmWave MEC system with dynamic offloading process, in which the UEs are characterized by being mobile and having the imperfect knowledge of the offloading tasks coming. By introducing the multi-objective Markov decision process (MOMDP), the resource allocation problem is modeled by simultaneously minimizing the delay and energy consumption, where jointly considering the multi-beam assignment (mBA) and beamwidth and power optimization (BPO). To tackle this problem, we innovatively propose a matching-aided-learning (MaL) resource allocation scheme, with the aid of a learnable weight based attention mechanism (LW-AM) for adapting the dynamic offloading process. In particular, our MaL scheme includes many-to-one matching (M2O-M) based mBA algorithm and deep deterministic policy gradient (DDPG) based BPO algorithm, which are executed iteratively and converge with relatively low number of iterations. The simulation results show the practical value of the proposed MaL, which can approach the performance of benchmark scheme with perfect knowledge of offloading tasks.

In this paper, a novel nonlinear precoding (NLP) technique, namely constellation-oriented perturbation (COP), is proposed to tackle the scalability problem inherent in conventional NLP techniques. The basic concept of COP is to apply vector perturbation (VP) in the constellation domain instead of symbol domain; as often used in conventional techniques. By this means, the computational complexity of COP is made independent to the size of multi-antenna (i.e., MIMO) networks. Instead, it is related to the size of symbol constellation. Through widely linear transform, it is shown that COP has its complexity flexibly scalable in the constellation domain to achieve a good complexity-performance tradeoff. Our computer simulations show that COP can offer very comparable performance with the optimum VP in small MIMO systems. Moreover, it significantly outperforms current sub-optimum VP approaches (such as degree-2 VP) in large MIMO whilst maintaining much lower computational complexity.

In vehicle-to-infrastructure (V2I) networks, a cluster of multi-antenna access points (APs) can collaboratively conduct transmitter beamforming to provide data services (e.g., eMBB or URLLC). The collaboration between APs effectively forms a networked linear antenna-array with extra-large aperture (i.e., network-ELAA), where the wireless channel exhibits spatial non-stationarity. Major contribution of this work lies in the analysis of beamforming gain and radio coverage for network-ELAA non-stationary Rician channels considering the AP clustering. Assuming that: 1) the total transmit-power is fixed and evenly distributed over APs, 2) the beam is formed only based on the line-of-sight (LoS) path, it is found that the beamforming gain is concave to the cluster size. The optimum size of the AP cluster varies with respect to the user’s location, channel uncertainty as well as data services. A user located farther from the ELAA requires a larger cluster size. URLLC is more sensitive to the channel uncertainty when comparing to eMBB, thus requiring a larger cluster size to mitigate the channel fading effect and extend the coverage. Finally, it is shown that the network-ELAA can offer significant coverage extension (50% or more in most of cases) when comparing with the single-AP scenario.

Distributed mobility management (DMM) solution is proposed to address the downsides of centralized mobility management protocols. The standard DMM is proposed for flat architectures and always selects the anchor point from access layer. Numerical analysis is used in this paper to show that dynamic anchor point selection can improve the performance of standard DMM in terms of packet signalling and delivery cost. In next step, an SDN-based DMM solution that we refer to as SD-DMM is presented to provide dynamic anchor point selection for hierarchical mobile network architecture. In SD-DMM, the anchor point is dynamically selected for each mobile node by a virtual function implemented as an application on top of the SDN controller which has a global view of the network. The main advantages of SD-DMM is to decrease packet delivery cost.

Wireless interfaces, remote control schemes, and increased autonomy have raised the attacks surface of vehicular networks. As powerful monitoring entities, intrusion detection systems (IDS) must be updated and customised to respond to emerging networks' requirements. As server-based monitoring schemes were prone to significant privacy concerns, new privacy constrained learning methods such as federated learning (FL) have received considerable attention in designing IDS. However, to alleviate the efficiency and enhance the scalability of the original FL, this paper proposes a novel collaborative hierarchical federated IDS, named CHFL for the vehicular network. In the CHFL model, a group of vehicles assisted by vehicle-to-everything (V2X) communication technologies can exchange intrusion detection information collaboratively in a private format. Each group nominates a leader, and the leading vehicle serves as the intermediate in the second level detection system of the hierarchical federated model. The leader communicates directly with the server to transmit and receive model updates of its nearby end vehicles. By reducing the number of direct communications to the server, our proposed system reduces network uplink traffic and queuing-processing latency. In addition, CHFL improved the prediction loss and the accuracy of the whole system. We are achieving an accuracy of 99.10% compared with 97.01 % accuracy of the original FL.

Open radio access network (Open-RAN) is becoming a key component of cellular networks, and therefore optimizing its architecture is vital. The Open-RAN is a distributed architecture that lets the virtualized networking functions be split between Distributed Units (DU) and Centralized Units (CUs); as a result, there is a wide range of design options. We propose an optimization problem to choose the split points. The objective is to balance the load across CUs as well as midhaul links by considering delay requirements. The resulting formulation is an NP-hard problem that is solved with a novel heuristic algorithm. Performance evaluation shows that the gap between optimal and heuristic solutions does not exceed 2%. An in-depth analysis of different centralization levels shows that using multi-CUs could reduce the total bandwidth usage by up to 20%. Moreover, multipath routing can improve the result of load balancing between midhaul links while increasing bandwidth usage.

Concerning ultra-reliable low-latency communication (URLLC) for the downlink operating in the frequency-division multiple-access with random channel assignment, a lightweight power allocation approach is proposed to maximize the number of URLLC users subject to transmit-power and individual user-reliability constraints. Provided perfect channel-state-information at the transmitter (CSIT), the proposed approach is proven to ensure maximized URLLC users. Assuming imperfect CSIT, the proposed approach still aims to maximize the URLLC users without compromising the individual user reliability by using a pessimistic evaluation of the channel gain. It is demonstrated, through numerical results, that the proposed approach can significantly improve the user capacity and the transmit-power efficiency in Rayleigh fading channels. With imperfect CSIT, the proposed approach can still provide remarkable user capacity at limited cost of transmit-power efficiency.

Network coverage is an increasing concern for the Quality of Service (QoS) targets of new mobile technologies. New solutions designed to fulfill the requirements of the existing fifthgeneration (5G) and upcoming sixth-generation (6G) emerging scenarios are based on deploying a high number of network access points (APs), which tend to considerably degrade coverage and cell-edge performance due to added interference and increase the energy consumption of cellular systems. In this paper, we present new results on our recently proposed novel concept of cell-sweeping that aims to minimize the coverage dead-spots and improve cell-edge user performance. More specifically, the concept is explored further in this paper analyzing the impact of different cell-sweeping configurations and evaluating the potential benefits towards achieving energy efficiency. By means of system level computer simulations, it is shown that cell-sweeping provides energy savings of 11% and 26.5% for a similar average and cell-edge user throughput performance, respectively, when compared to the conventional static cell deployment in a typical urban macro cell scenario.

This paper proposes a secure transmission in reconfigurable intelligent surfaces (RIS) aided non-terrestrial cooperative networks (NTCN), where the practical phase-dependent model is considered in which the RIS reflection amplitudes change with the corresponding discrete phase shifts. Moreover, we employ a full-duplex transmission scheme at the relay nodes to reduce the long-range signal loss and improve the security between the satellite and the relay node. To solve the complex nonconvex optimization problem of the joint RIS reflection coefficient and relay selection optimization, we propose the deep cascade correlation learning (DCCL) algorithm to enhance optimization efficiency. Simulation results show that the proposed DCCL-based method significantly improves the secrecy capacity compared to the random relay selection and RIS coefficient methods.

Good network coverage is an important element of Quality of Service (QoS) provision that mobile cellular operators aim to provide. The established requirements for the existing Fifth Generation (5G) and the emerging scenarios for upcoming Sixth Generation (6G) cellular communication technologies highly depend on the coverage quality that the network is able to provide. In addition, some proposed 5G solutions such as densification, are complex, costly, and tend to degrade network coverage due to increased interference which is critical for the cell-edge performance. In this direction, we present a novel concept of cell-sweeping for coverage enhancement in cellular networks. One of the main objectives behind this mechanism relies on overcoming the cell-edge problem which directly translates into better network coverage. In sequence, the concept operation is introduced and compared to the conventional static cell scenarios. These comparisons target mostly the benefits at the cell-edge locations. Additionally, the use of schedulers that take advantage of the sweeping system is expected to extend the cell-edge benefits to the entire network. This is observed when deploying cell-sweeping with the Proportional Fair (PF) scheduler. A 5th-percentile improvement of 125% and an average throughput increase of 35% were obtained through system level simulations. The preliminary results presented in this paper suggest that cell-sweeping can be adopted as an emerging technology for future Radio Access Network (RAN) deployments.

A cell-free massive multiple-input multiple-output (MIMO) uplink is investigated in this paper. We address a power allocation design problem that considers two conflicting metrics, namely the sum rate and fairness. Different weights are allocated to the sum rate and fairness of the system, based on the requirements of the mobile operator. The knowledge of the channel statistics is exploited to optimize power allocation. We propose to employ large scale-fading (LSF) coefficients as the input of a twin delayed deep deterministic policy gradient (TD3). This enables us to solve the non-convex sum rate fairness trade-off optimization problem efficiently. Then, we exploit a use-and-then-forget (UatF) technique, which provides a closed-form expression for the achievable rate. The sum rate fairness trade-off optimization problem is subsequently solved through a sequential convex approximation (SCA) technique. Numerical results demonstrate that the proposed algorithms outperform conventional power control algorithms in terms of both the sum rate and minimum user rate. Furthermore, the TD3-based approach can increase the median of sum rate by 16%-46% and the median of minimum user rate by 11%-60% compared to the proposed SCA-based technique. Finally, we investigate the complexity and convergence of the proposed scheme.

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

Toward 6G networks, such as virtual reality (VR) applications, Industry 4.0, and automated driving, demand mobile-edge computing (MEC) techniques to offload computing tasks to nearby servers, which, however, causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, enabling low latency and high throughput. For this sake, this article investigates the resource management for the offload transmission of the mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computing-oriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation, and power allocation is formulated as a multiobjective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using the$\epsilon $-constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that the gap between Pareto front generated by the three-stage iterative resource allocation algorithm and the real Pareto front is less than 0.16%. Furthermore, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-II, while significantly outperforms the other traditional schemes.

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes.

The vehicular ad hoc network (VANET) is a platform for exchanging information between vehicles and everything to enhance driver’s driving experience and improve traffic conditions. The reputation system plays an essential role in judging whether to communicate with the target vehicle based on other vehicles’ feedback. However, existing reputation systems ignore the privacy protection of feedback providers. Additionally, traditional VANET based on wireless sensor networks (WSNs) has limited power, storage, and processing capabilities, which cannot meet the real-world demands in a practical VANET deployment. Thus, we attempt to integrate cloud computing with VANET and proposes a privacy-preserving protocol of vehicle feedback (PPVF) for cloud-assisted VANET. In cloud-assisted VANET, we integrate homomorphic encryption and data aggregation technology to design the scheme PPVF, in which with the assistance of the roadside units (RSU), cloud service provider (CSP) obtains the total number of vehicles with the corresponding parameters in the feedback for reputation calculation without violating individual feedback privacy. Simulation results and security analysis confirm that PPVF achieves effective privacy protection for vehicle feedback with acceptable computational and communication burden. Besides, the RSU is capable of handling 1999 messages for every$300ms$, so as the number of vehicles in the communication domain increases, the PPVF has a lower message loss rate.

Among the existing satellite types, Low Earth Orbit (LEO) satellites provide short round-trip delays and are becoming increasingly important. Due to its low orbital profile, the LEO satellites can provide high-speed, low-latency and no dead zone network services for ground users. However, as the number of satellites continues to increase, frequency bands as non-renewable resources will seriously restrict the future development of the Space-Earth integration network. In this paper, a flexible spectrum sharing and cooperative service method is proposed to address the co-linear interference issue caused by LEO satellites while passing through the coverage area of the GEO beam and allows the LEO satellites to provide services for multiple LEO ground users. In our proposed scheme, through continuous power allocation optimization, we ensure that the service of LEO satellites will not reduce the service quality of the GEO beam. At by taking full advantage of the cooperation between LEO satellites, the quality of their service can be significantly improved. Simulation results show that our proposed scheme converges quickly, the transmission efficiency and the stability of the system can all be guaranteed.

Software-Defined Networking (SDN) has found applications in different domains, including wired- and wireless networks. The SDN controller has a global view of the network topology, which is vulnerable to topology poisoning attacks, e.g., link fabrication and host-location hijacking. The adversaries can leverage these attacks to monitor the flows or drop them. However, current defence systems such as TopoGuard and TopoGuard+ can detect such attacks. In this paper, we introduce the Link Latency Attack (LLA) that can successfully bypass the systems' defence mechanisms above. In LLA, the adversary can add a fake link into the network and corrupt the controller's view from the network topology. This can be accomplished by compromising the end hosts without the need to attack the SDN-enabled switches. We develop a Machine Learning-based Link Guard (MLLG) system to provide the required defence for LLA. We test the performance of our system using an emulated network on Mininet, and the obtained results show an accuracy of 98.22% in detecting the attack. Interestingly, MLLG improves 16% the accuracy of TopoGuard+.

This paper investigates a multiple intelligent reflecting surfaces (IRSs) aided integrated terrestrial-satellite network (ITSN), where the IRSs are deployed to cooperatively assist the low channel gain users in the co-existing transmission system. In such a network, the coordinated beamforming and frame based transmission scheme are considered for the terrestrial network and the satellite network, respectively. We aim at maximizing the weighted sum rate (WSR) of all users by jointly designing the frame based beamforming at the small base stations (SBSs) and the phase shifts at the IRSs, subject to the individual maximum SBS's transmit power constraints and the IRSs' reflection constraints. This non-convex problem is firstly decomposed via fractional programming (FP) technique in the objective function, then transmit beamforming vectors and reflective phase shifts matrix are optimized alternatingly. A block coordinate descent (BCD) method is proposed to obtain the stationary solution. Simulation results verify the effectiveness of the proposed algorithm compared with different benchmark schemes.

In this paper, a novel spatially non-stationary channel model is proposed for link-level computer simulations of massive multiple-input multiple-output (mMIMO) with extremely large aperture array (ELAA). The proposed channel model allows a mix of non-line-of-sight (NLoS) and LoS links between a user and service antennas. The NLoS/LoS state of each link is characterized by a binary random variable, which obeys a correlated Bernoulli distribution. The correlation is described in the form of an exponentially decaying window. In addition, the proposed model incorporates shadowing effects which are non-identical for NLoS and LoS states. It is demonstrated, through computer emulation, that the proposed model can capture almost all spatially non-stationary fading behaviors of the ELAA-mMIMO channel. Moreover, it has a low implementational complexity. With the proposed channel model, Monte-Carlo simulations are carried out to evaluate the channel capacity of ELAA-mMIMO. It is shown that the ELAA-mMIMO channel capacity has considerably different stochastic characteristics from the conventional mMIMO due to the presence of channel spatial non-stationarity.

Integrating Low Earth Orbit (LEO) satellites with terrestrial network infrastructures to support ubiquitous Internet service coverage has recently received increasing research momentum. One distinct challenge is the frequent topology change caused by the constellation behaviour of LEO satellites. In the context of software defined networking (SDN), the controller function that is originally required to control the conventional data plane fulfilled by terrestrial SDN switches will need to expand its responsibility to cover their counterparts in the space, namely LEO satellites that are used for data forwarding. As such, seamless integration of the fixed control plane on the ground and the mobile data plane fulfilled by constellation LEO satellites will become a distinct challenge. In this paper, we propose the Virtual Data-Plane Addressing (VDPA) Scheme by leveraging IP addresses to represent virtual switches at the fixed space locations which are periodically instantiated by the nested LEO satellites traversing them in a predictable manner. With such a scheme the changing data-plane network topology incurred by LEO satellite constellation can be made completely agnostic to the control plane on the ground, thus enabling a native approach to supporting seamless communication between the two planes. Our testbed-based experiment results prove the technical feasibility of the proposed VDPA-based flow rule manipulation mechanism in terms of data plane performance.

In this paper, we investigate the downlink transmission in the integrated terrestrial satellite networks, whereby the same spectrum is shared between two systems, and thus interference to each other should be carefully mitigated. We address this challenging issue by unifying the terrestrial beamformer design and satellite user scheduling into the same optimization framework. This nontrivial problem is decomposed into two subproblems, one deals with the terrestrial beamformer design to control the interference from the terrestrial base stations to the satellite users, the other tries to optimize the scheduling of the satellite users following the framing structure the DVB-S2X standards for satellite communication systems. A deep clustering user scheduling scheme is developed to group suitable satellite users to the same frame using the channel state information as the input feature. Finally, a joint iterative algorithm is designed to maximize the sum rate of all users in the integrated systems. We conduct extensive simulation results to show the effectiveness of the proposed scheme.

Software-defined networking (SDN) enables centralized control of a network of programmable switches by dynamically updating flow rules. This paves the way for dynamic and autonomous control of the network. In order to be able to apply a suitable set of policies to the correct set of traffic flows, SDN needs input from traffic classification mechanisms. Today, there is a variety of classification algorithms in machine learning. However, recent studies have found that using an arbitrary algorithm does not necessarily provide the best classification outcome on a dataset, and therefore a framework called ensemble which combines individual algorithms to improve classification results has gained attraction. In this paper, we propose the application of the ensemble algorithm as a machine learning pre-processing tool, which classifies ingress network traffic for SDN to pick the right set of traffic policies. Performance evaluation results show that this ensemble classifier can achieve robust performance in all tested traffic types.

In a critical infrastructure such as Smart Grid (SG), providing security of the system and privacy of consumers are significant challenges to be considered. The SG developers adopt Machine Learning (ML) algorithms within the Intrusion Detection System (IDS) to monitor traffic data and network performance. This visibility safeguards the SG from possible intrusions or attacks that may trigger the system. However, it requires access to residents’ consumption information which is a severe threat to their privacy. In this paper, we present a novel method to detect abnormalities on a large scale SG while preserving the privacy of users. We design a Federated IDS (FIDS) architecture using Federated Learning (FL) in a 5G environment for the SG metering network. In this way, we design Federated Deep Neural Network (FDNN) model that protects customers’ information and provides supervisory management for the whole energy distribution network. Simulation results for a real-time dataset demonstrate the reasonable improvement of the proposed FDNN model compared with the state-of-the-art algorithms. The FDNN achieves approximately 99.5% accuracy, 99.5% precision/recall, and 99.5% f1-score when comparing with classification algorithms.

Distributed Mobility Management (DMM) protocol is proposed to address the shortcomings of centralized mobility management protocols. In DMM, unlike centralized protocols, flows can be routed optimally in the network by dynamic IP address allocation, which can yield lower signaling and packet delivery costs. In this paper, we introduce an SDN based mobility management service with multiple controllers called Hierarchical Software Defined Distributed Mobility Management (HSD-DMM). In contrast to standard DMM which is proposed for flat architectures, HSD-DMM uses a dynamic anchor point selection method for each flow in a hierarchical mobile network architecture. The main condition in selecting an anchor point is packet delivery cost reduction based on collaboration of multiple controllers responsible for different tiers of the hierarchy. Numerical analysis results reveal that HSD-DMM can decrease signaling and packet delivery cost compared to an SDN based standard DMM solution.

Live holographic teleportation is an emerging media application that allows Internet users to communicate with each other in a fully immersive manner. One distinct feature of such an application is the capability of simultaneously teleporting multiple objects from different network locations to the receiver’s field of view, mimicking the effect of group-based communications in a common physical space. In this case, teleportation frames from individual sources need to be stringently synchronized in order to assure user Quality of Experiences (QoE) in terms of avoiding the perception of motion misalignment at the receiver side. In this paper, we carry out systematic performance evaluations on how different Internet path conditions may affect the teleportation frame synchronisation performances. Based on this, we present a lightweight, edge-computing based scheme that is able to achieve controllable frame synchronisation operations for multi-source based teleportation applications at the Internet scale.

Quantization is the characterization of analogue-to-digital converters (ADC) in massive MIMO systems. The design of quantization function or quantization thresholds is found to relate to quantization step, which is the factor that adapts with the changing of transmit power and noise variance. With the objective of utilizing low-resolution ADC is reducing the cost of massive MIMO, we propose an idea as if it is necessary to have adaptive-threshold quantization function. It is found that when maximum-likelihood (ML) is employed as the detection method, having quantization thresholds fixed for low-resolution ADCs will not cause significant performance loss. Moreover, such fixed-threshold quantization function does not require any information of signal power which can reduce the hardware cost of ADCs. Simulations have been carried out in this paper to make comparisons between fixed-threshold and adaptive-threshold quantization regarding various factors.

With increased wireless connectivity and embedded sensors, vehicles are becoming more intelligent, offering Internet access, telematics, and advanced driver assistance systems. Along with all benefits, connectivity to the public network and automotive control systems introduces new threats and security risks to connected and autonomous driving systems. Therefore, it is highly critical to design robust security mechanisms to protect the system from potential attacks and security vulnerabilities. An intrusion detection system (IDS) is a promising solution to detect and identify attacks and malicious behaviour within the network. This paper proposes a two-layer IDS mechanism that exploits machine learning (ML) solutions for collaborative attack detection between an on-vehicle IDS module and a developed IDS platform at a mobile edge computing (MEC) server. The results illustrate that the proposed solution can significantly reduce communication latency and energy consumption up to 80% while maintaining a high level of detection accuracy.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Authentication protocols are powerful tools to ensure confidentiality as an important feature of Internet of Things (IoT). The Denial-of-Service (DoS) attack is one of the significant threats to availability, as another essential feature of IoT, which deprives users of services by consuming the energy of IoT nodes. On the other hand, computational intelligence algorithms can be applied to solve such issues in the network and cyber domains. Motivated by this, this article links these concepts. To do so, we analyze two lightweight authentication protocols, present a DoS attack inspired by users' misbehavior and suggest a solution called received signal strength, which is easy to compute, applicable for resisting against different kinds of vulnerabilities in Internet protocols, and feasible for practical implementations. We implement it on two scenarios for locating attackers, investigate the effects of IoT devices' internal error on locating, and propose an optimization problem to finding the exact location of attackers, which is efficiently solvable for computational intelligence algorithms, such as TLBO. Besides, we analyze the solutions for unreliable results of accurate devices and provide a solution to detect attackers with less than 12-cm error and the false alarm probability of 0.7%.

In this paper, a novel terahertz (THz) spectroscopy technique and a new graphene-based sensor is proposed. The proposed sensor consists of a graphene-based metasurface (MS) that operates in reflection mode over a broad range of frequency band (0.2 - 6 THz) and can detect relative permittivity of up to 4 with a resolution of 0.1 and a thickness ranging from 5 μm to 600 μm with a resolution of 0.5 μm. To the best of author's knowledge, such a THz sensor with such capabilities has not been reported yet. Additionally, an equivalent circuit of the novel unit cell is derived and compared with two conventional grooved structures to showcase the superiority of the proposed unit cell. The proposed spectroscopy technique utilizes some unique spectral features of a broadband reflection wave including Accumulated Spectral power (ASP) and Averaged Group Delay (AGD), which are independent to resonance frequencies and can operate over a broad range of spectrum. ASP and AGD can be combined to analyse the magnitude and phase of the reflection diagram as a coherent technique for sensing purposes. This enables the capability to distinguish between different analytes with high precision which, to the best of author's knowledge, has been accomplished for the first time.

In conventional hybrid beamforming approaches, the number of radio-frequency (RF) chains is the bottleneck on the achievable spatial multiplexing gain. Recent studies have overcome this limitation by increasing the update-rate of the RF beamformer. This paper presents a framework to design and evaluate such approaches, which we refer to as agile RF beamforming, from theoretical and practical points of view. In this context, we consider the impact of the number of RF-chains, phase shifters' speed, and resolution to design agile RF beamformers. Our analysis and simulations indicate that even an RF-chain-free transmitter, which its beamformer has no RF-chains, can provide a promising performance compared with fully-digital systems and significantly outperform the conventional hybrid beamformers. Then, we show that the phase shifter's limited switching speed can result in signal aliasing, in-band distortion, and out-of-band emissions. We introduce performance metrics and approaches to measure such effects and compare the performance of the proposed agile beamformers using the Gram-Schmidt orthogonalization process. Although this paper aims to present a generic framework for deploying agile RF beamformers, it also presents extensive performance evaluations in communication systems in terms of adjacent channel leakage ratio, sum-rate, power efficiency, error vector magnitude, and bit-error rates.

Cloud envisioned cyber--physical systems (CCPS) is a practical technology that relies on the interaction among cyber elements like mobile users to transfer data in cloud computing. In CCPS, cloud storage applies data deduplication techniques aiming to save data storage and bandwidth for real-time services. In this infrastructure, data deduplication eliminates duplicate data to increase the performance of the CCPS application. However, it incurs security threats and privacy risks. For example, the encryption from independent users with different keys is not compatible with data deduplication. In this area, several types of research have been done. Nevertheless, they are suffering from a lack of security, high performance, and applicability. Motivated by this, in this article, we propose a message lock encryption with neVer-decrypt homomorphic encRyption (LEVER) protocol between the uploading CCPS user and cloud storage to reconcile the encryption and data deduplication. Interestingly, LEVER is the first brute-force resilient encrypted deduplication with only cryptographic two-party interactions. We perform several numerical analysis of LEVER and confirm that it provides high performance and practicality compared to the literature.

The sheer volume of industrial Internet of Things (IIoT) malware is one of the most serious security threats in today's interconnected world, with new types of advanced persistent threats and advanced forms of obfuscations. This article presents a robust federated learning based architecture called Fed-IIoT for detecting Android malware applications in IIoT. Fed-IIoT consists of two parts: first, participant side, where the data are triggered by two dynamic poisoning attacks based on a generative adversarial network (GAN) and federated GAN; and second, server side, which aims to monitor the global model and shape a robust collaboration training model, by avoiding anomaly in aggregation by a GAN network (A3GAN) and adjust two GAN-based countermeasure algorithms. One of the main advantages of Fed-IIoT is that devices can safely participate in the IIoT and efficiently communicate with each other, with no privacy issues. We evaluate our solutions through experiments on various features using three IoT datasets. The results confirm the high accuracy rates of our attack and defense algorithms and show that the A3GAN defensive approach preserves the robustness of data privacy for Android mobile users and is about 8% higher accuracy with existing state-of-the-art solutions.

The fifth-generation (5G) mobile communication technology with higher capacity and data rate, ultra-low device to device (D2D) latency, and massive device connectivity will greatly promote the development of vehicular ad hoc networks (VANETs). Meantime, new challenges such as security, privacy and efficiency are raised. In this article, a hybrid D2D message authentication (HDMA) scheme is proposed for 5G-enabled VANETs, in which a novel group signature-based algorithm is used for mutual authentication between vehicle to vehicle (V2V) communication. In addition, a pre-computed lookup table is adopted to reduce the computation overhead of modular exponentiation operation. Security analysis shows that HDMA is robust to resist various security attacks, and performance analysis also points out that, the authentication overhead of HDMA is more efficient than some traditional schemes with the help of the pre-computed lookup table in V2V and vehicle to infrastructure (V2I) communication.

Volunteer computing is an Internet-based distributed computing in which volunteers share their extra available resources to manage large-scale tasks. However, computing devices in a Volunteer Computing System (VCS) are highly dynamic and heterogeneous in terms of their processing power, monetary cost, and data transferring latency. To ensure both of the high Quality of Service (QoS) and low cost for different requests, all of the available computing resources must be used efficiently. Task scheduling is an NP-hard problem that is considered as one of the main critical challenges in a heterogeneous VCS. Due to this, in this article, we design two task scheduling algorithms for VCSs, named Min-CCV and Min-V . The main goal of the proposed algorithms is jointly minimizing the computation, communication, and delay violation cost for the Internet of Things (IoT) requests. Our extensive simulation results show that proposed algorithms are able to allocate tasks to volunteer fog/cloud resources more efficiently than the state-of-the-art. Specifically, our algorithms improve the deadline satisfaction task rates around 99.5% and decrease the total cost between 15 to 53% in comparison with the genetic-based algorithm.

Being able to accommodate multiple simultaneous transmissions on a single channel, non-orthogonal multiple access (NOMA) appears as an attractive solution to support massive machine type communication (mMTC) that faces a massive number of devices competing to access the limited number of shared radio resources. In this paper, we first analytically study the throughput performance of NOMA-based random access (RA), namely NOMA-RA. We show that while increasing the number of power levels in NOMA-RA leads to a further gain in maximum throughput, the growth of throughput gain is slower than linear. This is due to the higher-power dominance characteristic in power-domain NOMA known in the literature. We explicitly quantify the throughput gain for the very first time in this paper. With our analytical model, we verify the performance advantage of NOMA-RA scheme by comparing with the baseline multi-channel slotted ALOHA (MS-ALOHA), with and without capture effect. Despite the higher-power dominance effect, the maximum throughput of NOMA-RA with four power levels achieves over three times that of the MS-ALOHA. However, our analytical results also reveal the sensitivity of load on the throughput of NOMA-RA. To cope with the potential bursty traffic in mMTC scenarios, we propose adaptive load regulation through a practical user barring algorithm. By estimating the current load based on the observable channel feedback, the algorithm adaptively controls user access to maintain the optimal loading of channels to achieve maximum throughput. When the proposed user barring algorithm is applied, simulations demonstrate that the instantaneous throughput of NOMA-RA always remains close to the maximum throughput confirming the effectiveness of our load regulation.

Low Earth Orbit (LEO) satellites are becoming increasingly important among the existing satellite communication systems. Due to its low orbital profile, the LEO satellites can provide high-speed, low-latency, and ubiquitous services for ground users. However, as the number of satellites continues to increase, frequency bands as non-renewable resources will seriously restrict the future evolution of the LEO networks. In this paper, a flexible spectrum sharing and cooperative service method is proposed to address the collinear interference issue caused by LEO satellites while passing through the coverage area of the GEO beam. By using the continuous power allocation optimization, our scheme ensures that the service of the LEO satellites will not lead to the degradation of the service quality of the GEO beam. Meanwhile, by taking full advantage of the cooperation between LEO satellites, the quality of their service can be significantly improved. Simulation results show that our proposed algorithm converges quickly. The transmission efficiency and the stability of the system can all be guaranteed.

With the advent of Network Function Virtualization (NFV) techniques, a subset of the Internet traffic will be treated by a chain of virtual network functions (VNFs) during their journeys while the rest of the background traffic will still be carried based on traditional routing protocols. Under such a multi-service network environment, we consider the co-existence of heterogeneous traffic control mechanisms, including flexible, dynamic service function chaining (SFC) traffic control and static, dummy IP routing for the aforementioned two types of traffic that share common network resources. Depending on the traffic patterns of the background traffic which is statically routed through the traditional IP routing platform, we aim to perform dynamic service function chaining for the foreground traffic requiring VNF treatments, so that both the end-to-end SFC performance and the overall network resource utilization can be optimized. Towards this end, we propose a deep reinforcement learning based scheme to enable intelligent SFC routing decision-making in dynamic network conditions. The proposed scheme is ready to be deployed on both hybrid SDN/IP platforms and future advanced IP environments. Based on the real GEANT network topology and its one-week traffic traces, our experiments show that the proposed scheme is able to significantly improve from the traditional routing paradigm and achieve close-to-optimal performances very fast while satisfying the end-to-end SFC requirements.

Our earlier work has demonstrated that a sufficiently trained recurrent neural network (RNN) can effectively detect base station performance degradations. We encountered a performance limit however: the accuracy gain diminishes as the RNN deepens. In this paper, we investigate the performance limit of a well-trained RNN by visualising its processes and modeling its internal operation. We first illustrate that inputs following a certain probability density undergo transformation in the RNN. By linearising the RNN process, we then develop a linear model to analyse the transformation. Using the model, we not only unveil insights into RNN operational behaviour, but are also able to explain the effect of diminishing gains in deeper RNNs. Finally, we validate our model and demonstrate its ability to accurately predict the performance of a well-trained RNN.

The launch of the StarLink Project has recently stimulated a new wave of research on integrating Low Earth Orbit (LEO) satellite networks with the terrestrial Internet infrastructure. In this context, one distinct technical challenge to be tackled is the frequent topology change caused by the constellation behaviour of LEO satellites. Frequent change of the peering IP connection between the space and terrestrial Autonomous Systems (ASes) inevitably disrupts the Border Gateway Protocol (BGP) routing stability at the network boundaries which can be further propagated into the internal routing infrastructures within ASes. To tackle this problem, we introduce the Geosynchronous Network Grid Addressing (GNGA) scheme by decoupling IP addresses from physical network elements such as a LEO satellite. Specifically, according to the density of LEO satellites on the orbits, the IP addresses are allocated to a number of stationary "grids" in the sky and dynamically bound to the interfaces of the specific satellites moving into the grids along time. Such a scheme allows static peering connection between a terrestrial BGP speaker and a fixed external BGP (e-BGP) peer in the space, and hence is able to circumvent the exposure of routing disruptions to the legacy terrestrial ASes. This work-in-progress specifically addresses a number of fundamental technical issues pertaining to the design of the GNGA scheme.

A novel reconfigurable dielectric resonator antenna (DRA) employed a T-Shaped microstrip-fed structure in order to excite the dielectric resonator is presented. By carefully adjusting the location of the inverted U-shaped slot, switches, and length of arms, the proposed antenna can support WLAN wireless system. In addition, the presented DRA can be proper for cognitive radio because of availability switching between wideband and narrowband operation. The proposed reconfigurable DRA consisting of a Roger substrate with relative permittivity 3 and a size of 20 mm × 30 mm × 0.75 mm and a dielectric resonator (DR) with a thickness of 9 mm and the overall size of 18 mm × 18 mm. Moreover, the antenna has been fabricated and tested which test results have enjoyed a good agreement with the simulated results. As well as this, the measured and simulated results show the reconfigurability that the proposed DRA provides a dual-mode operation and also three different resonance frequencies as a result of switching the place of arms.

A machine learning (ML) technique has been used to synthesis a linear millimetre wave (mmWave) phased array antenna by considering the phase-only synthesis approach. For the first time, gradient boosting tree (GBT) is applied to estimate the phase values of a 16-element array antenna to generate different far-field radiation patterns. GBT predicts phases while the amplitude values have been equally set to generate different beam patterns for various 5G mmWave transmission scenarios such as multicast, unicast, broadcast and unmanned aerial vehicle (UAV) applications.

It is well documented that the achievable throughput of MIMO systems that employ linear beamforming can significantly degrade when the number of concurrently transmitted information streams approaches the number of base-station antennas. To increase the number of the supported streams, and therefore, to increase the achievable net throughput, non-linear beamforming techniques have been proposed. These beamforming approaches are typically evaluated via simulations or via simplified over-the-air experiments that are sufficient for validating their basic principles, but they neither provide insights about potential practical challenges when trying to adopt such approaches in a standards-compliant framework, nor they provide any indication about the achievable performance when they are part of a standards-compliant protocol stack. In this work, for first time, we evaluate non-linear beamforming in a 3GPP standards-compliant framework, using our recently-proposed SWORD research platform. SWORD is a flexible, open for research, software-driven platform that enables the rapid evaluation of advanced algorithms without extensive hardware optimizations that can prevent promising algorithms from being evaluated in a standards-compliant stack. We show that in an indoor environment, vector perturbation-based non-linear beamforming can provide up to 46% throughput gains compared to linear approaches for 4×4 MIMO systems, while it can still provide gains of nearly 10% even if the number of base-station antennas is doubled.

Decentralized joint transmit power and beamforming selection for multiple antenna wireless ad hoc networks operating in a multi-user interference environment is considered. An important feature of the considered environment is that altering the transmit beamforming pattern at some node generally creates more significant changes to interference scenarios for neighboring nodes than variation of the transmit power. Based on this premise, a good neighbor algorithm is formulated in the way that at the sensing node, a new beamformer is selected only if it needs less than the given portion of the transmit power required for the current beamformer. Otherwise, it keeps the current beamformer and achieves the performance target only by means of power adaptation. Equilibrium performance and convergence behavior of the proposed algorithm compared to the best response and regret matching solutions is demonstrated by means of semi-analytic Markov chain performance analysis for small scale and simulations for large scale networks.

The first wave of IEEE 802.11ax capable devices have already hit the market, aiming at enhancing the Quality of Experience (QoE) for the users in dense deployments, by enabling novel features to improve throughput and spectrum efficiency. One of these features is Spatial Reuse (SR) mechanism, which is introduced for coping with the exposed node problem. Under the SR operation, nodes belonging on different Basic Service Sets (BSSs) are allowed to initiate concurrent transmissions, utilising the spectrum resources and improving throughput. However, the main challenge for this enabling technology is the increased interference level that is introduced by the concurrent transmissions. Even though, there are a few algorithms available in the literature that study this issue for the IEEE 802.11ax, in this article we look into that issue from a different perspective. We propose an Interference-Aware scheduler for the Medium Access Control (MAC) queue based on the interference level observed and other characteristics that can be obtained from the channel and the inter-BSS frames. This paper considers only downlink traffic for the evaluation of the proposed scheme with simulation-based results showing a clear performance improvement of up to 34% against the legacy First-In First-Out (FIFO) MAC queue by introducing new policies and leave room for further exploration and enhancements.

This paper aims to handle the joint transmitter and noncoherent receiver design for multiuser multiple-input multiple-output (MU-MIMO) systems through deep learning. Given the deep neural network (DNN) based noncoherent receiver, the novelty of this work mainly lies in the multiuser waveform design at the transmitter side. According to the signal format, the proposed deep learning solutions can be divided into two groups. One group is called pilot-aided waveform, where the information-bearing symbols are time-multiplexed with the pilot symbols. The other is called learning-based waveform, where the multiuser waveform is partially or even completely designed by deep learning algorithms. Specifically, if the information-bearing symbols are directly embedded in the waveform, it is called systematic waveform. Otherwise, it is called non-systematic waveform, where no artificial design is involved. Simulation results show that the pilot-aided waveform design outperforms the conventional zero forcing receiver with least squares (LS) channel estimation on small-size MU-MIMO systems. By exploiting the time-domain degrees of freedom (DoF), the learning-based waveform design further improves the detection performance by at least 5 dB at high signal-to-noise ratio (SNR) range. Moreover, it is found that the traditional weight initialization method might cause a training imbalance among different users in the learning-based waveform design. To tackle this issue, a novel weight initialization method is proposed which provides a balanced convergence performance with no complexity penalty.

The fifth-generation (5G) new radio (NR) cellular system promises a significant increase in capacity with reduced latency. However, the 5G NR system will be deployed along with legacy cellular systems such as the long-term evolution (LTE). Scarcity of spectrum resources in low frequency bands motivates adjacent-/co-carrier deployments. This approach comes with a wide range of practical benefits and it improves spectrum utilization by re-using the LTE bands. However, such deployments restrict the 5G NR flexibility in terms of frame allocations to avoid the most critical mutual adjacent-channel interference. This in turns prevents achieving the promised 5G NR latency figures. In this we paper, we tackle this issue by proposing to use the mini-slot uplink feature of 5G NR to perform uplink acknowledgment and feedback to reduce the frame latency with selective blind retransmission to overcome the effect of interference. Extensive system-level simulations under realistic scenarios show that the proposed solution can reduce the peak frame latency for feedback and acknowledgment up to 33% and for retransmission by up to 25% at a marginal cost of an up to 3% reduction in throughput.

Multi-access edge computing for mobile computing-task offloading is driving the extreme utilization of available degrees of freedom (DoF) for ultra-reliable low-latency downlink communications. The fundamental aim of this work is to find latency-constrained transmission protocols that can achieve a very-low outage probability (e.g. 0.001%). Our investigation is mainly based upon the Polyanskiy-Poor-Verdú formula on the finite-length coded channel capacity, which is extended from the quasi-static fading channel to the frequency selective channel. Moreover, the use of a suitable duplexing mode is also critical to the downlink reliability. Specifically, time-division duplexing (TDD) outperforms frequency-division duplexing (FDD) in terms of the frequency diversity-gain. On the other hand, FDD takes the advantage of having more temporal DoF in the downlink, which can be exchanged into the spatial diversity-gain through the use of space-time coding. Numerical study is carried out to compare the reliability between FDD and TDD under various latency constraints.

This article exploits a generic downlink symbiotic radio (SR) system, where a base station (BS) establishes a direct (primary) link with a receiver having an integrated backscatter device (BD). In order to accurately measure the backscatter link, the backscattered signal packets are designed to have finite block length. As such, the backscatter link in this SR system employs the finite blocklength channel codes. According to different types of the backscatter symbol period and transmission rate, we investigate the noncooperative and cooperative SR systems, and derive their average achievable rate of the direct and backscatter links, respectively. We formulate two optimization problems, i.e., transmit power minimization and energy-efficiency maximization. Due to the nonconvex property of these formulated optimization problems, the semidefinite programming (SDP) relaxation and the successive convex approximation (SCA) are considered to design the transmit beamforming vector. Moreover, a low-complexity transmit beamforming structure is constructed to reduce the computational complexity of the SDP relaxed solution. Finally, the simulation results are demonstrated to validate the proposed schemes.

This paper proposes a novel unipolar transceiver for visible light communication (VLC) by using orthogonal waveforms. The main advantage of our proposed scheme over most of the existing unipolar schemes in the literature is that the polarity of the real-valued orthogonal frequency division multiplexing (OFDM) sample determines the pulse shape of the continuous-time signal and thus, the unipolar conversion is performed directly in the analog instead of the digital domain. Therefore, our proposed scheme does not require any direct current (DC) biasing or clipping as it is the case with existing schemes in the literature. The bit error rate (BER) performance of our proposed scheme is analytically derived and its accuracy is verified by using Matlab simulations. Simulation results also substantiate the potential performance gains of our proposed scheme against the state-of-the-art OFDM-based systems in VLC; it indicates that the absence of DC shift and clipping in our scheme supports more reliable communication and outperforms the asymmetrically clipped optical-OFDM (ACO-OFDM), DC optical-OFDM (DCO-OFDM) and unipolar-OFDM (U-OFDM) schemes. For instance, our scheme outperforms ACO-OFDM by at least 3 dB (in terms of signal to noise ratio) at a target BER of 10-4, when considering the same spectral efficiency for both schemes.

In this treatise, we introduce a novel polarization modulation (PM) scheme, where we capitalize on the reconfigurable polarization antenna design for exploring the polarization domain degrees of freedom, thus boosting the system throughput. More specifically, we invoke the inherent properties of a dual polarized (DP) antenna for transmitting additional information carried by the axial ratio (AR) and tilt angle of elliptic polarization, in addition to the information streams transmitted over its vertical (V) and horizontal (H) components. Furthermore, we propose a special algorithm for generating an improved PM constellation tailored especially for wireless PM modulation. We also provide an analytical framework to compute the average bit error rate (ABER) of the PM system. Furthermore, we characterize both the discrete-input continuous-output memoryless channel (DCMC) capacity and the continuous-input continuous-output memoryless channel (CCMC) capacity as well as the upper and lower bounds of the CCMC capacity. The results show the superiority of our proposed PM system over conventional modulation schemes in terms of both higher throughput and lower BER. In particular, our simulation results indicate that the gain achieved by the proposed Q-dimensional PM scheme spans between 10dB and 20dB compared to the conventional modulation. It is also demonstrated that the PM system attains between 54% and 87.5% improvements in terms of ergodic capacity. Furthermore, we show that this technique can be applied to MIMO systems in a synergistic manner in order to achieve the target data rate target for 5G wireless systems with much less system resources (in terms of bandwidth and the number of antennas) compared to existing MIMO techniques.

The concept of Ultra Dense Networks (UDNs) is often seen as a key enabler of the next generation mobile networks. The massive number of BSs in UDNs represents a challenge in deployment, and there is a need to understand the performance behaviour and benefit of a network when BS locations are carefully selected. This can be of particular importance to the network operators who deploy their networks in large indoor open spaces such as exhibition halls, airports or train stations where locations of BSs often follow a regular pattern. In this paper we study performance of UDNs in downlink for regular network produced by careful BS site selection and compare to the irregular network with random BS placement. We first develop an analytical model to describe the performance of regular networks showing many similar performance behaviour to that of the irregular network widely studied in the literature. We also show the potential performance gain resulting from proper site selection. Our analysis further shows an interesting finding that even for over-densified regular networks, a non-negligible system performance could be achieved.

Conventional mobility management schemes tend to hit the core network with increased signaling load when the cell size is shrinking and the user mobility speed increases. To mitigate this problem research community has proposed various intelligent mobility management schemes that take advantage of the predictability of the users mobility pattern. However, most of the proposed solutions are only focused on signaling of the active-state (i.e., handover signaling) and proposals on improvement of the idle-state signaling has been limited and were not well received from the industrial practitioners. This paper first surveys the major shortcomings of the existing proposals for the idle mode mobility management and then proposes a new architecture, namely predictive mobility management (PrMM) to mitigate the identified challenges. An analytical framework is developed and a closed form solution for the expected signaling overhead of the PrMM is presented. The results of numerical evaluations confirm that, depending on user mobility and network configuration, the PrMM efficiency can surpass the long term evolution (LTE) 4G signaling scheme by over 90%. Analysis of the results shows that the best performance is achieved at highly dense paging areas and lower cell crossing rates.

A clear understanding of mixed-numerology signals multiplexing and isolation in the physical layer is of importance to enable spectrum efficient radio access network (RAN) slicing, where the available access resource is divided into slices to cater to services/users with optimal individual design. In this paper, a RAN slicing framework is proposed and systematically analyzed from the physical layer perspective. According to the baseband and radio frequency (RF) configurations imparities among slices, we categorize four scenarios and elaborate on the numerology relationships of slices configurations. By considering the most generic scenario, system models are established for both uplink and downlink transmissions. Besides, a low out of band emission (OoBE) waveform is implemented in the system for the sake of signal isolation and inter-service/slice-band-interference (ISBI) mitigation. We propose two theorems as the basis of algorithms design in the established system, which generalize the original circular convolution property of discrete Fourier transform (DFT). Moreover, ISBI cancellation algorithms are proposed based on a collaboration detection scheme, where joint slices signal models are implemented. The framework proposed in the paper establishes a foundation to underpin extremely diverse use cases in 5G that implement on a common infrastructure.

This article proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for ” user grouping ” and ” content classification .” The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 users of BBC iPlayer. Using the proposed grouping technique, user groups of similar interest and up to two video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art, including Szabo-Huberman (SH), Multivariate Linear (ML), and Multivariate linear Radial Basis Functions (MRBF) models by an average of 45%, 33%, and 24%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising, and video broadcasting technologies benefit from our findings to illustrate the implications.

In this paper, we investigate the hybrid precoding design for joint multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. The subarray-based sparse radio frequency chain structure is considered at base station (BS). Then, we formulate a joint hybrid analog/digital precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at BS and minimum harvested energy at receivers are considered. Due to the difficulty in solving the formulated problem, we first design the codebook-based analog precoding approach and then, we only need to jointly optimize the digital precoding and power splitting ratio. Next, we equivalently transform the fractional objective function of the optimization problem into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques, which is solved by a proposed iterative algorithm. Finally, simulation results are provided to show the performance of the proposed algorithm.

Millimeter wave (mmWave) communication is a promising technology in future wireless networks because of its wide bandwidths that can achieve high data rates. However, high beam directionality at the transceiver is needed due to the large path loss at mmWave. Therefore, in this paper, we investigate the beam alignment and power allocation problem in a nonorthogonal multiple access (NOMA) mmWave system. Different from the traditional beam alignment problem, we consider the NOMA scheme during the beam alignment phase when two users are at the same or close angle direction from the base station. Next, we formulate an optimization problem of joint beamwidth selection and power allocation to maximize the sum rate, where the quality of service (QoS) of the users and total power constraints are imposed. Since it is difficult to directly solve the formulated problem, we start by fixing the beamwidth. Next, we transform the power allocation optimization problem into a convex one, and a closed-form solution is derived. In addition, a one-dimensional search algorithm is used to find the optimal beamwidth. Finally, simulation results are conducted to compare the performance of the proposed NOMA-based beam alignment and power allocation scheme with that of the conventional OMA scheme.

In this paper, a novel unsupervised deep learning approach is proposed to tackle the multiuser frequency synchronization problem inherent in orthogonal frequency-division multiple-access (OFDMA) uplink communications. The key idea lies in the use of the feed-forward deep neural network (FF-DNN) for multiuser interference (MUI) cancellation taking advantage of their strong classification capability. Basically, the proposed FF-DNN consists of two essential functional layers. One is called carrier-frequency-offsets (CFOs) classification layer that is responsible for identifying the users' CFO range, and another is called MUI-cancellation layer responsible for joint multiuser detection (MUD) and frequency synchronization. By such means, the proposed FF-DNN approach showcases remarkable MUI-cancellation performances without the need of multiuser CFO estimation. In addition, we also exhibit an interesting phenomenon occurred at the CFO-classification stage, where the CFO-classification performance get improved exponentially with the increase of the number of users. This is called multiuser diversity gain in the CFO-classification stage, which is carefully studied in this paper.

Physical layer security (PLS) technologies have attracted much attention in recent years for their potential to provide information-theoretically secure communications. Artificial Noise (AN)-aided transmission is considered as one of the most practicable PLS technologies, as it can realize secure transmission independent of the eavesdropper's channel status. In this paper, we reveal that AN transmission has the dependency of eavesdropper's channel condition by introducing our proposed attack method based on a supervised-learning algorithm which utilizes the modulation scheme, available from known packet preamble and/or header information, as supervisory signals of training data. Numerical simulation results with the comparison to conventional clustering methods show that our proposed method improves the success probability of attack from 4.8% to at most 95.8% for the QPSK modulation. It implies that the transmission to the receiver in the cell-edge with low order modulation will be cracked if the eavesdropper's channel is good enough by employing more antennas than the transmitter. This work brings new insights into the effectiveness of AN schemes and provides useful guidance for the design of robust PLS techniques for practical wireless systems.

In this paper, unsupervised deep learning solutions for multiuser single-input multiple-output (MU-SIMO) coherent detection are extensively investigated. According to the ways of utilizing the channel state information at the receiver side (CSIR), deep learning solutions are divided into two groups. One group is called equalization and learning, which utilizes the CSIR for channel equalization and then employ deep learning for multiuser detection (MUD). The other is called direct learning, which directly feeds the CSIR, together with the received signal, into deep neural networks (DNN) to conduct the MUD. It is found that the direct learning solutions outperform the equalization-and-learning solutions due to their better exploitation of the sequence detection gain. On the other hand, the direct learning solutions are not scalable to the size of SIMO networks, as current DNN architectures cannot efficiently handle many co-channel interferences. Motivated by this observation, we propose a novel direct learning approach, which can combine the merits of feedforward DNN and parallel interference cancellation. It is shown that the proposed approach trades off the complexity for the learning scalability, and the complexity can be managed due to the parallel network architecture.

In this paper, an ultra-wideband, Dielectric Resonator Antenna (DRA) has been proposed. The proposed antenna is based on isosceles triangular DRA (TDRA), which is fed from the base side using a 50Ω probe. For bandwidth enhancement and radiation characteristics improvement, a partially cylindrical-shape hole is etched from its base side which approached probe feed to the center of TDRA. The dielectric resonator (DR) is located over an extended conducting ground plane. This technique has significantly enhanced antennas bandwidth from 48.8% to 80% (5.29-12.35 GHz), while the biggest problem was radiation characteristics. The basis antenna possesses negative gain in a wide range of bandwidth from 7.5 GHz to 10.5 GHz down to -13.8 dBi. Using this technique improve antenna gain over 1.6 dBi for whole bandwidth, while peak gain is 7.2 dBi.

In this paper, a high flat gain waveguide-fed aperture antenna has been proposed. For this purpose, two layers of FR4 dielectric as superstrates have been located in front of the aperture to enhance the bandwidth and the gain of the antenna. Moreover, a conductive shield, which is connected to the edges of the ground plane and surrounding aperture and superstrates, applied to the proposed structure to improve its radiation characteristics. The proposed antenna has been simulated with HFSS and optimized with parametric study and the following results have been obtained. The maximum gain of 13.0 dBi and 0.5-dBi gain bandwidth of 25.9 % (8.96-11.63 GHz) has been achieved. The 3-dBi gain bandwidth of the proposed antenna is 40.7% (8.07-12.20 GHz), which has a suitable reflection coefficient (≤ -10dBi) in whole bandwidth. This antenna comprises a compact size of (1.5λ×1.5λ), easy structure and low-cost fabrication.

In this paper, we present a novel random access method for future mobile cellular networks that support machine type communications. Traditionally, such networks establish connections with the devices using a random access procedure, however massive machine type communication poses several challenges to the design of random access for current systems. State-of-the-art random access techniques rely on predicting the traffic load to adjust the number of users allowed to attempt the random access preamble phase, however this delays network access and is highly dependent on the accuracy of traffic prediction and fast signalling. We change this paradigm by using the preamble phase to estimate traffic and then adapt the network resources to the estimated load. We introduce Preamble Barring that uses a probabilistic resource separation to allow load estimation in a wide range of load conditions and combine it with multiple random access responses. This results in a load adaptive method that can deliver near-optimal performance under any load condition without the need for traffic prediction or signalling, making it a promising solution to avoid network congestion and achieve fast uplink access for massive MTC.

Network Coding is an efficient concept to improve the network capacity under lossy channels. In this paper, we present a scheme based on Serially Concatenated Code (SCC) that has been proposed in the literature and comprises an outer fountain code and Random Linear Network Coding (RLNC) as an inner code to allow intermediate nodes to re-encode the information. We have placed SCC in a shim layer between the Medium Access Control (MAC) and Network layers where the (de-)coders operate. Furthermore, a header for the SCC scheme was also introduced that carries the information required for decoding the information. Simulation results in a fading IEEE 802.11 channel and an analysis based on the IEEE 802.11 basic access scheme, show that SCC can indeed improve the performance when it is carefully designed and can provide a means of enabling robust communications without the use of ACKs and retransmissions (i.e. multicast/broadcast communications).

The recent paradigm shift towards the transmission of large numbers of mutually interfering information streams, as in the case of aggressive spatial multiplexing, combined with requirements towards very low processing latency despite the frequency plateauing of traditional processors, initiates a need to revisit the fundamental maximum-likelihood (ML) and, consequently, the sphere-decoding (SD) detection problem. This work presents the design and VLSI architecture of MultiSphere; the first method to massively parallelize the tree search of large sphere decoders in a nearly-concurrent manner, without compromising their maximum-likelihood performance, and by keeping the overall processing complexity comparable to that of highly-optimized sequential sphere decoders. For a 10 × 10 MIMO spatially multiplexed system with 16-QAM modulation and 32 processing elements, our MultiSphere architecture can reduce latency by 29× against well-known sequential SDs, approaching the processing latency of linear detection methods, without compromising ML optimality. In MIMO multicarrier systems targeting exact ML decoding, MultiSphere achieves processing latency and hardware efficiency that are orders of magnitude improved compared to approaches employing one SD per subcarrier. In addition, for 16×16 both “hard”and “soft”-output MIMO systems, approximate MultiSphere versions are shown to achieve similar error rate performance with state-of-the art approximate SDs having akin parallelization properties, by using only one tenth of the processing elements, and to achieve up to approximately 9× increased energy efficiency.

This paper investigates a secure wireless-powered integrated service system with full-duplex self-energy recycling. Specifically, an energy-constrained information transmitter (IT), powered by a power station (PS) in a wireless fashion, broadcasts two types of services to all users: a multicast service intended for all users and a confidential unicast service subscribed to by only one user while protecting it from any other unsubscribed users and an eavesdropper. Our goal is to jointly design the optimal input covariance matrices for the energy beamforming, the multicast service, the confidential unicast service, and the artificial noises from the PS and the IT, such that the secrecy-multicast rate region (SMRR) is maximized subject to the transmit power constraints. Due to the non-convexity of the SMRR maximization (SMRRM) problem, we employ a semidefinite programming-based two-level approach to solve this problem and find all of its Pareto optimal points. In addition, we extend the SMRRM problem to the imperfect channel-state information case, where a worst-case SMRRM formulation is investigated. Moreover, we exploit the optimized transmission strategies for the confidential service and energy transfer by analyzing their own rank-one profile. Finally, numerical results are provided to validate our proposed schemes.

In time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radio-frequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closed-form expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.

The spatially-incoherent radiators in visible light communication (VLC) constrain the optical carrier to be only driven by a real electrical sub-carrier, which cannot be quadrature modulated as in classic RF-based communication systems. This, in turn, severely limits the transmission throughput of VLC systems. To overcome this technical challenge, a novel coherent transmission is proposed. As such, the optical carrier is only treated as a purely amplitude- modulated carrier capable of transmitting the quadrature modulated symbols. The ability of our novel coherent transmission to fully reconstruct the in-phase and quadrature parts is validated through analytical symbol error rate and Matlab simulations. Results also show that the proposed scheme, while remarkable in its simplicity, can improve both the spectral and energy efficiency of VLC systems, i.e. double the spectral efficiency and achieve more than 45% energy efficiency improvement, when compared to its existing counterpart.

This paper introduces a millimeter-wave multiple-input-multiple-output (MIMO) antenna for autonomous (self-driving) cars. The antenna is a modified four-port balanced antipodal Vivaldi which produces four directional beams and provides pattern diversity to cover 90 deg angle of view. By using four antennas of this kind on four corners of the car's bumper, it is possible to have a full 360 deg view around the car. The designed antenna is simulated by two commercially full-wave packages and the results indicate that the proposed method can successfully bring the required 90 deg angle of view.

Network function virtualization (NFV) is one of key features envisioned for the upcoming 5G core networks in order to support high flexibility in network deployment and management. However, potential performance degradations that could be caused by virtualization of network functions is still a controversial issue, especially in regards to virtualization of core network components. In this paper, we evaluate the effect of NFV on an end-to-end mobile network testbed, which is deployed in the 5G Innovation Centre (5GIC) in University of Surrey. The testbed consists of indoor and outdoor LTE Radio Access Networks (RAN) equipment, as well as an enhanced Evolved Packet Core (EPC) following LTE Release 14 specifications, such as control and user plane separation (CUPS). The paper compares the performance of the softwarised core network and that of its virtualized counterpart. Measurement results show that the virtualized core network has adequately similar network performance in terms of throughput and latency, compared with the non-virtual core.

The aim of this paper is to handle the multi-frequency synchronization problem inherent in orthogonal frequency-division multiple access (OFDMA) uplink communications, where the carrier frequency offset (CFO) for each user may be different, and they can be hardly compensated at the receiver side. Our major contribution lies in the development of a novel OFDM receiver that is resilient to unknown random CFO thanks to the use of a CFO-compensator bank. Specifically, the whole CFO range is evenly divided into a set of sub-ranges, with each being supported by a dedicated CFO compensator. Given the optimization for CFO compensator a NP-hard problem, a machine deep-learning approach is proposed to yield a good sub-optimal solution. It is shown that the proposed receiver is able to offer inter-carrier interference free performance for OFDMA systems operating at a wide range of SNRs.

Considering a densely populated area where a mobile device, with a single RF chain, shares its message with a set of mobile devices through narrowband mmWave channel, an analogue-beam splitting approach is proposed to achieve a good capacity and coverage trade-off. The proposed approach aims at maximizing the capacity of the mmWave multicast channel through antenna-element grouping and adaptive phase shifting, which takes into account of the inter-beam interference. When receivers are randomly distributed on a circle centered at the transmitter, according to the uniform distribution, it is found that the impact of inter-beam interference on the channel capacity can be negligibly small, and thus the analogue-beam splitting approach can be largely simplified in practice. Computer simulations are carried out to elaborate our theoretical study and demonstrate considerable advantages of the proposed analogue-beam splitting approach.

IEEE 802.11ax Spatial Reuse (SR) is a new category in the IEEE 802.11 family, aiming at improving the spectrum efficiency and the network performance in dense deployments. The main and perhaps the only SR technique in that amendment is the Basic Service Set (BSS) Color. It aims at increasing the number of concurrent transmissions in a specific area, based on a newly defined Overlapping BSS/Preamble-Detection (OBSS/PD) threshold and the Received Signal Strength Indication (RSSI) from Overlapping BSSs (OBSSs). In this paper, we propose a Control OBSS/PD Sensitivity Threshold (COST) algorithm for adjusting OBSS/PD threshold based on the interference level and RSSI from the associated recipient(s). In contrast to the Dynamic Sensitivity Control (DSC) algorithm that was proposed for setting OBSS/PD, COST is fully aware of any changes in OBSSs and can be applied to any IEEE 802.11ax node. Simulation results in various scenarios, show a clear performance improvement of up to 57% gain in throughput over a conservative fixed OBSS/PD for the legacy BSS Color and DSC.

A techno‐economic analysis of the 5G enhanced mobile broadband scenario in dense urban areas has been accomplished by radio capacity modelling of probable 5G technologies within a 1 km 2 grid representing central London. Different density networks were modelled at 700 MHz (macro network), 3.5 GHz (micro network) and 24–27.5 GHz (hot spots) – together with 802.11ac access points. Using published data on network costs various deployment options have been evaluated for capacity, headline rate and capital expenditure/operating expense. It has been shown that reaching headline rates of 64–100 Mbps everywhere is possible with a number of different technology options. Massive increases in capacity (in excess of 100 Gbps/km 2 ), however, can only be realistically achieved with a millimetre wave (outdoor) and 802.11ac (internally). The cost of deploying such capacity, however, will be several times that of LTE – the authors estimate a 4–5 times increase in costs for a 100 Mbps everywhere network that has ×100 capacity increase over existing LTE networks. The cost of rolling out 5G is becoming an important issue and this work provides one of the few published estimates of the economics of ultra‐high capacity networks.

When dealing with a large number of devices, the existing indexing solutions for the discovery of Internet of Things (IoT) sources often fall short to provide an adequate scalability. This is due to the high computational complexity and communication overhead that is required to create and maintain the indices of the IoT sources particularly when their attributes are dynamic. This paper presents a novel approach for indexing distributed IoT sources and paves the way to design a data discovery service to search and gain access to their data. The proposed method creates concise references to IoT sources by using Gaussian mixture models. Furthermore, a summary update mechanism is introduced to tackle the change of sources availability and mitigate the overhead of updating the indices frequently. The proposed approach is benchmarked against a standard centralized indexing and discovery solution. The results show that the proposed solution reduces the communication overhead required for indexing by three orders of magnitude while depending on IoT network architecture it may slightly increase the discovery time.

In this paper, a novel approach, namely, real-complex hybrid modulation (RCHM), is proposed to scale up multiuser multiple-input multiple-output (MU-MIMO) detection with particular concern on the use of equal or approximately equal service antennas and user terminals (UTs). By RCHM, we mean that UTs transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown that, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing tradeoff that minimizes the required transmit-power given a sum rate. The signal pattern of RCHM is optimized with respect to the real-to-complex symbol ratio as well as power allocation. It is also shown that RCHM equips the successive interference canceling MU-MIMO receiver with near-optimal performances and fast convergence in Rayleigh fading channels. This result is validated through our mathematical analysis of the average bit-error-rate as well as extensive computer simulations considering the case with single or multiple base stations.

Filtered orthogonal frequency division multiplexing (F-OFDM) system is a promising waveform for 5G and beyond to enable the multi-service system and spectrum efficient network slicing. However, the performance for F-OFDM systems has not been systematically analyzed in the literature. In this paper, we first establish a mathematical model for an F-OFDM system and derive the conditions to achieve the interference-free one-tap channel equalization. In the practical cases (e.g., insufficient guard interval, asynchronous transmission, and so on), the analytical expressions for inter-symbol interference, inter-carrier interference, and adjacent-carrier interference are derived, where the last term is considered as one of the key factors for asynchronous transmissions. Based on the framework, an optimal power compensation matrix is derived to make all of the subcarriers having the same ergodic performance. Another key contribution of this paper is that we propose a multi-rate F-OFDM system to enable low-complexity low-cost communication scenarios, such as narrow-band Internet of Things, at the cost of generating inter-subband interference (ISubBI). Low computational complexity algorithms are proposed to cancel the ISubBI. The result shows that the derived analytical expressions match the simulation results, and the proposed ISubBI cancelation algorithms can significantly save the original F-OFDM complexity (up to 100 times) without significant performance loss.

Software-defined networking (SDN) is a promising paradigm of computer networks, offering a programmable and centralized network architecture. However, although such a technology supports the ability to dynamically handle network traffic based on real-time and flexible traffic control, SDN-based networks can be vulnerable to dynamic change of flow control rules, which causes transmission disruption and packet loss in SDN hardware switches. This problem can be critical because the interruption and packet loss in SDN switches can bring additional performance degradation for SDN-controlled traffic flows in the data plane. In this paper, we propose a novel robust flow control mechanism referred to as priority-based flow control (PFC) for dynamic but disruption-free flow management when it is necessary to change flow control rules on the fly. PFC minimizes the complexity of flow modification process in SDN switches by temporarily adapting the priority of flow rules in order to substantially reduce the time spent on control-plane processing during run-time. Measurement results show that PFC is able to successfully prevent transmission disruption and packet loss events caused by traffic path changes, thus offering dynamic and lossless traffic control for SDN switches.

Wireless technology is the strongest contender for catering for the 5G backhaul (BH) stipulated performance, where optical fiber is unavailable. In the presence of ultra-dense networks, such occurrences are exponentially increasing, and different wireless technologies are investigated for this application. We present the first BH-specific wireless link performance modeling that considers its inherent line-of-sight nature, together with an appropriate representation of the network topology using stochastic geometry. To this end, novel tractable models are obtained to capture the performance of wireless BH links. These are integrated into a multi-hop hybrid BH performance modeling framework and are applied in the analysis of a BH-aware user association optimization problem.

This paper proposes a low-complexity hybrid beamforming design for multi-antenna communication systems. The hybrid beamformer is comprised of a baseband digital beamformer and a constant modulus analog beamformer in the radio frequency (RF) part of the system. As in singular-value-decomposition (SVD)-based beamforming, hybrid beamforming design aims to generate parallel data streams in multi-antenna systems, however, due to the constant modulus constraint of the analog beamformer, the problem cannot be solved similarly. To address this problem, mathematical expressions of the parallel data streams are derived in this paper and desired and interfering signals are specified per stream. The analog beamformers are designed by maximizing the power of desired signal while minimizing the sum-power of interfering signals. Finally, digital beamformers are derived by defining the equivalent channel observed by the transmitter/receiver. Regardless of the number of the antennas or type of channel, the proposed approach can be applied to a wide range of MIMO systems with hybrid structure wherein the number of the antennas is more than the number of the RF chains. In particular, the proposed algorithm is verified for sparse channels that emulate mm-wave transmission as well as rich scattering environments. In order to validate the optimality, the results are compared with those of the state-of-the-art and it is demonstrated that the performance of the proposed method outperforms state-of-the-art techniques, regardless of type of the channel and/or system configuration.

Due to dynamic wireless network conditions and heterogeneous mobile web content complexities, web-based content services in mobile network environments always suffer from long loading time. The new HTTP/2.0 protocol only adopts one single TCP connection, but recent research reveals that in real mobile environments, web downloading using single connection will experience long idle time and low bandwidth utilization, in particular with dynamic network conditions and web page characteristics. In this paper, by leveraging the Mobile Edge Computing (MEC) technique, we present the framework of Mobile Edge Hint (MEH), in order to enhance mobile web downloading performances. Specifically, the mobile edge collects and caches the meta-data of frequently visited web pages and also keeps monitoring the network conditions. Upon receiving requests on these popular webpages, the MEC server is able to hint back to the HTTP/2.0 clients on the optimized number of TCP connections that should be established for downloading the content. From the test results on real LTE testbed equipped with MEH, we observed up to 34.5% time reduction and in the median case the improvement is 20.5% compared to the plain over-the-top (OTT) HTTP/2.0 protocol.

In this paper, a novel low-complexity and spectrally efficient modulation scheme for visible light communication (VLC) is proposed. Our new spatial quadrature modulation (SQM) is designed to efficiently adapt traditional complex modulation schemes to VLC; i.e. converting multi-level quadrature amplitude modulation (M-QAM), to real-unipolar symbols, making it suitable for transmission over light intensity. The proposed SQM relies on the spatial domain to convey the orthogonality and polarity of the complex signals, rather than mapping bits to symbol as in existing spatial modulation (SM) schemes. The detailed symbol error analysis of SQM is derived and the derivation is validated with link level simulation results. Using simulation and derived results, we also provide a performance comparison between the proposed SQM and SM. Simulation results demonstrate that SQM could achieve a better symbol error rate (SER) and/or data rate performance compared to the state of the art in SM; for instance a Eb/Nogain of at least 5 dB at a SER of 10−4.

The Internet of Things (IoT) has become a new enabler for collecting real-world observation and measurement data from the physical world. The IoT allows objects with sensing and network capabilities (i.e. Things and devices) to communicate with one another and with other resources (e.g. services) on the digital world. The heterogeneity, dynamicity and ad-hoc nature of underlying data, and services published by most of IoT resources make accessing and processing the data and services a challenging task. The IoT demands distributed, scalable, and efficient indexing solutions for large-scale distributed IoT networks. We describe a novel distributed indexing approach for IoT resources and their published data. The index structure is constructed by encoding the locations of IoT resources into geohashes and then building a quadtree on the minimum bounding box of the geohash representations. This allows to aggregate resources with similar geohashes and reduce the size of the index. We have evaluated our proposed solution on a large-scale dataset and our results show that the proposed approach can efficiently index and enable discovery of the IoT resources with 65% better response time than a centralised approach and with a high success rate (around 90% in the first few attempts).

Dynamic network conditions are inevitable in wireless sensor networks (WSNs). In multihop WSN data collection scenarios, data traffic load becomes heavier closer to the data sink. Therefore, extensive research work has been devoted to duty cycle adaptation mechanisms to meet the time-varying and/or spatially non-uniform traffic loads. In this paper, we propose a new algorithm, called Control-theoretic approach for Duty Cycle Adaptation (CDCA), based on the non-beacon-enable mode of the IEEE 802.15.4 protocol. We consider a distributed controller to be run at each node which adapts its duty cycle to achieve energy efficiency and maximise packet delivery performance under variable traffic conditions. CDCA adjusts a sensor's duty cycle using a traffic estimation mechanism based on a novel concept called a virtual queue. This is determined based on the sensor's local queue length as well as explicit packet-drop indications collected from its child nodes in a multihop tree structure rooted at the network's data sink. Based on control theory, the proposed controller is analysed to derive system stability. Performance results obtained using Network Simulator 3 demonstrate that CDCA outperforms the most recent and relevant scheme by saving more energy while also significantly improving packet delivery ratio to the data sink.

In this paper, the design, construction, and testing of a fully-functional virtualised mobile core network is outlined. Lessons learned and recommendations for future improvements are provided. The presented work uses open-source software for virtual network function infrastructure control (OpenStack), network flow programming (OpenDaylight), and network orchestration (OpenBaton) to virtualise a commercial software evolved packet core solution deployed on common off-the-shelf hardware. The findings presented in this paper prove the concept of function virtualisation for mobile networks, and paves the way towards future mobile core network function flexibility as required for 5G networks. The paper provides researchers and network operators with first-hand experience to help build similar virtual mobile network infrastructures, and highlights the challenges to tackle and the issues to address to harness the power of virtualisation in 5G networks.

The fifth generation of mobile networks (5G) is maturing fast and the target year 2020 is around the corner. However, the realistic backhaul network may not be ready for 5G arrival as it is likely to converge to 5G requirements at a slower pace than the radio counterpart. In this work, we develop a method that identifies pertinent backhaul upgrade stages that are ranked based on their associated cost. First, the User-centric- backhaul (UCB) scheme is employed to reveal the bottlenecks of the incumbent backhaul network, as perceived by users and holistic network. A multi- hop hybrid backhaul modelling framework is then employed to quantify possible rectifications that would deliver the highest improvement at the lowest cost. These are implemented and the results are verified following another usage of UCB. A case study is presented that demonstrates the strength of this method in enabling an effective and cost efficient evolution road map towards the 5G backhaul.

Network slicing has been identified as one of the most important features for 5G and beyond to enable operators to utilize networks on an as-a-service basis and meet the wide range of use cases. In physical layer, the frequency and time resources are split into slices to cater for the services with individual optimal designs, resulting in services/slices having different baseband numerologies (e.g., subcarrier spacing) and / or radio frequency (RF) front-end configurations. In such a system, the multi-service signal multiplexing and isolation among the service/slices are critical for the Physical-Layer Network Slicing (PNS) since orthogonality is destroyed and significant inter-service/ slice-band-interference (ISBI) may be generated. In this paper, we first categorize four PNS cases according to the baseband and RF configurations among the slices. The system model is established by considering a low out of band emission (OoBE) waveform operating in the service/slice frequency band to mitigate the ISBI. The desired signal and interference for the two slices are derived. Consequently, one-tap channel equalization algorithms are proposed based on the derived model. The developed system models establish a framework for further interference analysis, ISBI cancelation algorithms, system design and parameter selection (e.g., guard band), to enable spectrum efficient network slicing.

The emergence of the Internet of Things (IoT) has led to the production of huge volumes of real-world streaming data. We need effective techniques to process IoT data streams and to gain insights and actionable information from real-world observations and measurements. Most existing approaches are application or domain dependent. We propose a method which determines how many different clusters can be found in a stream based on the data distribution. After selecting the number of clusters, we use an online clustering mechanism to cluster the incoming data from the streams. Our approach remains adaptive to drifts by adjusting itself as the data changes. We benchmark our approach against state-of-the-art stream clustering algorithms on data streams with data drift. We show how our method can be applied in a use case scenario involving near real-time traffic data. Our results allow to cluster, label, and interpret IoT data streams dynamically according to the data distribution. This enables to adaptively process large volumes of dynamic data online based on the current situation. We show how our method adapts itself to the changes. We demonstrate how the number of clusters in a real-world data stream can be determined by analyzing the data distributions.

Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results.

The Internet-of-Things (IoT) paradigm envisions billions of devices all connected to the Internet, generating low-rate monitoring and measurement data to be delivered to application servers or end-users. Recently, the possibility of applying in-network data caching techniques to IoT traffic flows has been discussed in research forums. The main challenge as opposed to the typically cached content at routers, e.g., multimedia files, is that IoT data are transient and therefore require different caching policies. In fact, the emerging location-based services can also benefit from new caching techniques that are specifically designed for small transient data. This paper studies in-network caching of transient data at content routers, considering a key temporal data property: data item lifetime. An analytical model that captures the trade-off between multihop communication costs and data item freshness is proposed. Simulation results demonstrate that caching transient data are a promising information-centric networking technique that can reduce the distance between content requesters and the location in the network where the content is fetched from. To the best of our knowledge, this is a pioneering research work aiming to systematically analyze the feasibility and benefit of using Internet routers to cache transient data generated by IoT applications.

Frequent handovers (HOs) in dense small cell deployment scenarios could lead to a dramatic increase in signaling overhead. This suggests a paradigm shift toward a signaling conscious cellular architecture with intelligent mobility management. In this direction, a futuristic radio access network with a logical separation between control and data planes has been proposed in research community. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This approach enables signaling efficient HO procedures since the control plane remains unchanged when the users move within the footprint of the same umbrella. Considering this configuration, we propose a core-network efficient radio resource control signaling scheme for active state HO and develop an analytical framework to evaluate its signaling load as a function of network density, user mobility, and session characteristics. In addition, we propose an intelligent HO prediction scheme with advance resource preparation in order to minimize the HO signaling latency. Numerical and simulation results show promising gains in terms of reduction in HO latency and signaling load as compared with conventional approaches.

Flexibly supporting multiple services, each with different communication requirements and frame structure, has been identified as one of the most significant and promising characteristics of next generation and beyond wireless communication systems. However, integrating multiple frame structures with different subcarrier spacing in one radio carrier may result in significant inter-service-band-interference (ISBI). In this paper, a framework for multi-service (MS) systems is established based on a subband filtered multi-carrier system. The subband filtering implementations and both asynchronous and generalized synchronous (GS) MS subband filtered multi-carrier (SFMC) systems have been proposed. Based on the GS-MS-SFMC system, the system model with ISBI is derived and a number of properties on ISBI are given. In addition, low-complexity ISBI cancelation algorithms are proposed by precoding the information symbols at the transmitter. For asynchronous MS-SFMC system in the presence of transceiver imperfections, including carrier frequency offset, timing offset, and phase noise, a complete analytical system model is established in terms of desired signal, inter-symbol-interference, inter-carrier-interference, ISBI, and noise. Thereafter, new channel equalization algorithms are proposed by considering the errors and imperfections. Numerical analysis shows that the analytical results match the simulation results, and the proposed ISBI cancelation and equalization algorithms can significantly improve the system performance in comparison with the existing algorithms.

The backhaul network is a critical challenge towards the success of 5G and corresponding difficulties are many-fold, such as network coverage expansion, very high bandwidth, ultra- low latency and energy consumption, at a minimum cost. No single backhaul solution can address all these requirements but, on the other hand, not all of the backhaul links require the same set of stringent requirements. To this end, we propose a novel scheme that capitalises on the diversity in both performance requirements and backhaul capabilities to maximise the system-centric as well as user-centric performance indicators. The user-centric backhaul provisioning scheme uses multiple attribute decision making (MADM) for the user-cell-backhaul association criteria in a way that intelligently associates users with available cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator of the end-to-end network performance such as capacity, latency, resilience, energy consumption, etc. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul capabilities that conform to the user requirements. Reinforcement learning is used by the radio cell to optimise the bias factors for each performance indicator in a way that maximises the system performance and users' end-to-end quality of experience (QoE). Preliminary results based on a case study show considerable improvement in users QoE when compared to state-of-the-art user-cell association schemes.

This work introduces MultiSphere, a method to massively parallelize the tree search of large sphere decoders in a nearly-independent manner, without compromising their maximum-likelihood performance, and by keeping the overall processing complexity at the levels of highly-optimized sequential sphere decoders. MultiSphere employs a novel sphere decoder tree partitioning which can adjust to the transmission channel with a small latency overhead. It also utilizes a new method to distribute nodes to parallel sphere decoders and a new tree traversal and enumeration strategy which minimize redundant computations despite the nearly-independent parallel processing of the subtrees. For an 8 × 8 MIMO spatially multiplexed system with 16-QAM modulation and 32 processing elements MultiSphere can achieve a latency reduction of more than an order of magnitude, approaching the processing latency of linear detection methods, while its overall complexity can be even smaller than the complexity of well-known sequential sphere decoders. For 8 × 8 MIMO systems, MultiSphere's sphere decoder tree partitioning method can achieve the processing latency of other partitioning schemes by using half of the processing elements. In addition, it is shown that for a multi-carrier system with 64 subcarriers, when performing sequential detection across subcarriers and using MultiSphere with 8 processing elements to parallelize detection, a smaller processing latency is achieved than when parallelizing the detection process by using a single processing element per subcarrier (64 in total).

A well known problem of regularization (diagonal loading) of the interference rejection combining (IRC) and IRC / maximum ratio combining (MRC) switching is addressed. Different empirical loading factor selection rules adjusted to specific scenarios have been introduced in the literature. It is expected that future network will be characterized by variety of scenarios, transmission modes, and receiver configurations. Empirical IRC regularization may not be suitable for such networks. In this study we consider the Expected Likelihood (EL) criterion for estimation/selection of the interference plus noise covariance matrix and demonstrate that it gives the diagonal loading selection rules that are effective in wide range of scenarios and receiver configurations.

Database-aided user association, where users are associated with data base stations (BSs) based on a database which stores their geographical location with signal-to-noise-ratio tagging, will play a vital role in the futuristic cellular architecture with separated control and data planes. However, such approach can lead to inaccurate user-data BS association, as a result of the inaccuracies in the positioning technique, thus leading to sub-optimal performance. In this paper, we investigate the impact of database-aided user association approach on the average spectral efficiency (ASE). We model the data plane base stations using its fluid model equivalent and derive the ASE for the channel model with pathloss only and when shadowing is incorporated. Our results show that the ASE in database-aided networks degrades as the accuracy of the user positioning technique decreases. Hence, system specifications for database-aided networks must take account of inaccuracies in positioning techniques.

Decentralized dynamic spectrum allocation (DSA) that exploit adaptive antenna array interference mitigation (IM) diversity at the receiver, is studied for interference-limited environments with high level of frequency reuse. The system consists of base stations (BSs) that can optimize uplink frequency allocation to their user equipments (UEs) to minimize impact of interference on the useful signal, assuming no control over band allocation of other BSs sharing the same bands. To this end, “good neighbor” (GN) rules allow effective trade off between the equilibrium and transient decentralized DSA behavior if the performance targets are adequate to the interference scenario. In this paper, we extend the GN rules by including a spectrum occupation control that allows adaptive selection of the performance targets corresponding to the potentially “interference free” DSA; define the semi-analytic absorbing Markov chain model for the GN DSA with occupation control and study the convergence properties including effects of possible breaks of the GN rules; and for higher-dimension networks, develop the simplified search GN algorithms with occupation and power control (PC) and demonstrate their efficiency by means of simulations in the scenario with unlimited requested network occupation.

Coping with the extreme growth of the number of users is one of the main challenges for the future IEEE 802.11 networks. The high interference level, along with the conventional standardized carrier sensing approaches, will degrade the network performance. To tackle these challenges, the Dynamic Sensitivity Control (DSC) and the BSS Color scheme are considered in IEEE 802.11ax and IEEE 802.11ah, respectively. The main purpose of these schemes is to enhance the network throughput and improve the spectrum efficiency in dense networks. In this paper, we evaluate the DSC and the BSS Color scheme along with the PARTIAL-AID (PAID) feature introduced in IEEE 802.11ac, in terms of throughput and fairness. We also, exploit the performance when the aforementioned techniques are combined. The simulations show a significant gain in total throughput when these techniques are applied.

To flexibly support diverse communication requirements (e.g., throughput, latency, massive connection, etc.) for the next generation wireless communications, one viable solution is to divide the system bandwidth into several service subbands, each for a different type of service. In such a multi-service (MS) system, each service has its optimal frame structure while the services are isolated by subband filtering. In this paper, a framework for multi-service (MS) system is established based on subband filtered multi-carrier (SFMC) modulation. We consider both single-rate (SR) and multi-rate (MR) signal processing as two different MS-SFMC implementations, each having different performance and computational complexity. By comparison, the SR system outperforms the MR system in terms of performance while the MR system has a significantly reduced computational complexity than the SR system. Numerical results show the effectiveness of our analysis and the proposed systems. These proposed SR and MR MS-SFMC systems provide guidelines for next generation wireless system frame structure optimization and algorithm design.

With the recent development of Device-to-Device (D2D) communication technologies, mobile devices will no longer be treated as pure “terminals”, but they could become an integral part of the network in specific application scenarios. In this paper, we introduce a novel scheme of using D2D communications for enabling data relay services in partial Not-Spots, where a client without local network access may require data relay by other devices. Depending on specific social application scenarios that can leverage on the D2D technology, we consider tailored algorithms in order to achieve optimised data relay service performance on top of our proposed network-coordinated communication framework. The approach is to exploit the network's knowledge on its local user mobility patterns in order to identify best helper devices participating in data relay operations. This framework also comes with our proposed helper selection optimization algorithm based on reactive predictability of individual user. According to our simulation analysis based on both theoretical mobility models and real human mobility data traces, the proposed scheme is able to flexibly support different service requirements in specific social application scenarios.

Multiuser selection scheduling concept has been recently proposed in the literature in order to increase the multiuser diversity gain and overcome the significant feedback requirements for the opportunistic scheduling schemes. The main idea is that reducing the feedback overhead saves per‐user power that could potentially be added for the data transmission. In this work, the authors propose to integrate the principle of multiuser selection and the proportional fair scheduling scheme. This is aimed especially at power‐limited, multi‐device systems in non‐identically distributed fading channels. For the performance analysis, they derive closed‐form expressions for the outage probabilities and the average system rate of the delay‐sensitive and the delay‐tolerant systems, respectively, and compare them with the full feedback multiuser diversity schemes. The discrete rate region is analytically presented, where the maximum average system rate can be obtained by properly choosing the number of partial devices. They optimise jointly the number of partial devices and the per‐device power saving in order to maximise the average system rate under the power requirement. Through the authors’ results, they finally demonstrate that the proposed scheme leveraging the saved feedback power to add for the data transmission can outperform the full feedback multiuser diversity, in non‐identical Rayleigh fading of devices’ channels.

Multiuser multiple-input multiple-output (MUMIMO) nonlinear precoding techniques face the problem of poor computational scalability to the size of the network. In this paper, the fundamental problem of MU-MIMO scalability is tackled through a novel signal-processing approach, which is called degree-2 vector perturbation (D2VP). Unlike the conventional VP approaches that aim at minimizing the transmit-to-receive energy ratio through searching over an N-dimensional Euclidean space, D2VP shares the same target through an iterative-optimization procedure. Each iteration performs vector perturbation over two optimally selected subspaces. By this means, the computational complexity is managed to be in the cubic order of the size of MU-MIMO, and this mainly comes from the inverse of the channel matrix. In terms of the performance, it is shown that D2VP offers comparable bit-error-rate to the sphere encoding approach for the case of small MU-MIMO. For the case of medium and large MU-MIMO when the sphere encoding does not apply due to unimplementable complexity, D2VP outperforms the lattice-reduction VP by around 5-10 dB in Eb/No and 10-50 dB in normalized computational complexity.

Energy efficiency is a key enabler for the next generation of communication systems. Equally, resource allocation and cooperative communication are effective techniques for improving the communication system performance. In this paper, we propose an optimal energy-efficient joint resource allocation method for the multi-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) system. We define the joint source and multiple relays optimization problem and prove that its objective function, which is not generally quasiconvex, can be lower bounded by a convex function. Moreover, all the minima of this objective function are strict minima. Based on these two properties, we then simplify the original multivariate optimization problem into a single variable problem and design a novel approach for optimally solving it in both the unconstraint and power constraint cases. In addition, we provide a sub-optimal approach with reduced complexity; the latter reduces the computational complexity by a factor of up to 40 with a near-optimal performance. We finally utilize our novel approach for comparing the optimal energy-per-bit consumption of multi-hop MIMO-AF and MIMO systems; the results indicate that MIMO-AF can help to save energy when the direct link quality is poor.

Abstract This paper presents a comprehensive study of the performance of routing protocols in distributed vehicular networks. We propose a novel and efficient routing protocol, namely cross‐layer, weighted, position‐based routing, which considers link quality, mobility and utilisation of nodes in a cross layer manner to make effective position‐based forwarding decisions. An analytic hierarchy process approach is utilised to combine multiple decision criteria into a single weighting function and to perform a comparative evaluation of the effects of aforementioned criteria on forwarding decisions. Comprehensive simulations are performed in realistic representative urban scenarios with synthetic and real traffic. Insights on the effect of different communication and mobility parameters are obtained. The results demonstrate that the proposed protocol outperforms existing routing protocols for vehicular ad hoc networks, including European Telecommunications Standards Institute (ETSI's) proposed greedy routing protocol, greedy traffic aware routing protocol and advanced greedy forwarding in terms of combined packet delivery ratio, end‐to‐end delay and overhead. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract In future heterogeneous cellular networks, cognitive radio compatible with device to device communication technique can be an aid to further enhance system spectral and energy efficiency. The unlicensed smart devices (SDs) are allowed to detect the available licensed spectrum and utilise the spectrum resource which is detected as not being used by the licensed users. In this work, we propose such a system and provide comprehensive analysis of the effect of selection of SDs' frame structure on the energy efficiency, throughput and interference. Moreover, uplink power control strategy is also considered where the licensed users and SDs adapt the transmit power based on the distance from their reference receivers. The optimal frame structure with power control is investigated under high‐signal‐to‐noise ratio (SNR) and low‐SNR network environments. The impact of power control and optimal sensing time and frame length, on the achievable energy efficiency, throughput and interference are illustrated and analysed by simulation results. It has been also shown that the optimal sensing time and frame length which maximizes the energy efficiency of SDs strictly depends on the power control factor employed in the underlying network such that the considered power control strategy may decrease the energy efficiency of SDs under very low‐SNR regime. Copyright © 2016 John Wiley & Sons, Ltd.

Fingerprint-based indoor positioning technique is one of sustainable approaches to provide highly accurate information about mobile user's location. However, it requires the constructing of a high-resolution radio map, which is time consuming and labor intensive. In this paper, a novel less-calibration Wi-Fi-based indoor positioning is proposed. The new technique takes advantage of regular mobile users' movement inside the environment to build the radio map. During these movements mobile users can measure and record received signal strength (RSS) from available wireless access points. The radio map will be adapted to environment changes using the recent collected RSSs data. The proposed algorithm does not use extra hardware (e.g., accelerometer, magnetometer) to exploit the collected RSSs. Experimental results showed that the proposed technique can be used to build the radio map with surveying only one-third of the area of interest (about two-third of the effort is reduced). The achieved localization error and location precision are competitive with the current proposed solutions.

In research community, a new radio access network architecture with a logical separation between control plane (CP) and data plane (DP) has been proposed for future cellular systems. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This configuration could provide significant savings in signalling overhead. In particular, mobility robustness with minimal handover (HO) signalling is considered as one of the most promising benefits of this architecture. However, the DP mobility remains an issue that needs to be investigated. We consider predictive DP HO management as a solution that could minimise the out-of-band signalling related to the HO procedure. Thus we propose a mobility prediction scheme based on Markov Chains. The developed model predicts the user's trajectory in terms of a HO sequence in order to minimise the interruption time and the associated signalling when the HO is triggered. Depending on the prediction accuracy, numerical results show that the predictive HO management strategy could significantly reduce the signalling cost as compared with the conventional non-predictive mechanism.

Most of the wireless systems such as the long term evolution (LTE) adopt a pilot symbol-aided channel estimation approach for data detection purposes. In this technique, some of the transmission resources are allocated to common pilot signals which constitute a significant overhead in current standards. This can be traced to the worst-case design approach adopted in these systems where the pilot spacing is chosen based on extreme condition assumptions. This suggests extending the set of the parameters that can be adaptively adjusted to include the pilot density. In this paper, we propose an adaptive pilot pattern scheme that depends on estimating the channel correlation. A new system architecture with a logical separation between control and data planes is considered and orthogonal frequency division multiplexing (OFDM) is chosen as the access technique. Simulation results show that the proposed scheme can provide a significant saving of the LTE pilot overhead with a marginal performance penalty.

In this paper, we investigate the optimal inter-frequency small cell discovery (ISCD) periodicity for small cells deployed on carrier frequency other than that of the serving macro cell. We consider that the small cells and user terminals (UTs) positions are modelled according to a homogeneous Poisson Point Process (PPP). We utilize polynomial curve fitting to approximate the percentage of time the typical UT missed small cell offloading opportunity, for a fixed small cell density and fixed UT speed. We then derive analytically, the optimal ISCD periodicity that minimizes the average UT energy consumption (EC). Furthermore, we also derive the optimal ISCD periodicity that maximizes the average energy efficiency (EE), i.e. bit-per-joule capacity. Results show that the EC optimal ISCD periodicity always exceeds the EE optimal ISCD periodicity, with the exception of when the average ergodic rates in both tiers are equal, in which the optimal ISCD periodicity in both cases also becomes equal.

Security is of paramount importance for vehicular communications to verify regular updates from authorized vehicles while detecting misleading reports from malicious or malfunctioning on board units (OBUs). This should however be provided without violating the privacy of the users. Using frequently changing pseudonyms for the vehicles is an effective solution to preserve their privacy. However, in current systems the vehicles need to be always connected to the Certification Authority (CA) to receive the pseudonyms. This might be unfeasible due to the sparse infrastructure deployment, especially in the early stage of the technology, that can lead to availability and scalability problems. In this paper, we solve this issue by proposing efficient protocols for authentication, pseudonym generation, message verification and anonymity revocation that do not require permanent contact with the CA. Even in case of network availability, this feature also reduces the communication overhead for the pseudonym generation and revocation list distribution.

A specific characteristic for the isolator circuits used in small form-factor in-band full-duplex transceivers, used to attenuate the leakage of the transmit signal to the receive path (self-interference), is nonuniform performance over the wide frequency band as these circuits must tune at the operating frequency. Moreover active cancellation circuits are required in addition to the isolation circuit to attenuate the self-interference further. In this work an RF self-interference cancellation is proposed to attenuate further the remaining self-interference, on the top of achieved isolation, uniformly over the wide band. This approach divides the wideband signal into a number of narrower subbands and then self-interference cancellation is performed at each subband separately. This is an alternate approach to the previously proposed delay and attenuation network active RF self-interference cancellation. Simulation results indicate significant improvements in uniformly self-interference cancellation and system performance which is achieved in the expense of moderate extra loss.

An attractive hybrid method of mitigating the effects of the residual self-interference imposed by the signal propagation and analog/digital circuit non-idealities for Full-Duplex (FD) point-to-point multiple-input multiple-output (MIMO) systems is proposed. Furthermore, the effect of channel estimation errors on system performance is considered. The simulation results demonstrate that our proposed cancellation technique for FD systems achieve a significant gain over traditional Half-Duplex (HD) systems. For example, the sum rate of our proposed scheme of FD MIMO system is approximately 1.8 times higher than that of the HD transmission.

With the advent of device-to-device (D2D) communications, user equipment (UE) such as smart phones will become an integral part of the (mobile) network for content distribution operations. In this context, we introduce a novel Information centric networking (ICN) framework for mobile content caching and distribution based on direct D2D communications in cellular network environments. Specifically, small pieces of mobile content can be cached at incentivised mobile UEs known as helpers, and the ICN-aware cellular network edge (e.g. base stations) are able to resolve content requests from local mobile clients to the helpers in their D2D proximity. With simple POI (point of interests) based selection of helpers as well as content caching/eviction control at the base station side, a significant proportion of mobile content requests can be locally resolved to helpers in proximity of clients, thus achieving very effective content traffic offloading away from the cellular network infrastructure.

Full-duplex transceivers enable transmission and reception at the same time on the same frequency, and have the potential to double the wireless system spectral efficiency. Recent studies have shown the feasibility of full-duplex transceivers. In this paper, we address the radio resource allocation problem for full-duplex system. Due to the self-interference and inter-user interference, the problem is coupled between uplink and downlink channels, and can be formulated as joint uplink and downlink sum-rate maximization. As the problem is non-convex, an iterative algorithm is proposed based on game theory by modelling the problem as a noncooperative game between the uplink and downlink channels. The algorithm iteratively carries out optimal uplink and downlink resource allocation until a Nash equilibrium is achieved. Simulation results show that the algorithm achieves fast convergence, and can significantly improve the full-duplex performance comparing to the equal resource allocation approach. Furthermore, the full-duplex system with the proposed algorithm can achieve considerable gains in spectral efficiency, that reach up to 40%, comparing to half-duplex system.

Information paths in current communication networks can often be modelled by set of serial links interconnected by intermediate nodes. These kind of scenarios are called line networks. If the links between nodes experience connection problems, burst erasures of information can happen. In this article, hybrid serial concatenated network codes are proposed, which consist in the serial concatenation of a 'classical' coding scheme and a network code. A novel way to decrease complexity of this approach is that the 'classical' coding scheme is not performed on a link level. The new coding schemes improve performance of the communications in line networks in terms of error- correcting capability. The evaluations involve MATLAB simulations and analytical analysis.

Device-to-device (D2D) communication is being considered an important traffic offloading mechanism for future cellular networks. Coupled with pro-active device caching, it offers huge potential for capacity and coverage enhancements. In order to ensure maximum capacity enhancement, number of nodes for direct communication needs to be identified. In this paper, we derive analytic expression that relates number of D2D nodes (i.e., D2D user density) and average coverage probability of reference D2D receiver. Using stochastic geometry and poisson point process, we introduce retention probability within cooperation region and shortest distance based selection criterion to precisely quantify interference due to D2D pairs in coverage area. The simulation setup and numerical evaluation validates the closed-form expression.

This study investigates energy efficient allocation of radio resource blocks in orthogonal frequency division multiple access (OFDMA) systems while considering the status of the users’ data buffers to reduce packet dropping rate by the downlink scheduler. The proposed scheme exploits the fluctuations of traffic load to efficiently schedule users’ data packets by reducing the overall energy consumption of the system whenever the status of data buffers permits. From information theory point of view, the proposed scheme exploits the fundamental trade‐off between energy efficiency and spectral efficiency to perform scheduling. First, the problem is formulated as an optimisation problem and then a novel solution, based on dynamic programming, is applied. By comparing the analytical solution with the ones obtained by exhaustive search, it is demonstrated that the proposed scheme is close to the optimal solution, with low computational complexity. In addition, comprehensive simulations are conducted to evaluate the performance of the suggested algorithm. Both analytical and simulation results demonstrate the superiority of the proposed algorithm compared with the well‐known benchmark schemes in terms of energy efficiency and packet dropping rate.

Abstract Cognitive radio is envisaged as a promising solution to cope with the problem of spectrum scarcity. In cognitive radio networks, users can sense the medium and opportunistically use available frequency bands. Users can cooperate in order to increase the reliability of the sensing process, which is called cooperative spectrum sensing (CSS). However, cooperative paradigms are threatened by the behavior of malicious users. Two types of attacks represent the main threats for CSS network operation, namely, spectrum sensing data falsification (SSDF) and incumbent emulation (IE). These two types of attacks have received a considerable amount of attention in the literature, but they have always been studied separately. In this paper, we propose a novel mechanism based on lightweight cryptography that considers both SSDF and IE attacks combined in a CSS network for the first time. Lightweight cryptography, in contrast to previous techniques used (such as intrusion detection or reputation systems), provides higher resilience to such attacks when a high number of mobile malicious users exist, providing better energy efficiency. Analytical and simulation results show the outperformance of the proposed algorithm compared with previous mechanisms, in terms of lower false alarm probability (i.e., higher chance of using the free frequency bands) and hence better energy ratings. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, the capacity of OFDM/OQAM with isotropic orthogonal transfer algorithm (IOTA) pulse shaping is evaluated through information theoretic analysis. In the conventional OFDM systems the insertion of a cyclic prefix (CP) decreases the system's spectral efficiency. As an alternative to OFDM, filter bank based multicarrier systems adopt proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. We evaluate the spectral efficiency of OFDM/OQAM systems with IOTA pulse shaping in comparison with conventional OFDM/QAM systems, and our analytical model is further extended in order to gain insights into the effect of utilizing the intrinsic interference on the performance of our system. Furthermore, the spectral efficiency of OFDM/OQAM systems is analyzed when the effect of inter-symbol and inter-carrier interference is considered.

This paper proposes a low-complexity joint source and relay energy-efficient resource allocation scheme for the two-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) system when channel state information is available. We first simplify the multivariate unconstrained energy efficiency (EE)-based problem and derive a convex closed-form approximation of its objective function as well as closed-form expressions of subchannel rates in both the unconstrained and power constraint cases. We then rely on these expressions for designing a low-complexity energy-efficient joint resource allocation algorithm. Our approach has been compared with a generic nonlinear constrained optimization solver and results have indicated the low-complexity and accuracy of our approach. As an application, we have also compared our EE-based approach against the optimal spectral efficiency (SE)-based joint resource allocation approach and results have shown that our EE-based approach provides a good trade-off between power consumption and SE.

In this paper, we consider Coordinated Multi-Point (CoMP) in a cloud-based radio access network (RAN), where each coordinating access point compresses its observation using distributed Wyner-Ziv compression and forwards the compressed signal to the receiving processing unit. We map this architecture to the multiple-relay compress-and-forward (CF) problem, derive the achievable rate of such a system and then show that the achievable rate can be maximized by optimizing the distributed compression rate at each individual coordinating point in a joint manner. An iterative optimization algorithm is proposed and numerical results indicate that compared with other coordinating schemes, when the channel between the coordinating points and the processing unit is strong, using distributed compression can effectively improve the spectrum efficiency.

Hierarchical ad hoc networks have gained popularity due to the huge number of mobile devices, as well as the social content distribution which is frequently location based. One major issue of hierarchical networks due to mobility is the overlapping of clusters. The issue is often resolved by merging of overlapping clusters. Some efforts propose immediate merging of clusters, while others suggest contention intervals before merging without detailed analysis of the length of such intervals. The literature lacks a thorough study of the efficiency of merging procedures. This paper represents a comprehensive study of the merging procedures. The paper derives an analytical model to compare the merging procedure with and without applying contention intervals. The effect of varying the length of contention intervals on the cluster stability and energy efficiency is also studied. The results show that adopting contention intervals is not recommended. Instead, to achieve the best energy efficiency when two clusters overlap, the clusters should either merge immediately or avoid merging, depending on the characteristics of the scenario. The analytical results are supported with simulations.

Cooperative communications have been gaining huge attention lately, due to the increase in the number of mobile devices and the advancement in their capabilities. In this framework, a commonly suggested approach to benefit from cooperation is the formation of virtual groups of mobile terminals. Mobility-aware algorithms are commonly proposed, where nodes form cooperative clusters according their basic mobile characteristics. However, more detailed mobility pattern information is not used to improve the performance of these systems. The reason is the complexity of current mobility pattern recognition algorithms that require previous set up and advanced knowledge on the scenario. This paper presents a novel cognitive algorithm for the identification of mobility patterns that can be used for mobile group formation and that requires no configuration. The proposed algorithm, based on data mining techniques and cooperative optimization, can evaluate the number of mobility patterns in the scenario and classify nodes according these patterns in a distributive fashion. The algorithm is mathematically presented and tested with extended simulations in both Matlab and ns-2.

Node clustering has been widely studied in recent years for Wireless Sensor Networks (WSN) as a technique to form a hierarchical structure and prolong network lifetime by reducing the number of packet transmissions. Cluster Heads (CH) are elected in a distributed way among sensors, but are often highly overloaded, and therefore re-clustering operations should be performed to share the resource intensive CH-role. Existing protocols involve periodic network-wide re-clustering operations that are simultaneously performed, which requires global time synchronisation. To address this issue, some recent studies have proposed asynchronous node clustering for networks with direct links from CHs to the data sink. However, for large-scale WSNs, multihop packet delivery to the sink is required since longrange transmissions are costly for sensor nodes. In this paper, we present an asynchronous node clustering protocol designed for multihop WSNs, considering dynamic conditions such as residual node energy levels and unbalanced data traffic loads caused by packet forwarding. Simulation results demonstrate that it is possible to achieve similar levels of lifetime extension by re-clustering a multihop WSN via independently made decisions at CHs, without a need for time synchronisation required by existing synchronous protocols.

The recent forecast of billions of devices, all connected to the Internet and generating low-rate monitoring, measurement, or automation data that many end-users/applications frequently request, signifies the need for applying in-network caching techniques to Internet-of-Things (IoT) traffic. Although time delay is not critically important for small-sized IoT content, the expected total traffic load on the Internet from a large number of devices is significant. However, the main challenge as opposed to the typically cached content at content routers, e.g. multimedia files, is that IoT data are transient and therefore require different caching policies. This paper studies in-network caching of IoT data at content routers in the Internet. An IoT data item is uniquely defined not only by its time and location tags, but also a time-range value set by end-users/applications. We provide a model for the trade-off between multihop communication costs and the freshness of a transient data item. Results show that the model can successfully capture the effect of data transiency and can accurately represent the expected gains of a caching system: considerable savings in terms of reduction of network load, especially for highly requested data items.

The diffusion of mobile devices, that are capable to play videos, opened new challenges for video streaming applications. Since wireless communications are prone to erasures, an important goal is to research possible ways to reliably provide video content. Fountain codes are an efficient way to protect video streaming against erasures: in particular, burst erasures are the ones that can significantly reduce the quality of the transmitted video. In this work, Luby codes for burst erasure channels are investigated. Our main focus is on the design of Luby codes to make the receiver able to recover all the lost information without any retransmission. Furthermore, we analytically demonstrate that lower energy consumption is achievable by using LT codes instead of a general retransmission scheme.

This paper studies the performance of network coding (NC) based system, especially looking at the implications in surveillance applications. We argue that NC can provide advantages from a data throughput perspective; however, an analysis of NC mechanism, especially when there are erroneous channels in the network, is required. We study the performance of the NC mechanism based on several parameters for different scenarios categorized by the error-free and erroneous communication channels that introduce random errors either at packet or at symbol level.

This paper brings to light the reasoning and principles shaping the standardization direction in the 3rd Generation Partnership Project (3GPP) regarding provision of low complexity Machine Type Communication (MTC), via Long Term Evolution (LTE) of 3GPP. It elaborates 3GPP's approaches and suggestions towards addressing the physical layer challenges and discusses solutions 3GPP proposed specifically in relation to the low complexity operation of MTC devices and the coverage extension of current cellular networks to challenging scenarios of MTC. Results of comprehensive set of simulations are presented to assess the impact of repetition coding and power boosting on the physical downlink shared channel (PDSCH). In addition, novel ideas are presented to reduce the complexity of channel estimation, one of the most computation intensive blocks in the baseband receive chain. These results all leap towards finding the physical layer design for low cost MTC devices via 3GPP LTE.

Interference forwarding has been shown to be beneficial in the interference channel with a relay as it enlarges the strong interference region, allowing the decoding of the interference at the receivers for larger ranges of the channel gains. In this work we demonstrate the benefit of adding a relay to the cognitive interference channel. We pay special attention to the effect of interference forwarding in this configuration. Two setups are presented. In the first, the interference forwarded by the relay is the primary user's signal, and in the second, this is the cognitive user's signal. We characterise the capacity regions of these two models in the case of strong interference. We show that as opposed to the first setup, in the second setup the capacity region is enlarged, compared to the capacity region of the cognitive interference channel, when the relay does not help the intended receiver.

This study proposes a performance analysis of the measurements mechanism applicable to the terminal controlled cell reselection algorithm in a mobile cellular communication system. The cell reselection mechanism decides on which cell, a user equipment (UE) is camped on, when it is in the idle mode. The analysis demonstrates the impact of the neighbour cells measurement threshold setting during the reselection on the serving cell pilot channel quality and a measurement effort in the UE, in terms of the time spent measuring neighbour cells. The authors demonstrate that there is a trade‐off between the cell reselection performance, in terms of the signal quality during the mobility and the battery life in the mobile terminal with different system configurations. Furthermore, a novel approach and enhanced load based measurement threshold configuration scheme is proposed, in order to improve this trade‐off and also the overall system performance and the user experience. Performance analysis results are given in different scenarios to demonstrate the efficacy of the proposed scheme.

A novel method for a mobile phone centric observation of a user's facing direction is presented. To estimate this direction, our proposed technique exploits the acceleration pattern that can be measured by a smartphone as the user is walking. For an accurate analysis of the acceleration pattern, the proposed approach benefits from a new trigonometric interpolation scheme. Our algorithm is independent of the initial orientation of the device and is adaptable to various wearing positions on a user's body, which gives the user a larger degree of freedom. A detailed description of the algorithm, which has been customized for a trouser pocket is presented. In addition, complementary hints for adaptation of the algorithm to other wearing positions along with an example of chest pocket position are provided. We have evaluated a prototype implementation of our algorithm on a smartphone, through several field experiments. It has been observed that our algorithm outperforms the conventional GPS and PCA-based techniques in terms of accuracy, reliability and energy consumption. The results also show that our approach has been able to handle the sudden variations of the user's direction. We have further incorporated our algorithm into a dead-reckoning application as an example of its real-world utility.

Despite years of physical-layer research, the capacity enhancement potential of relays is limited by the additional spectrum required for Base Station (BS)-Relay Station (RS) links. This paper presents a novel distributed solution by exploiting a system level perspective instead. Building on a realistic system model with impromptu RS deployments, we develop an analytical framework for tilt optimization that can dynamically maximize spectral efficiency of both the BS-RS and BS-user links in an online manner. To obtain a distributed self-organizing solution, the large scale system-wide optimization problem is decomposed into local small scale subproblems by applying the design principles of self-organization in biological systems. The local subproblems are non-convex, but having a very small scale, can be solved via standard nonlinear optimization techniques such as sequential quadratic programming. The performance of the developed solution is evaluated through extensive simulations for an LTE-A type system and compared against a number of benchmarks including a centralized solution obtained via brute force, that also gives an upper bound to assess the optimality gap. Results show that the proposed solution can enhance average spectral efficiency by up to 50% compared to fixed tilting, with negligible signaling overheads. The key advantage of the proposed solution is its potential for autonomous and distributed implementation.

This paper examines the uplink of cellular systems employing base station cooperation for joint signal processing. We consider clustered cooperation and investigate effective techniques for managing inter-cluster interference to improve users' performance in terms of both spectral and energy efficiency. We use information theoretic analysis to establish general closed form expressions for the system achievable sum rate and the users' Bit-per-Joule capacity while adopting a realistic user device power consumption model. Two main inter-cluster interference management approaches are identified and studied, i.e., through: 1) spectrum re-use; and 2) users' power control. For the former case, we show that isolating clusters by orthogonal resource allocation is the best strategy. For the latter case, we introduce a mathematically tractable user power control scheme and observe that a green opportunistic transmission strategy can significantly reduce the adverse effects of inter-cluster interference while exploiting the benefits from cooperation. To compare the different approaches in the context of real-world systems and evaluate the effect of key design parameters on the users' energy-spectral efficiency relationship, we fit the analytical expressions into a practical macrocell scenario. Our results demonstrate that significant improvement in terms of both energy and spectral efficiency can be achieved by energy-aware interference management.

Relay with opportunistic channel assignment (OCA) is an intermediate wireless node, which forwards messages through temporarily unused licensed channels. The spectral efficiency of OCA relay largely depends on the channel usage. This paper shows that, with a practical model of channel usage, the OCA relay outperforms the relay with fixed channel assignment by up to 0.8 bits/s/Hz/user. This interesting result is observed through our extensive investigation of the demodulation-and-forward relay adopting various resource allocation algorithms, which include the round-robin, joint channel and power allocation, best-user selection, and combinations of them. Moreover, the communication delay of all the considered OCA relaying schemes is evaluated through Monte Carlo simulations.

In this paper a multiple-input multiple-output (MIMO) cognitive system is investigated. In this setting the receivers in the primary user (PU) group null interference with the application of zero-forcing (ZF) filters and the transmit signals are adapted to meet individual rate constraints. The objective is to maximize the rate of the secondary user (SU), subject to a SU power constraint and individual PU rate constraints. Since the PU receiver cooperates with the SU by actively reserving part of its receive space for interference, the problem critically depends on the feasibility of the PU ZF operation and this in turn translates into a restriction on the number of SU transmit antennas. To investigate the behavior of the system in terms of SU rate and PU power sacrifice a SU transmit antenna selection method is proposed. It is demonstrated that PU cooperation enables SU communication in situations where transmit zero-forcing beamforming or opportunistic interference alignment (OIA) remains infeasible. In addition, simulation results highlight the advantage of PU cooperation over OIA in situations where the SU only just has enough transmit antennas to perform OIA.

Nodes in Wireless Sensor Networks (WSNs) are becoming more and more complex systems with the capabilities to run distributed structured applications. Which single task should be implemented by each WSN node needs to be decided by the application deployment strategy by taking into account both network lifetime and execution time requirements. In this paper, we propose an adaptive decentralised algorithm based on noncooperative game theory, where neighbouring nodes negotiate among each other to maximize their utility function. We then prove that an increment of the nodes utility corresponds to the same increment of the utility for the whole network. Simulation results show significant performance improvement with respect to existing algorithms.

Energy efficiency (EE) is becoming an important performance indicator for ensuring both the economical and environmental sustainability of the next generation of communication networks. Equally, cooperative communication is an effective way of improving communication system performances. In this paper, we propose a near-optimal energy-efficient joint resource allocation algorithm for multi-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) systems. We first show how to simplify the multivariate unconstrained EE-based problem, based on the fact that this problem has a unique optimal solution, and then solve it by means of a low-complexity algorithm. We compare our approach with classic optimization tools in terms of energy efficiency as well as complexity, and results indicate the near-optimality and low-complexity of our approach. As an application, we use our approach to compare the EE of multi-hop MIMO-AF with MIMO systems and our results show that the former outperforms the latter mainly when the direct link quality is poor.

The continuous increase in the energy consumption of wireless networks has led to extensive research and development into energy-efficient communications. Towards this objective, this paper employs a novel technique for maximizing the energy efficiency (EE) of wireless networks, using weighted average EE contours with multiple decoding policies (DPs), where users are prioritized based on different criteria such as channel condition. Moreover, our EE based resource allocation method is extended such that other system targets such as rate-fairness and quality of service (QoS) are satisfied. Results indicate that our EE-based resource allocation scheme achieves the highest EE when DP 2 is employed, i.e. the user with the best channel gain achieves its single user bound, whilst other users experience residual interference. Moreover, both the fairness and QoS constraints increase user satisfaction, in terms of achievable data rate, which comes at the cost of a higher transmit power, and therefore lower EE.

Consider spatial interweave opportunistic spectrum reuse scheme, primary user with power constraints maximizes its achievable rate by water-filling its power on singular values of its channel links and leaving some eigen modes free to use. Therefore, secondary user can transmit using this free subspace in order to avoid interference at primary receiver. By employing transmit beamforming, secondary user can design its precoding matrix, where the interference to primary user can be aligned to the free subspace. As a result of non-degraded primary user's performance, perfect channel state information (CSI) is required at secondary transmitter. Unfortunately, this kind of CSI is hard to realize, it is therefore in this paper to investigate a limited CSI scheme. By secondary transmitter sensing the label of the Grassmannian quantized channel vectors, the proposed criterion can upper-bound the overall interference at primary receiver with a constant value, which is independent of secondary transmit power. We also study an alternative primary user power allocation strategy in order to balance the overall system performance.

We present a distributed energy-efficient Inter-Cell Interference Coordination (ICIC) scheme for multi- cell HetNets (Heterogeneous Networks) in the downlink. This novel scheme aims to maximize the user fairness performance of the overall system as well as minimizing the total average transmit power at the base station side. We consider a realistic power model to characterize the power consumed (including circuit and transmitted power) at the base station side. We define an energy efficient performance metric in bits/Joule and the user fairness as the 5th percentile of the average user throughput. The proposed scheme is divided into the following three stages: dominant interferences classification, inter-cell radio resource allocation and intra-cell power control. The inter-cell radio resource allocation is formulated as a multidimensional knapsack problem. In order to satisfy the complexity requirements associated with multi-cell networks, we simplify, relax and decompose the original problem into a main master problem and multiple sub-problems. Our simulation results show the proposed scheme significantly increases both the user fairness and the energy efficiency (EE) of the overall network.

Emerging low date-rate Machine-to-Machine (M2M) communications call for promising Hybrid Automatic Repeat-reQuest (HARQ) schemes with improved link reliability and feedback efficiency. This motivates us to develop a novel adaptive HARQ scheme by exploiting the knowledge of Channel Quality Information (CQI), which can be obtained through either channel estimation techniques or location- aided technology. The proposed adaptive HARQ scheme will determine the suitable transmission mode to guarantee a specific Frame Error Rate (FER) during the first transmission with the aid of location information. Simulation results demonstrated that our proposed scheme can achieve more attractive throughput performance, while maintaining a similar FER, compared to conventional HARQ schemes.

In heterogeneous networks where multiple access networks are used, the resource management will be an important role to utilize wireless resources more effectively. We consider a base station that provides multiple access networks with different carrier frequencies and uses Adaptive Modulation and Coding scheme (AMC) based on the channel quality to communicate with the users at each access network. In order to maximize the wireless resource utilization with the guarantee of Quality of Service (QoS) for each user, the resource management in the base station needs to make various decisions taking into account of the state of wireless resource utilization and the users' behaviors. In this paper, we introduce a joint resource management which performs admission control, access network selection and vertical handover decision jointly and propose its optimal algorithm. The problem is naturally formulated as a Semi-Markov Decision Process (SMDP) and the optimal policy at each state is derived via reinforcement learning. The performance evaluation shows that the proposed algorithm outperforms other three algorithms in terms of the blocking probability and the system throughput.

Energy efficiency (EE) is undoubtedly an important criterion for designing power-limited systems, and yet in a context of global energy saving, its relevance for power-unlimited systems is steadily growing. Equally, resource allocation is a well-known method for improving the performance of cellular systems. In this paper, we propose an EE optimization framework for the downlink of planar cellular systems over frequency-selective channels. Relying on this framework, we design two novel low-complexity resource allocation algorithms for the single-cell and coordinated multi-cell scenarios, which are EE-optimal and EE-suboptimal, respectively. We then utilize our algorithms for comparing the EE performance of the classic non-coordinated, orthogonal and coordinated multi-cell approaches in realistic power and system settings. Our results show that coordination can be a simple and effective method for improving the EE of cellular systems, especially for medium to large cell sizes. Indeed, by using a coordinated rather than a non-coordinated resource allocation approach, the per-sector energy consumption and transmit power can be reduced by up to 15% and more than 90%, respectively.

This paper presents a novel pilot-assisted spectrum sensing technique for orthogonal frequency-division multiplexing (OFDM) systems. The main idea is based upon the physical nature that subcarriers carrying pilots or payload data have different first-order and second-order statistical properties. These differences vanish when the spectrum of interest is unoccupied. Therefore, the decision of spectrum availability can be formed based upon these differences, which can be explored through employment of frequency-domain differential operations. Thanks to the differential operations, the proposed technique has less sensitivity of the noise power uncertainty problem caused by imperfect hardware. Performance of the proposed technique is analytically formulated in terms of probability of false alarm (PFA) and probability of detection (PD). Computer simulations are carried out to elaborate the analytical results. It is shown that the second-order statistics based proposed technique outperforms the conventional pilot-assisted technique up to 7 dB. Moreover, it is shown that the first-order statistics based proposed technique outperforms the second-order statistics based proposed technique for small normalized Doppler shifts (≤ 0.013). However, the second-order statistics based proposed technique offers better performance for larger normalized Doppler shifts.

This paper presents a novel joint rate adaptation and relay selection scheme for multi-relay networks adopting half-duplex best-relay decode-and-forward protocol. The proposed scheme aims to maximize the overall transmission rate when relays are allowed to forward messages using different rates from the source. It is shown that the proposed scheme outperforms the conventional adaptive scheme in terms of the spectral efficiency (e.g. by 10.1% improvement for SNR=10 dB). Furthermore, in order to reduce signaling overhead of the proposed scheme, a number of joint discrete-rate adaptation and relay selection approaches are proposed for both non-reciprocal and reciprocal channels. The relay selection is basically a two-stage scheme. At the rm first stage, a set of relays are selected based on mixed channel quality information (CQI), i.e., the knowledge of CQI varies for different links; at the second stage, the best relay within the set is selected based on instantaneous CQI, which is obtained through carefully designed signaling protocols. It is shown that the proposed discrete-rate adaptation schemes can offer comparable spectral efficiency to the conventional adaptive scheme with significantly reduced signaling overhead.

In this paper, we evaluate the performance of Multicarrier-Low Density Spreading Multiple Access (MC-LDSMA) as a multiple access technique for mobile communication systems. The MC-LDSMA technique is compared with current multiple access techniques, OFDMA and SC-FDMA. The performance is evaluated in terms of cubic metric, block error rate, spectral efficiency and fairness. The aim is to investigate the expected gains of using MC-LDSMA in the uplink for next generation cellular systems. The simulation results of the link and system-level performance evaluation show that MC-LDSMA has significant performance improvements over SC-FDMA and OFDMA. It is shown that using MC-LDSMA can considerably reduce the required transmission power and increase the spectral efficiency and fairness among the users.

Many method has been applied previously to improve the fairness of a wireless communication system. In this paper, we propose using hybrid schemes, where more than one transmission scheme are used in one system, to achieve this objective. These schemes consist of cooperative transmission schemes, maximal ratio transmission and interference alignment, and non-cooperative schemes, orthogonal and non-orthogonal schemes used alongside and in combinations in the same system to improve the fairness. We provide different weight calculation methods to vary the output of the fairness problem. We show the solution of the radio resource allocation problem for the transmission schemes used. Finally, simulation results is provided to show fairness achieved, in terms of Jain's fairness index, by applying the hybrid schemes proposed and the different weight calculation methods at different inter-site distances.

Hybrid systems, where more than one transmission scheme are used within the same cluster, can be used as a way to improve spectral efficiency for the system as a whole and, more importantly, for the cell-edge users. In this paper, we will propose frequency reuse method by grouping the users into two groups, critical and non-critical users. Each user group is served with a transmission scheme, where the most vulnerable users are served by transmission scheme that avoid, make use of, and orthogo-nalise the interference. These schemes include the cooperative maximal ratio transmission and the non-cooperative orthogonal and non-orthogonal schemes. Radio resource allocation is studied and a solution is given for maximal ratio transmission and interference alignment. Simulation results are given, and showing the performance of each scheme when all users are considered critical and one scheme is used. Moreover, results showing the performance of our proposed frequency reuse scheme where different percentage of users considered critical.

Uneven traffic load among the cells increases call blocking rates in some cells and causes low resource utilisation in other cells and thus degrades user satisfaction and overall performance of the cellular system. Various centralised or semi centralised Load Balancing (LB) schemes have been proposed to cope with this time persistent problem, however, a fully distributed Self Organising (SO) LB solution is still needed for the future cellular networks. To this end, we present a novel distributed LB solution based on an analytical framework developed on the principles of nature inspired SO systems. A novel concept of super-cell is proposed to decompose the problem of “system-wide blocking minimization” into the local sub-problems in order to enable a SO distributed solution. Performance of the proposed solution is evaluated through system level simulations for both macro cell and femto cell based systems. Numerical results show that the proposed solution can reduce the blocking in the system close to an Ideal Central Control (ICC) based LB solution. The added advantage of the proposed solution is that it does not require heavy signalling overheads.

Energy efficiency (EE) is emerging as a key design criterion for both power limited, i.e. mobile devices, and power-unlimited, i.e. cellular networks, applications. Whereas, resource allocation is a well-known technique for improving the performance of communication systems. In this paper, we design a simple and optimal EE-based resource allocation method for the orthogonal multi-user channel by adapting the transmit power and rate to the channel condition such that the energy-per-bit consumption is minimized. We present our EE framework, i.e. EE metric and node power consumption model, and utilize it for formulating our EE-based optimization problem with or without constraint. In both cases, we derive explicit formulations of the optimal energy-per-bit consumption as well as optimal power and rate for each user. Our results indicate that EE-based allocation can substantially reduce the consumed power and increase the EE in comparison with spectral efficiency-based allocation.

In this paper, we propose a low-complexity distributed Inter-Cell Interference Coordination (ICIC) for emerging multi-cell HetNets (Heterogeneous Networks). The proposed scheme is quickly solved using linear programming tools and aims to maximize both the critical and the overall performance of the multi-cell system. Additionally, a utility measure is used to provide a varying level of user fairness to satisfy the most demanding network providers. Simulation results confirm the low-complexity of the proposed algorithm and its increased effectiveness over a number of state-of-art interference avoidance schemes.

In this paper, hybrid relaying schemes are investigated in the two-way relay channel, where the relay node is able to adaptively switch between different forwarding schemes based on the current channel state and its decoding status and thus provides more flexibility as well as improved performance. The analysis is conducted from the energy efficiency perspective for two transmission protocols distinguished by whether exploiting the direct link between two main communicating nodes (the source and destination nodes, and vice versa since it is two way communication) or not. A realistic power model taking circuitry power consumption of all involved nodes into account is employed. The energy efficiency is optimized in terms of consumed energy per bit subject to the Quality of Service (QoS) constraint. Numerical results show that the hybrid schemes are able to achieve the highest energy efficiency due to its capability of adapting to the channel variations and the protocol where the direct link is exploited is more energy efficient.

While many studies have concentrated on providing theoretical analysis for the relay assisted compress-and-forward systems little effort has yet been made to the construction and evaluation of a practical system. In this paper a practical CF system incorporating an error-resilient multilevel Slepian-Wolf decoder is introduced and a novel iterative processing structure which allows information exchanging between the Slepian-Wolf decoder and the forward error correction decoder of the main source message is proposed. In addition, a new quantization scheme is incorporated as well to avoid the complexity of the reconstruction of the relay signal at the final decoder of the destination. The results demonstrate that the iterative structure not only reduces the decoding loss of the Slepian-Wolf decoder, it also improves the decoding performance of the main message from the source.

Until recently, link adaptation and resource allocation for communication system relied extensively on the spectral efficiency as an optimization criterion. With the emergence of the energy efficiency (EE) as a key system design criterion, resource allocation based on EE is becoming of great interest. In this paper, we propose an optimal EE-based resource allocation method for the scalar broadcast channel (BC-S). We introduce our EE framework, which includes an EE metric as well as a realistic power consumption model for the base station, and utilize this framework for formulating our EE-based optimization problem subject to a power as well as fairness constraints. We then prove the convexity of this problem and compare our EE-based resource allocation method against two other methods, i.e. one based on sum-rate and one based on fairness optimization. Results indicate that our method provides large EE improvement in comparison with the two other methods by significantly reducing the total consumed power. Moreover, they show that near-optimal EE and average fairness can be simultaneously achieved over the BC-S channel.

Recently developed adaptive modulation schemes for opportunistic decode-and-forward (DF) relaying require knowledge of instantaneous channel-state-information (CSI) of all links. This paper aims at reducing signalling overhead of modulation-adaptive opportunistic DF relaying through exploitation of mixed channel knowledge. A novel semi-deterministic approach is proposed for performing joint rate-adaptation and best-relay selection. The selection process employs two criteria using mixed channel knowledge. A new metric namely the probability of missing the best relay (PMBR) is introduced, as only when the best relay is selected, the full diversity order can be achieved. It is shown that the proposed approach can achieve a good trade-off between the spectral efficiency and signalling overhead.

Cognitive Radio (CR) has been recognized as a promising solution to solve the current spectrum inefficiency problem. We consider a CR Base Station (CR-BS) has multiple spectrum bands over a wide frequency range and uses channel-dependent data rates to communicate with the Secondary Users (SUs). To maximize the spectrum utilization while supporting the Quality of Service (QoS) of the SUs, various decisions needs to be made during the operational period of the CR system taking into account not only the behavior of Primary Users (PUs) and SUs but also the current utilization of each spectrum band. In this paper, we propose an optimal policy for joint spectrum resource management in multi-band CR networks, which consists of admission control, spectrum band selection, spectrum handover and eviction control. The problem is formulated as a Semi-Markov Decision Process (SMDP) and the optimal decision for each system state is derived to reduce the SU blocking probability while keeping the SU dropping probability upper-bounded. Performance evaluation shows that the proposed policy outperforms other three policies in terms of both the SU blocking probability and the SU dropping probability.

In cognitive radio networks, the licensed frequency bands of the primary users (PUs) are available to the secondary user (SU) provided that they do not cause significant interference to the PUs. In this study, the authors analysed the normalised throughput of the SU with multiple PUs coexisting under any frequency division multiple access communication protocol. The authors consider a cognitive radio transmission where the frame structure consists of sensing and data transmission slots. In order to achieve the maximum normalised throughput of the SU and control the interference level to the legal PUs, the optimal frame length of the SU is found via simulation. In this context, a new analytical formula has been expressed for the achievable normalised throughput of SU with multiple PUs under prefect and imperfect spectrum sensing scenarios. Moreover, the impact of imperfect sensing, variable frame length of SU and the variable PU traffic loads, on the normalised throughput has been critically investigated. It has been shown that the analytical and simulation results are in perfect agreement. The authors analytical results are much useful to determine how to select the frame duration length subject to the parameters of cognitive radio network, such as network traffic load, achievable sensing accuracy and number of coexisting PUs.

Along with spectral efficiency (SE), energy efficiency (EE) is becoming one of the key performance evaluation criteria for communication system. These two criteria, which are conflicting, can be linked through their trade-off. The EE-SE trade-off for the multi-input multi-output (MIMO) Rayleigh fading channel has been accurately approximated in the past but only in the low-SE regime. In this paper, we propose a novel and more generic closed-form approximation of this trade-off which exhibits a greater accuracy for a wider range of SE values and antenna configurations. Our expression has been here utilized for assessing analytically the EE gain of MIMO over single-input single-output (SISO) system for two different types of power consumption models (PCMs): the theoretical PCM, where only the transmit power is considered as consumed power; and a more realistic PCM accounting for the fixed consumed power and amplifier inefficiency. Our analysis unfolds the large mismatch between theoretical and practical MIMO vs. SISO EE gains; the EE gain increases both with the SE and the number of antennas in theory, which indicates that MIMO is a promising EE enabler; whereas it remains small and decreases with the number of transmit antennas when a realistic PCM is considered.

This work addresses joint transceiver optimization for multiple-input, multiple-output (MIMO) systems. In practical systems the complete knowledge of channel state information (CSI) is hardly available at transmitter. To tackle this problem, we resort to the codebook approach to precoding design, where the receiver selects a precoding matrix from a finite set of pre-defined precoding matrices based on the instantaneous channel condition and delivers the index of the chosen precoding matrix to the transmitter via a bandwidth-constraint feedback channel. We show that, when the symbol constellation is improper, the joint codebook based precoding and equalization can be designed accordingly to achieve improved performance compared to the conventional system.

This paper proposes an analytical model for the throughput of the enhanced distributed channel access (EDCA) mechanism in the IEEE 802.11p medium-access control (MAC) sublayer. Features in EDCA such as different contention windows (CW) and arbitration interframe space (AIFS) for each access category (AC) and internal collisions are taken into account. The analytical model is suitable for both basic access and the request-to-send/clear-to-send (RTS/CTS) access mode. Different from most of existing 3-D or 4-D Markov-chain-based analytical models for IEEE 802.11e EDCA, without computation complexity, the proposed analytical model is explicitly solvable and applies to four access categories of traffic in the IEEE 802.11p. The proposed model can be used for large-scale network analysis and validation of network simulators under saturated traffic conditions. Simulation results are given to demonstrate the accuracy of the analytical model. In addition, we investigate service differentiation capabilities of the IEEE 802.11p MAC sublayer.

Energy savings are becoming a global trend, hence the importance of energy efficiency (EE) as an alternative performance evaluation metric. This paper proposes an EE based resource allocation method for the broadcast channel (BC), where a linear power model is used to characterize the power consumed at the base station (BS). Having formulated our EE based optimization problem and objective function, we utilize standard convex optimization techniques to show the concavity of the latter, and thus, the existence of a unique globally optimal energy-efficient rate and power allocation. Our EE based resource allocation framework is also extended to incorporate fairness, and provide a minimum user satisfaction in terms of spectral efficiency (SE). We then derive the generic equation of the EE contours and use them to get insights about the EE-SE trade-off over the BC. The performances of the aforementioned resource allocation schemes are compared for different metrics against the number of users and cell radius. Results indicate that the highest EE improvement is achieved by using the unconstrained optimization scheme, which is obtained by significantly reducing the total transmit power. Moreover, the network EE is shown to increase with the number of users and decrease as the cell radius increases.

Network virtualization has been recognized as a promising solution to enable the rapid deployment of customized services by building multiple Virtual Networks (VNs) on a shared substrate network. Whereas various VN embedding schemes have been proposed to allocate the substrate resources to each VN requests, little work has been done to provide backup mechanisms in case of substrate network failures. In a virtualized infrastructure, a single substrate failure will affect all the VNs sharing that resource. Provisioning a dedicated backup network for each VN is not efficient in terms of substrate resource utilization. In this paper, we investigate the problem of shared backup network provision for VN embedding and propose two schemes: shared on-demand and shared pre-allocation backup schemes. Simulation experiments show that both proposed schemes make better utilization of substrate resources than the dedicated backup scheme without sharing, while each of them has its own advantages.

Cooperative Transmission can be used in a multicell scenario where base stations are connected to a central processing unit. This cooperation can be used to improve the fairness for users with bad channel conditions-critical users. This paper will look into using cooperative transmission alongside the orthogonal OFDM scheme to improve fairness by careful selection of critical users and a resource allocation and resource division between the two schemes. A solution for power and subcarrier allocations is provided together with a solution for the selection of the critical users. Simulation results are provided to show the fairness achieved by the proposed critical users selection method, resource allocation and the resource division method applied under the stated assumptions.

This paper investigates the impacts of deploying Mobile Femtocell (MFemtocell) in LET networks. We investigate access delay, capacity, and feedback signalling overhead required for implementation of opportunistic scheduling in LTE cellular networks. We particularly study the impacts of deploying MFemtocells stations on the signalling overhead for opportunistic scheduling. Our system level simulation results indicate that one potential advantage of deploying MFemtocells can contribute to improve spectral efficiency by reducing the amount of feedback signalling.

One of the major challenges of Cellular network based localization techniques is lack of hearability between mobile terminals (MTs) and base stations (BSs), thus the number of available anchors is limited. In order to solve the hearability problem, previous work assume some of the MTs have their location information via Global Positioning System (GPS). These located MT can be utilized to find the location of an un-located MT without GPS receiver. However, its performance is still limited by the number of available located MTs for cooperation. This paper consider a practical scenario that hearability is only possible between a MT and its home BS. Only one located MT together with the home BS are utilized to find the location of the un-located MT. A hybrid cooperative localization approach is proposed to combine time-of-arrival and received signal strength based fingerprinting techniques. It is shown in simulations that the proposed hybrid approach outperform the stand-alone time-of-arrival techniques or received signal strength based fingerprinting techniques in the considered scenario. It is also found that the proposed approach offer better accuracy with larger distance between the located MT and the home BS.

Cooperative communication is an effective approach for increasing the spectral efficiency and/or the coverage of cellular networks as well as reducing the cost of network deployment. However, it remains to be seen how energy efficient it is. In this paper, we assess the energy efficiency of the conventional Amplifyand- forward (AF) scheme in an in-building relaying scenario. This scenario simplifies the mutual information formulation of the AF system and allows us to express its channel capacity with a simple and accurate closed-form approximation. In addition, a framework for the energy efficiency analysis of AF system is introduced, which includes a power consumption model and an energy efficiency metric, i.e. the bit-per-joule capacity. This framework along with our closed-form approximation are utilized for assessing both the channel and bit-per-joule capacities of the AF system in an in-building scenario. Our results indicate that transmitting with maximum power is not energy efficient and that AF system is more energy efficient than point-to-point communication at low transmit powers and signal-to-noise ratios.

With the exponential growth of internet communications and the rise to prominence of environmental concerns, the energy consumption of the Information Communications Technology industry has become an issue of major importance. One area that has not been yet fully explored is the evaluation of the computational loads required in the processing and forwarding of traffic. While significant work has been performed towards optimizations especially of wireless protocols for energy efficiency “outside the devices” through energy-optimizing mechanisms that deal with the characteristics of the transmission medium, the computational loads required by the protocols' operations and mechanisms themselves “within the devices” have not been yet investigated adequately. Towards this end, in this paper we put forward a method suitable for the measurement and profiling of the processing requirements of the operations, as well as for the traffic loads, generated by both wired and wireless protocols. As we show, this method can be used to determine specific operations, functionality sets and configurations that increase the protocols' and the resulting overall network energy use. It can be used, therefore, to derive recommendations for further protocol optimizations towards energy efficiency as well as practical rules for the selection of specific protocols depending on the higher level applications and the specific deployment environment under which they operate.

Cooperative communication is an alternative way to generate spatial diversity. However due to the extra time slot required for relay transmission, the spectral efficiency (SE) is reduced. Adaptive modulation can optimize the spectral efficiency by adopting the transmission parameters to the channel conditions. This requires the perfect channel state information (CSI) feedback at the transmitter. However, in real life this is not practical. The feedback channel is noisy and fading which causes feedback errors. In this contribution, first we investigate the performance of adaptive modulation schemes for cooperative communications with perfect feedback, the adaptive modulation is applied both by the source and the relay. Then the impact of noisy feedback channels on the performance is analysed. The simulation results demonstrate the spectral efficiency improvement, and illustrates that the effect of the noisy feedback can be ignored when the feedback channel signal-to-noise ratio (SNR) is relatively high i.e. 10 dB.

In this paper, the mutual information transfer characteristics of turbo Multiuser Detector (MUD) for a novel air interface scheme, called Low Density Signature Orthogonal Frequency Division Multiplexing (LDS-OFDM) are investigated using Extrinsic Information Transfer (EXIT) charts. LDS-OFDM uses Low Density Signature structure for spreading the data symbols in frequency domain. This technique benefits from frequency diversity besides its ability of supporting parallel data streams more than the number of subcarriers (overloaded condition). The turbo MUD couples the data symbols' detector of LDS scheme with users' FEC (Forward Error Correction) decoders through the message passing principle. The effect of overloading on LDS scheme's performance is evaluated using EXIT chart. The results show that at Eb/N0as low as 0.3, LDS-OFDM can support loads up to 300%.

In this paper we present the uDirect algorithm as a novel approach for mobile phone centric observation of a user's facing direction, through which the device and user orientations relative to earth coordinate are estimated. While the device orientation estimation is based on accelerometer and magnetometer measurements in standing mode, the unique behavior of measured acceleration during stance phase of a human's walking cycle is used for detecting user direction. Furthermore, the algorithm is independent of initial orientation of the device which gives the user higher space of freedom for long term observations. As the algorithm only relies on embedded accelerometer and magnetometer sensors of the mobile phone, it is not susceptible to shadowing effect as GPS. In addition, by performing independent estimations during each step of walking the model is robust to error accumulation. Evaluating the algorithm with 180 data samples from 10 participates has empirically confirmed the assumptions of our analytical model about the unique characteristics of the human stance phase for direction estimation. Moreover, our initial inspection has shown a system based on our algorithm outperforms conventional use of GPS and PCA analysis based techniques for walking distances more than 2 steps.

This paper quantifies the impact of co-tier interference in femtocells (i.e. inter-cell interference arising from other neighbouring femtocells) through a semi-analytical approach concluding that without interference mitigation techniques, QoS in the form of acceptable outage probability is not achievable in scenarios representing medium to worst cases of interference. Results obtained from semi-analytical approach are also compared against Monte Carlo simulations for evaluation purposes. Subsequently, some important radio access parameters and deployment configurations, such as path loss model, shadowing, wall penetration loss, location of femtocells and user distribution are further examined as key elements that can potentially affect, positively or negatively, the femtocell-to-femtocell interference in a multi-femtocell deployment. The results can be used as guidelines in the deployments of femtocells.

In this paper, we propose a novel energy-aware adaptive sectorisation strategy, where the base stations are able to adapt themselves to the temporal traffic variation by switching off some sectors and changing the beam-width of the remaining sectors. An event based user traffic model is established according to Markov-Modulated Poisson Process (MMPP). Adaptation is performed while taking into account the the target Quality of Service (QoS), in terms of blocking probability. In addition, coverage requirement is also considered. This work targets at future cellular systems, in particular LTE systems. The results show that at least 21% energy consumption can be reduced by using the proposed adaptive sectorisation strategy.

Energy efficiency has become increasingly important in wireless communications, with significant environmental and financial benefits. This paper studies the achievable capacity region of a single carrier uplink channel consisting of two transmitters and a single receiver, and uses average energy efficiency contours to find the optimal rate pair based on four different targets: Maximum energy efficiency, a trade-off between maximum energy efficiency and rate fairness, achieving energy efficiency target with maximum sum-rate and achieving energy efficiency target with fairness. In addition to the transmit power, circuit power is also accounted for, with the maximum transmit power constrained to a fixed value. Simulation results demonstrate the achievability of the optimal energy-efficient rate pair within the capacity region, and provide the trade-off for energy efficiency, fairness and maximum sum-rate.

Low Density Signature-Orthogonal Frequency Division Multiplexing (LDS-OFDM) has been introduced recently as an efficient multiple access technique. In this paper, we focus on the subcarrier and power allocation scheme for uplink LDS-OFDM system. Since the resource allocation problem is not convex due to the discrete nature of subcarrier allocation, the complexity of finding the optimal solutions is extremely high. We propose a heuristic subcarrier and power allocation algorithm to maximize the weighted sum-rate. The simulation results show that the proposed algorithm can significantly increase the spectral efficiency of the system. Furthermore, it is shown that LDS-OFDM system can achieve an outage probability much less than that for OFDMA system.

This paper presents a novel differential energy detection scheme based on extremes of order statistics for sensing OFDM signals. The underlying initiative of this approach is applying the order statistics of the differential Energy Spectral Density in frequency domain. The proposed technique takes advantage of the channel selectivity which is inherited from high data-rate communications. The introduced frequency diversity allows this approach to meet FCC requirements even in low SNR environments i.e., (-25 ,-10) dB. Analytical results of sensing performance are provided in terms of both probability of false alarm and probability of detection. Furthermore, computer simulations show that the proposed technique outperforms two most commonly used source detection approaches namely conventional energy detection and cyclostationarity based detection for up to 10 dB gain in low SNR environments.

Previous work about cooperative localization in cellular networks usually consider a centralized processor (CP) is available for location estimation. This paper consider cooperative localization in a distributed base station (BS) scenario, where there is no CP, and the distributed BSs are responsible for location estimation. In this scenario, Global Positioning System (GPS) enable mobile terminals (MTs), i.e., located MTs, are employed as reference nodes.Then, several located MTs can help to find the locations of an un-located MT, by estimating the distance between the un-located MT using received signal strength techniques. Two localization approaches are proposed, the first approach requires only one BS to collect all the assistance information for localization and estimate the location. The second approach distribute the location estimation task to several BSs. The communication overhead between distributed BSs are investigated for these two approaches. Moreover, by taking into account the effect of imperfect location knowledge of the located MTs, the accuracy limits of both approaches are derived. The simulation results shows that compared with the first approach, the second approach can reduce the communication overhead between distributed BSs with the paid of accuracy.

This paper consider cooperative localization in cellular networks. In this scenario, several located mobile terminals (MTs) are employed as reference nodes to find the location of an un-located MT. The located MTs sent training sequences in the uplink, then the un-located MT perform distance estimation using received signal strength techniques. The localization accuracy of the un-located MT is characterized in terms of squared position error bound (SPEB). By taking into account the imperfect a priori location knowledge of the located MTs, the SPEB is derived in a closed-form. The closed-form indicate that the effect of the imperfect location knowledge on SPEB is equivalent to the increase of the variance of distance estimation. Moreover, based on the obtained closed-form, a MT selection scheme is proposed to decrease the number of located MTs sending training sequences, thus reduce the training overhead for localization. The simulation results show that the proposed scheme can reduce the training overhead with the paid of accuracy. And with the same training overhead, the accuracy of the proposed scheme is better than that of random selection.

Amplify-and-forward (AF) is one of the most popular and simple approaches for transmitting information over a cooperative multi-input multi-output (MIMO) relay channel. In cooperative communication, relays are employed for improving the coverage or enhancing the spectral efficiency, especially of cell-edge users. However, in a multi-cell context, the use of relays will also lead to an increase in the level of interference that is experienced by cell-edge users of neighboring cells. In this paper, two novel precoding schemes are proposed for mitigating this adverse effect of cooperative communication. They are designed by taking into account the effect of interference coming from neighboring cells, i.e. other cell-interference (OCI), in order to maximize the sum-rate of cell-edge users. Our novel OCI-aware precoding schemes are compared against non OCI-aware techniques and results show the large performance gain in terms of sum-rate that our schemes can achieved, especially for large numbers of users and/or antennas in the multi-cell system.

Coordinated multi-point (CoMP) architecture has proved to be very effective for improving the user fairness and spectral efficiency of cellular communication system, however, its energy efficiency remains to be evaluated. In this paper, CoMP system is idealized as a distributed antenna system by assuming perfect backhauling and cooperative processing. This simplified model allows us to express the capacity of the idealized CoMP system with a simple and accurate closed-form approximation. In addition, a framework for the energy efficiency analysis of CoMP system is introduced, which includes a power consumption model and an energy efficiency metric, i.e. bit-per-joule capacity. This framework along with our closed-form approximation are utilized for assessing both the channel and bit-per-joule capacities of the idealized CoMP system. Results indicate that multi-base-station cooperation can be energy efficient for cell-edge communication and that the backhauling and cooperative processing power should be kept low. Overall, it has been shown that the potential of improvement of CoMP in terms of bit-per-joule capacity is not as high as in terms of channel capacity due to associated energy cost for cooperative processing and backhauling.

A mass deployment of femtocells is anticipated to affect a number of areas more adversely, especially in the cell-edge of the macrocell network. In this paper, we propose a flexible frequency-partitioning scheme for OFDMA network based on cyclic difference sets. The cyclic property of these sets allows a quick construction of orthogonal patterns for the macrocell network with an emphasis on tri-sector sites. The impact and the co-deployment of femtocells is also investigated. Unlike, with existing works in the literature, the novel scheme can adaptively control the level of coverage in the cell-edges areas and additionally enables coexistence of femtocells in the network. Simulation results confirm the effectiveness of the proposed scheme in both macrocell and femtocell networks compared to the legacy soft frequency reuse and universal frequency reuse.

In this paper, we analyze the Hybrid Automatic Repeat re-Quest (HARQ) protocols used in conjunction with hybrid relaying schemes from the energy efficiency perspective. In cooperative communications, hybrid relaying strategy allows the relay to dynamically switch between different forwarding schemes according to its decoding status and provide either transmit or receive diversity to the users depending on the current channel condition. The energy efficiency analysis takes the circuitry energy consumption of all involved nodes into consideration and the energy efficiency is optimized in terms of energy per bit by manipulating the activation time and the transmission energy.

Femtocell networks are inherently interference- limited similar to other cellular networks. However, massive and unplanned roll-outs of femtocells make the classical macro cells more susceptible to the resulting interference. In this paper a novel distributed approach is proposed for resource allocation for femtocells that opportunistically identifies low-cost resources (in terms of interference) in a way that would cause minimal impact on the service level of primary macro users. Simulation study confirms the efficiency of proposed algorithm compared to benchmarking scenario of macro-only network operation. As it is shown, the proposed method enables flexible tuning of femto throughput at the cost of macro users service.

In this paper, a cooperative iterative water-filling approach is investigated for two-user Gaussian interference channel. State-of-the-art approaches only maximize the individual user's own rate and always model interference as noise. Our proposed approach establishes user cooperation through sharing network side information. It iteratively maximizes the sum-rate of both users subject to distributed power constraint. Interference is optimally regarded as message or noise. Three efficient rate-sharing schemes are also investigated between two users based on priority. Numerical results are performed in frequency-selective environment. It is observed that the proposed approach offers significantly performance improvement in comparison with conventional iterative water-filling approaches.

A key component of an efficient multipath routing is the optimal traffic allocation strategy that deals with how the traffic should be distributed among the multiple paths. In this study, two intelligent traffic distribution policies that minimise the experienced delay by the end user are proposed. For such delay minimisation, the authors investigate and compare average delay minimisation and maximum delay minimisation (MDM) over all the paths. It will be shown that MDM is a more efficient strategy for end-to-end delay minimisation. Based on this fact, an intelligent algorithm that can minimise the maximum delay of all paths is proposed using an adaptive traffic sharing scheme.

In this paper, we propose novel Hybrid Automatic Repeat re-Quest (HARQ) strategies used in conjunction with hybrid relaying schemes, named as H2-ARQ-Relaying. The strategies allow the relay to dynamically switch between amplify-and-forward/compress-and-forward and decode-and-forward schemes according to its decoding status. The performance analysis is conducted from both the spectrum and energy efficiency perspectives. The spectrum efficiency of the proposed strategies, in terms of the maximum throughput, is significantly improved compared with their non-hybrid counterparts under the same constraints. The consumed energy per bit is optimized by manipulating the node activation time, the transmission energy and the power allocation between the source and the relay. The circuitry energy consumption of all involved nodes is taken into consideration. Numerical results shed light on how and when the energy efficiency can be improved in cooperative HARQ. For instance, cooperative HARQ is shown to be energy efficient in long distance transmission only. Furthermore, we consider the fact that the compress-and-forward scheme requires instantaneous signal to noise ratios of all three constituent links. However, this requirement can be impractical in some cases. In this regard, we introduce an improved strategy where only partial and affordable channel state information feedback is needed.

Providing quality of service (QoS) assurances in a mobile ad hoc network (MANET) is difficult due to node mobility, contention for channel access, a lack of centralized co-ordination, and the unreliable nature of the wireless channel. A QoS-aware routing (QAR) protocol and an admission control (AC) protocol are two of the most important components of a system attempting to provide QoS guarantees in the face of the above mentioned difficulties faced. Many QAR and AC-based solutions have been proposed, but such network layer solutions are often designed and studied with idealized lower layer models in mind. This means that existing solutions are not designed for dealing with practical phenomena such as shadow fading and the link quality-dependent fluctuation of link transmission rates. This paper proposes and evaluates new solutions for improving the performance of QAR and AC protocols in the face of mobility, shadowing, and varying link SINR. It is found that proactively maintaining backup routes for active sessions, adapting transmission rates, and routing around temporarily low-SINR links can noticeably improve the reliability of assured throughput services.

The mutual information (MI) of multiple-input multiple-output (MIMO) system over Rayleigh fading channel is known to asymptotically follow a normal probability distribution. In this paper, we first prove that the MI of distributed MIMO (DMIMO) system is also asymptotically equivalent to a Gaussian random variable (RV) by deriving its moment generating function (MGF) and by showing its equivalence with the MGF of a Gaussian RV. We then derive an accurate closed-form approximation of the outage probability for DMIMO system by using the mean and variance of the MI and show the uniqueness of its formulation. Finally, several applications for our analysis are presented.

Orthogonal relay based cooperative communication enjoys distributed spatial diversity gain at the price of spectral efficiency. This work aims at improving the spectral efficiency for orthogonal opportunistic decode-and-forward (DF) relaying through employment of novel adaptive modulation scheme. The proposed scheme allows source and relay to transmit information in different modulation formats, while the MAP receiver is employed at destination for the diversity combining. Given the individual power constraint and target bit-error-rate (BER), the proposed scheme can significantly improve the spectral efficiency in comparison with the non-adaptive DF relaying and adaptive direct transmission.

Hot spots in a wireless sensor network emerge as locations under heavy traffic load. Nodes in such areas quickly deplete energy resources, leading to disruption in network services. This problem is common for data collection scenarios in which Cluster Heads (CH) have a heavy burden of gathering and relaying information. The relay load on CHs especially intensifies as the distance to the sink decreases. To balance the traffic load and the energy consumption in the network, the CH role should be rotated among all nodes and the cluster sizes should be carefully determined at different parts of the network. This paper proposes a distributed clustering algorithm, Energy-efficient Clustering (EC), that determines suitable cluster sizes depending on the hop distance to the data sink, while achieving approximate equalization of node lifetimes and reduced energy consumption levels. We additionally propose a simple energy-efficient multihop data collection protocol to evaluate the effectiveness of EC and calculate the end-to-end energy consumption of this protocol; yet EC is suitable for any data collection protocol that focuses on energy conservation. Performance results demonstrate that EC extends network lifetime and achieves energy equalization more effectively than two well-known clustering algorithms, HEED and UCR.

Abstract Providing quality‐of‐service (QoS) assurances in a mobilead hocnetwork (MANET) is difficult due to node mobility, contention for channel access, a lack of centralised coordination, and the unreliable nature of the wireless channel. QoS‐aware routing (QAR) and admission control (AC) protocols comprise two of the most important components of a system attempting to provide QoS guarantees in the face of the above‐mentioned difficulties. This paper presents a comparative study, utilising a realistic shadow fading channel, of the performance of several state‐of‐the‐art amalgamated QAR‐AC protocols, which are designed for providing throughput guarantees to applications. The advantages and drawbacks of their particular features are highlighted. For an environment where link quality varies rapidly, the results of the study highlight the ineffectiveness of previously‐proposed methods of relying merely on the success of route discovery to perform AC, of relying on the exceeding of the MAC layer retransmission count for link failure detection, of existing congestion detection schemes and of careful re‐admission of data sessions rather than fast re‐routing after shadowing‐induced link failures. Based on the lessons learnt, design guidelines for future QAR and AC protocols operating in a mobile shadow‐fading‐afflicted environment are presented. Copyright © 2010 John Wiley & Sons, Ltd.

Exploiting path diversity to enhance communication reliability is a key desired property in Internet. While the existing routing architecture is reluctant to adopt changes, overlay routing has been proposed to circumvent the constraints of native routing by employing intermediary relays. However, the selfish inter-domain relay placement may violate local routing policies at intermediary relays and thus affect their economic costs and performances. With the recent advance of the concept of network virtualization, it is envisioned that virtual networks should be provisioned in cooperation with infrastructure providers in a holistic view without compromising their profits. In this paper, the problem of policy-aware virtual relay placement is first studied to investigate the feasibility of provisioning policy-compliant multipath routing via virtual relays for inter-domain communication reliability. By evaluation on a real domain-level Internet topology, it is demonstrated that policy-compliant virtual relaying can achieve a similar protection gain against single link failures compared to its selfish counterpart. It is also shown that the presented heuristic placement strategies perform well to approach the optimal solution.

Formal verification tools have been extensively used in the past to assess the correctness of protocols, processes, and systems in general. Their most common use so far has been in identifying whether livelock or deadlock situations can occur during protocol execution, process, or system operation. In this paper we aim to showcase that an additional equally important and useful application of formal verification tools can be in protocol design and optimization itself. This can be achieved by using the tools in a rather different context compared to their traditional use. That is not only as means to assess the correctness of a protocol in terms of lack of livelock and deadlock situations but rather as tools capable of building profiles of protocols, associating performance related metrics, and identifying operational patterns and possible bottleneck operations in terms of metrics of interest. This process can provide protocol designers with an insight about the protocols' behavior and guide them towards further protocol design optimizations. We illustrate these principles using some existing protocol implementations as case studies.

Line-of-sight (LoS) time-of-arrival (ToA) is one of key parameters for network-based localization techniques. Its estimation accuracy depends on the bandwidth of transmitted signals. This paper aims to investigate the LoS-ToA estimation for an interleaved OFDMA based network, where the signal bandwidth is not sufficiently wide to offer the acceptable estimation accuracy. In this situation, an over-sampling approach is proposed to improve the resolution. Moreover, the proposed scheme does not require modification of mobile communications systems. It is found that the proposed approach can significantly improve the ToA estimation performance, and offer adequate accuracy even for the low signal-to-noise ratio (SNR) range.

In this paper, we evaluate a two-stage call admission control (CAC) policy for Long Term Evolution (LTE) Systems working along with packet scheduler (PS). The proposed two-stage admission scheme uses the quality the received signal as an indicator to estimate the amount of resources for each call in order to improve the performance of the overall network. Unlikely with other CAC approaches we use a SINR-based estimator to clarified the connection quality, and thereafter quantize the user demand in term of physical resources. However, for scheduling, we use an algorithm where highest priority is assigned for guaranteed bit rate (GBR) proportional to their packet delays. Simulations results show that the proposed CAC scheme combined with proposed Packet Scheduling policy greatly improves performance of non-GBR service, while also maintaining quite well the Quality of Service (QoS) of delay sensitive traffic (GBR) and ends up with reduction in Packet Drop Ratio (PDR) for system.

A key component of an efficient multipath routing is the optimal resource allocation strategy that deals with how the traffic should be distributed amongst the multiple paths. In this paper, intelligent traffic distribution policies that minimise the experienced delay by the user are proposed. For such delay minimisation, we investigate and evaluate traffic distribution policies in a multipath environment that minimize the average delay and the bottleneck path delay over all the paths.

A novel joint decompression and forward error correction (FEC) decoding structure for a compress-and-forward relay system is proposed. We notice the possibility of iterative processing between the decompressor and the FEC decoder for the original source information and propose a turbo-like structure to allow interaction and extrinsic information exchange between these two parts. Simulation results show considerable gains through this interaction.

Adaptive modulation can improve the throughput of cooperative communications, which has been shown in previous research. So far the adaptive modulation is applied with respect to perfect transmitter side Channel State Information (CSI) in most current work. In this paper we have investigated the modulation-adaptive cooperation in Rayleigh fading channels providing imperfect CSI at senders. The adaptive modulation scheme with rate adaptation at the relay proposed in our previous work has been extended here first. Then a new adaptive modulation scheme with adaptation at both the source and relay is proposed. Simulation results show the proposed two schemes can provide significant improvement in throughput over the fixed DF relaying cooperation even with imperfect feedback; the throughput can be further improved with the proposed scheme allowing adaptive modulation at both the source and relay.

Reducing the consumed energy in wireless communication systems has a direct impact on operational expenditure as well as CO2 emissions. With increasing interest in multimedia applications and high data rate services, environmentally sustainable communications networks must reduce the energy per delivered bit at an equal rate, if not better. This paper addresses downlink energy-efficient transmission in OFDMA systems and maximizes the overall bits transmitted per joule of energy. In addition to the transmit power, circuit power is also accounted for in the energy-efficient design, which is tackled using both standard optimization techniques and a frame work based on time-sharing. Simulation results show similar performances for both cases with the latter having lower complexity and taking less CPU time to run.

A Hybrid ARQ (HARQ) scheme employing a flexible amplify and forward (AF) relaying is considered for a cooperative relay channel. Two phase types with flexible length: one phase for relay reception and another phase for relay forwarding are assumed to accommodate half duplex relaying. We assume two types of encoding: repetition coding (RC) and unconstrained coding (UC). The outage performance of the considered flexible AF scheme is analytically derived for any number of retransmissions, and both RC and UC encoding used by the considered HARQ. The state transition models of the considered protocols are presented and then the HARQ throughput and latency performance are analytically calculated; As a result time consuming Monte Carlo based evaluations are avoided. The provided analysis enables us to predict the performance and adjust transmission rate and frame structure for any combination of the signal to noise ratio (SNR) of the involved links. Performance gains of up to 1 and 1.5 dB are observed for the considered simulation scenarios thanks to the flexible AF combined with properly designed HARQ protocols. Also the flexible AF is able to attain its performance over all the range of SNRs of the constituent links while the conventional AF is only efficient on a restricted range of SNRs.

The most common use of formal verification methods and tools so far has been in identifying whether livelock and/or deadlock situations can occur during protocol execution, process, or system operation. In this work we aim to show that an additional equally important and useful application of formal verification tools can be in protocol design and protocol selection in terms of performance related metrics. This can be achieved by using the tools in a rather different context compared to their traditional use. That is not only as model checking tools to assess the correctness of a protocol in terms of lack of livelock and deadlock situations but rather as tools capable of building profiles of protocol operations, assessing their performance, and identifying operational patterns and possible bottleneck operations. This process can provide protocol designers with an insight about the protocols' behavior and guide them towards further protocol design optimizations. It can also assist network operators and service providers in selecting the most suitable protocol for specific network and service configurations. We illustrate these principles by showing how formal verification tools can be applied in this protocol profiling and performance assessment context using some existing protocols as case studies.

Amplify-and-forward (AF) is one of the most common and simple approaches for transmitting information over a cooperative multi-input multi-output (MIMO) relay channel. It has recently been demonstrated that the spectral efficiency of AF scheme can be maximized by using the relay as a smart precoder. However, source node precoding has not been included in the overall maximization problem. In this paper, we propose a joint precoder design at the relay and source nodes for maximizing the cooperative mutual information (MI), i.e., the combination of direct and relay link MI, and thus, for further improving the spectral efficiency of AF scheme. In addition, we provide an algorithm for performing our joint precoding technique. Performance analysis indicates that our novel precoding algorithm outperforms other existing AF precoding methods in various link configurations.

Amplify-and-forward (AF) is one of the most popular and simple approaches to transmit information over a cooperative multi-input multi-output (MIMO) relay channel. In this paper, we propose three novel power allocation methods for the downlink of cooperative multi-user MIMO AF system, which are designed to maximize the weighted sum-rate (WSR) of the cooperative system, by considering two different levels of channel state information (CSI). We design efficient precoding algorithms at the relay by assuming that either only the receive CSI or both receive and transmit CSI is available at the relay. Results show the performance improvement that our schemes can achieve in terms of sum-rate and WSR metrics.

Performance of next generation OFDM/OFDMA based Distributed Cellular Network (ODCN) where no cooperation based interference management schemes are used, is dependent on four major factors: 1) spectrum reuse factor, 2) number of sectors per site, 3) number of relay station per site and 4) modulation and coding efficiency achievable through link adaptation. The combined effect of these factors on the overall performance of a Deployment Architecture (DA) has not been studied in a holistic manner. In this paper we provide a framework to characterize the performance of various DA's by deriving two novel performance metrics for 1) spectral efficiency and 2) fairness among users. These metrics are designed to include the effect of all four contributing factors. We evaluate these metrics for a wide set of DA's through extensive system level simulations. The results provide a comparison of various DA's for both cellular and relay enhanced cellular systems in terms of spectral efficiency and fairness they offer and also provide an interesting insight into the tradeoff between the two performance metrics. Numerical results show that, in interference limited regime, DA's with highest spectrum efficiency are not necessarily those that resort to full frequency reuse. In fact, frequency reuse of 3 with 6 sectors per site is spectrally more efficient than that with full frequency reuse and 3 sectors. In case of relay station enhanced ODCN a DA with full frequency reuse, six sectors and 3 relays per site is spectrally more efficient and can yield around 170% higher spectrum efficiency compared to counterpart DA without RS.

Recent research on Frequency Reuse (FR) schemes for OFDM/OFDMA based cellular networks (OCN) suggest that a single fixed FR cannot be optimal to cope with spatiotemporal dynamics of traffic and cellular environments in a spectral and energy efficient way. To address this issue this paper introduces a novel Self Organizing framework for adaptive Frequency Reuse and Deployment (SO-FRD) for future OCN including both cellular (e.g. LTE) and relay enhanced cellular networks (e.g. LTE Advance). In this paper, an optimization problem is first formulated to find optimal frequency reuse factor, number of sectors per site and number of relays per site. The goal is designed as an adaptive utility function which incorporates three major system objectives; 1) spectral efficiency 2) fairness, and 3) energy efficiency. An appropriate metric for each of the three constituent objectives of utility function is then derived. Solution is provided by evaluating these metrics through a combination of analysis and extensive system level simulations for all feasible FRD's. Proposed SO-FRD framework uses this flexible utility function to switch to particular FRD strategy, which is suitable for system's current state according to predefined or self learned performance criterion. The proposed metrics capture the effect of all major optimization parameters like frequency reuse factor, number of sectors and relay per site, and adaptive coding and modulation. Based on the results obtained, interesting insights into the tradeoff among these factors is also provided.

Spectrum sensing is one of key enabling techniques to advanced radio technologies such as cognitive radios and ALOHA. This paper presents a novel non-cooperative spectrum sensing approach that can achieve a good trade-off between latency, reliability and computational complexity. Our major idea is to exploit the first-order cyclostationarity of the primary user's signal to reduce the noise-uncertainty problem inherent in the conventional energy detection approach. It is shown that the proposed approach is suitable for detecting the primary user's activity in the interweave paradigm of cognitive spectrum sharing, while the active primary user is periodically sending training sequence. Computer simulations are carried out for the typical IEEE 802.11g system. It is observed that the proposed approach outperforms both the energy detection and the second-order cyclostationarity approach when the observation period is more than 10 frames corresponding to 0.56 ms.

This paper presents a novel mechanism which increases mobile terminal battery performance. It supports a cell reselection algorithm which decides on which cell, user equipment (UE) is camped on when in idle mode (there is no active radio connection with a mobile network). Study is based on real 3G UTRA network measurements. Authors propose a technique to reduce UE current consumption in idle mode based on dynamic Sintrasearchneighbour cell measurements threshold optimization. System analysis covers both UTRA and E-UTRA - Long Term Evolution (LTE) technology.

Mobile communications are increasingly contributing to global energy consumption. The EARTH (Energy Aware Radio and neTworking tecHnologies) project tackles the important issue of reducing CO2emissions by enhancing the energy efficiency of cellular mobile networks. EARTH is a holistic approach to develop a new generation of energy efficient products, components, deployment strategies and energy-aware network management solutions. In this paper the holistic EARTH approach to energy efficient mobile communication systems is introduced. Performance metrics are studied so to assess the theoretical bounds of energy efficiency and the practical achievable limits. Moreover, various deployment strategies focusing on their potential to reduce energy consumption are studied, whilst providing uncompromised coverage and user experience. This includes heterogeneous networks with a sophisticated mix of different cell sizes, which may be further enhanced by energy efficient relaying and base station cooperation technologies. Finally, scenarios leveraging the capability of advanced terminals to operate on multiple radio access technologies (RAT) are discussed with respect to their energy savings potential.

This paper proposes an analytical model for the throughput of the Enhanced Distributed Channel Access (EDCA)mechanism in IEEE 802.11p MAC sub-layer. Features in EDCA such as different Contention Windows (CW) and Arbitration Interframe Space (AIFS) for each Access Category (AC), and internal collisions are taken into account. The analytical model is suitable for both basic access and the Request-To-Send/Clear-To-Send (RTS/CTS) access mode. The proposed analytical model is validated against simulation results to demonstrate its accuracy.

In this paper, multilevel coding is tailored for cooperative communication, where the source deploys a layered encoding structure. Due to the BER difference for different coding levels, there is some possibility that the relay, despite of successful decoding in upper coding levels, might not be able to correctly decode in lower levels. We propose a hybrid relaying scheme to cope with this problem, where the relay deploys decode-and-forward in upper levels and compress-and-forward in lower levels. Simulation results demonstrate that this hybrid technique approaches a fine balance among the complexity, BER performance and transmission efficiency.

Clustering algorithms have been extensively applied for energy conservation in wireless sensor networks (WSNs). In a clustered WSNs, cluster-heads (CHs) play an important role and drain energy more rapidly than other member nodes. Numerous mechanisms to optimize CH selection and cluster formation during the set-up phase have been proposed for extending the stable operation period of the network until any node depletes its energy. However, the existing mechanisms assume that the traffic load contributed by each node is the same, in other words, same amount of data are sent to CH from the member nodes during each scheduled round. This paper assumes the nodes contribute traffic load at different rates, and consequently proposes an energy-efficient clustering algorithm by considering both the residual node energy and the traffic load contribution of each node during the set-up phase. The proposed algorithm makes nodes with more residual energy and less traffic load contribution get more chances to become CHs. Furthermore, clusters are adaptively organized in a way that the deviation of ratio between the total cluster energy and the total cluster traffic load (ETRatio) is limited, in order to balance the energy usage among the clusters. Performance evaluation shows that the proposed algorithm extends the stable operation period of the network significantly.

Abstract In this paper, we propose a finite‐state Markov model for per‐user service of an opportunistic scheduling scheme over Rayleigh fading channels, where a single base station serves an arbitrary number of users. By approximating the power gain of Rayleigh fading channels as finite‐state Markov processes, we develop an algorithm to obtain dynamic stochastic model of the transmission service, received by an individual user for a saturated scenario, where user data queues are highly loaded. The proposed analytical model is a finite‐state Markov process. We provide a comprehensive comparison between the predicted results by the proposed analytical model and the simulation results, which demonstrate a high degree of match between the two sets. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract In this paper, a rate‐based admission control scheme for a single shared wireless base station with opportunistic scheduling and adaptive modulation and coding (AMC) is proposed. The proposed admission scheme maintains minimum average rates of the admitted users, i.e., new users will be admitted if the base station has enough resources to support the required minimum average transmission rates of all users. The proposed scheme relies on an analytical model for the average per‐user rates of an opportunistic scheduling in an unsaturated scenario, where some queues may be empty for certain periods of time. We provide extensive simulation results to demonstrate the accuracy of the base analytical model on which our admission scheme relies. Copyright © 2009 John Wiley & Sons, Ltd.

Two Hybrid ARQ (HARQ) schemes based on selective decode and forward are considered for a cooperative half-duplex relay channel. Two time slot types: T1slot for relay listening and T2slot for relay forwarding are assumed to accommodate half duplex relaying. The considered HARQ schemes differ in the frequency of the ARQ feedback: one is frame and the other is slot based where each frame is composed of one T1followed by one T2slot. Two types of encodings: repetition coding (RC) and unconstrained coding (UC) (also known as incremental redundancy) are assumed. Outage performance analysis is carried out for both RC and UC. The state transition models of the considered protocols are presented and are used to analytically calculate the HARQ throughput and latency performance; thus avoiding time consuming Monte Carlo based evaluations. The provided analysis enables us to predict the system performance and tune its transmission parameters (transmission rate and frame structure) for any combination of the signal to noise ratio (SNR) of the constituent links.

In this paper, we consider a three-node relay system with a hybrid relaying scheme, where the relay, based on its decoding status, switches between decode-and-forward (DF) and compress-and-forward (CF) adaptively. We propose according HARQ strategy and analyze its throughput performance. Then, we consider practical implementation issues by enhancing the ability of the feedback channel from the destination to the relay to convey a few extra bits (only 2 bits in this paper) and propose a modified HARQ scheme accordingly. The combined technique exhibits superior performance over direct transmission and conventional DF.

In this paper, we consider maximum rate opportunistic scheduling from a single wireless base station with a single antenna to multiple mobile users, each equipped with a single antenna. We show that a finite-state Markovian model can capture the dynamics of a single user's service, namely peruser service. We consider a saturated scenario, where the base station always has buffered data for transmission.

We noticed that in a relay system, the assumption of global Channel State Information (CSI) knowledge is unrealistic and normally, only the receiver but not the transmitter side has CSI knowledge. In this paper, we analyzed, with receive CSI, the performance of hybrid relaying scheme in the half-duplex relay channel in terms of the outage probability and expected throughput, where the relay switches between decode-and-forward (DF) and compress-and-forward (CF) modes adaptively according to the current channel realizations. Moreover, since the original CF technique requires global CSI at the relay to calculate the optimal compression rate, we established a restricted feedback link to convey extremely limited CSI (only 3 bits in our results) from the destination to the relay and develop a more practical hybrid relaying scheme. Simulation results revealed that the hybrid relaying scheme with restricted feedback outperforms DF especially when the link between source and relay is of low quality.

This paper highlights the deficiencies of classical figures of merit such as throughput and fairness index to evaluate different scheduling algorithms and proposes a new complementary figure of merit called as transport-throughput, which has been inspired from information theory literature to better represent trade-offs among throughput, fairness and coverage associated with a given scheduling algorithm. Furthermore, new objective functions for opportunistic scheduling are proposed that utilize the knowledge about the geographic distance of mobile terminals from the base station. The proposed concept can be easily integrated as an extension to classical scheduling algorithms and provides the ability to control the effective distribution of throughput across the network

In this paper we propose a novel flexible amplify and forward (AF) relaying protocol with different duplexing ratios. We show that the average throughput can be considerably improved by properly adjusting the duplex ratio. Here it is considered that the transmitted and received channel state information (CSI) are available at the relay. We introduce a new processing technique at relay for above case in order to achieve higher throughput and lower bit error rate (BER) than traditional schemes. Also BER upper bounds for our presented cooperative model are derived.

It is well-known that the values of IEEE 802.11 MAC parameters directly affect the utilization of the channel capacity and link layer throughput as well as higher layers performance. This paper first studies the throughput of ad hoc network under various 802.11 MAC parameters by developing a 3-dimensional Markov chain. Based on this model, it is mathematically proved that the current values of 802.11 parameters result in dramatic throughput degradation. Then, the optimum values of 802.11 parameters that lead to maximum 802.11 MAC throughput will be proposed.

Decode-and-forward (DF) is one of the most popular approach to transmit information over a cooperative relay channel. In DF, information coming from different sources is simply combined regardless of their respective link qualities, and thus DF is not optimized for any propagation conditions. In this paper, we propose a novel transmit cooperation scheme that exploits asymmetric link qualities by using layered higher order modulation. A partial forwarding approach is followed by taking advantage of the layered structure: one part of the information is forwarded by a relay for increasing the robustness of the direct link and the rest of the information flows through the boosted direct link. Moreover, our scheme is based on a distributed serially concatenated encoding structure that can accommodate iterative decoding to further improve its performance. The complexity of our scheme is seemingly identical to DF scheme. Performance evaluation shows that our scheme provides a better spectral efficiency in asymmetric propagation conditions than DF scheme for both non-iterative and iterative decoding.

Optimum decoding of the cooperative full decode and forward (DF) scheme is derived. It is shown that optimum decoding requires marginalization of a so called partially observed likelihood function over all relay decoder outcomes. Bhattacharyya bound on relay decoding error is used to fully factorize the partially observed likelihood function. The full factorization will allow efficient marginalization over the joint source-relay code space. A tight analytical error bound is derived that its computation further requires efficient tools to compute weight structure of the joint source-relay code. For a simple (7,4) Hamming code under fast fading condition the derived optimum decoder significantly outperforms the conventional decoder. The optimum decoder is more robust to weak source-relay link conditions.

A model that effectively captures the relay decoding error is proposed for a distributed turbo code (DTC) over a cooperative relay channel. The model consists of a memoryless binary symmetric channel (BSC) placed before the relay encoder, hence called pre-BSC. Accordingly the required iterative decoder is derived that consists of an extra update function to be applied on extrinsic information before their exchange. The derived update function is in form of a smooth clipping and therefore the overall decoder is called clipped iterative decoder (clipped ED). Clipped ID is robust to the relay decoding error and is able to significantly improve the final performance, especially when source-to-relay link becomes erroneous.

Linear interpolation is often employed for chunk-based OFDMA uplink because of its low complexity and good performance. However, the linear interpolation error induced by the mismatch between the linear interpolation model and the actual channel can be detrimental to high-data-rate systems operating in high signal-to-noise-ratio (SNR) range or high mobility scenario. In this paper, enhanced linear interpolation schemes with small time-direction interpolation error are proposed, particularly for the chunk-based OFDMA uplink. Unlike the conventional linear interpolation, which is based on the assumption that the real and imaginary parts of channel coefficients are separately linearly varying, the proposed schemes are based on an interesting observation that the amplitude and phase of a communication channel are also linearly varying within a short time duration, e.g. a chunk duration. Simulation results show the proposed schemes can effectively improve the performance.

When channel state information (CSI) is not available to the transmitter, outage events might happen and automatic repeat request (ARQ) is implemented to ensure reliable transmission in such case. In this paper, we consider a three nodes relay system with hybrid relay scheme, where the relay, based on it decoding status, could switch between decode-and-forward (DF) and compress-and-forward (CF) adaptively. We notice that CSI is required when CF is deployed and consider practical implementation issues by enhancing the ability of feedback channel from the destination to the relay to convey a few extra bits (only 2 bits in this paper) in addition to the ACK/NACK bit and propose a new ARQ scheme. The modified scheme allows the relay to utilize various relay schemes more flexibly according to its coding status and the extra feedback bits. ARQ strategies with hybrid relay schemes exhibits superior performance over direct transmission and pure DF, especially when the relay is close to the destination.

In this paper, we extend a well-developed quantization scheme to block fading relay system using compress-and-forward and propose a new achievable rate based quantization scheme (ARBQS). A new signal combination scheme with less complexity is also proposed accordingly. Based on the scalar quantizer obtained, vector quantizer with Trellis coded quantization (TCQ) scheme is provided. While many quantization schemes have concentrated on minimization of quantization distortion, our simulations results indicate that the new scheme achieves better performance in both AWGN case and block fading case without distortion minimization and achieve higher compression efficiency and reduced complexity simultaneously.

Automatic Repeat re-Quest (AQR) is implemented to ensure reliable transmission when channel state information (CSI) is not available to the source and the selected transmission rate is not supported by the current channel realization. We consider a relay system with hybrid relay scheme, where the relay switches between decode-and-forward (DF) and compress-and-forward (CF) adapting to the decoding status. In such case, we propose new ARQ strategy and analyze its performance in terms of maximum throughput, average reward and inter-renewal time. Compared with pure DF, the hybrid relay schemes show considerable gain.

Dynamic and flexible spectrum management schemes can improve spectrum utilization efficiency and hence are promising approaches to satisfy the increasing demand for radio spectrum. This work investigates the possibility for competing network operators to partially cooperate without any operational information exchange and to exploit the instantaneous opportunities for sharing which appear within each network. Under the proposed two novel dynamic spectrum selection (DSS) protocols all handsets have a primitive cognitive ability in order to enable multi-operator functionalities for inter-operator DSS. The worst case scenario for voice traffic where two networks with the same radio access technology (RAT) having an entirely correlated average traffic distribution (same capacity demand) during the busy hour is investigated. It has been shown through a UMTS system level simulator that the suggested protocols can increase each operator's throughput by up to 10% compared to the conventional fixed spectrum allocation (FSA) scheme, and hence better spectrum utilization can be accomplished. Also, the effects of queuing incoming and handover calls before blocking or dropping them on DSS gains have been investigated. The results suggest an increase of up to 25% in throughput against the FSA if the DSS protocol and the queuing scheme are implemented.

In this paper, a novel framework is proposed to integrate spectrum (resource) sharing into multihop scheduling in relay-assisted systems. This approach provides an effective solution to minimize the effect of extra resources that are required in multihop transmission. Particularly, this approach can be combined with different topologies of resource scheduling to provide better performance in terms of throughput and coverage compared to benchmark non-sharing algorithms.

The log-normal probability distribution has been commonly used in wireless communications to model the shadowing and, recently, the small-scale fading for indoor ultrawideband (UWB) communications. In this paper, a tight closed-form approximation of the ergodic capacity over log-normal fading channels is derived. This expression can be easily used to evaluate and compare the ergodic capacities of communication systems operating over log-normal fading channels. We also utilize this expression to show that the capacity of a multi-antennas UWB system operating over the IEEE 802.15.3a channel can be improved mainly through receive diversity.

Abstract This paper presents novel spectral resource management (SRM) functionalities of WINNER ‡ The WINNER project is a major European research activity to develop a flexible and scalable radio access interface for future wireless communication systems. project. The proposed SRM spectrum architecture uses more efficient resource allocation techniques where flexible spectrum access and usage leads to more capable and faster services with high quality‐of‐service (QoS) giving more user satisfaction than conventional networks. In this context, short‐term (ST) spectrum assignment is proposed where spectral resources are exchanged between WINNER radio access networks (RANs) on a fast basis, leading to a better utilization of the spectrum. Moreover, multi‐band scheduler (MBS) and base station (BS) to BS communications are presented as key enablers for efficient dynamic spectrum management. Performance gains of ST spectrum assignment are analyzed in a multi‐network, multi‐cell environment based on realistic traffic patterns while taking inter‐cell interference into account. The results confirm that spectrum availability and utilization can be considerably enhanced. Copyright © 2008 John Wiley & Sons, Ltd.

The log-normal probability distribution is commonly used in wireless communications to model the shadowing and, more recently, the small-scale fading for indoor ultra-wideband communications. In this paper, an accurate closed-form approximation of the ergodic capacity over log-normal fading channels is derived. This expression can be easily used to evaluate and compare the ergodic capacity of communication systems operating over log-normal fading channels.

A generic cooperative BICM system is described and an analytical model capturing relay error through a binary symmetric channel (BSC) is presented. The analytical model is called Post-BSC model as the BSC channel is placed after encoder of the relay node. Tight BER upper bound is derived for the presented model and close match between simulation and analysis for the presented model is observed. However it is also observed that the proposed model is optimistic and fails to effectively model the original system at low SNR conditions of the source to relay link. Despite this fact the incorporation of the Post-BSC model based maximum likelihood decoding greatly improves the system BER with respect to the original DF scheme.

A generic cooperative MIMO BICM system is described. Achievable rates are computed based on the extended equivalent binary input channel model of the original BICM system. Full decode and forward is assumed at the relay node. Two types of two-phased transmission/reception protocols are employed to establish orthogonal transmission/reception of the relay node. The achievable rate results are provided for different combinations of modulation orders and the number of antennas used at the source and relay nodes. Quantitative results provided in this paper could serve as a guide on when to engage cooperative transmission and how to choose proper constellations and puncturing ratios for the practical BICM coded systems. Comparison of the considered BICM system with other possible cooperative coded systems is also crucial that this paper due to lack of space for exposition misses to address.

In OFDM-based systems, data symbols are usually transmitted together with pilots, which are used for channel estimation or prediction. More pilots in use can further improve the accuracy of channel estimation or prediction. However, this can also result in the efficiency loss of channel gain allocated for data transmission. In this paper, we investigate the joint pilot and data adaptation regarding pilot allocation, payload power and bits. Three different implementation approaches are proposed, the performance is evaluated and shows joint pilot and data adaptation can considerably improve system overall performance, and the efficiency gain related to the pilot-allocation adaptation can be significant.

This paper presents a method to counteract the detrimental effects of carrier frequency offsets (CFOs) in the uplink of OFDMA-based wireless networks. The CFO values of different users are first estimated from the received decomposed signal using the best linear unbiased estimator (BLUE). The CFOs effects are then compensated based on the estimated values and also taking advantage of an iterative parallel interference cancellation (PIC) scheme. A novel contribution of this paper is introducing an iterative concatenated estimation and compensation algorithm for suppressing the residual interference due to imperfect estimators. The introduced method is successful where the estimator converges after only one iteration with a significant improvement in estimation accuracy. Also very close BER performance results can be achieved to that of systems with perfect knowledge or without CFOs particularly at practical SNRs.

This paper presents a novel blind method to estimate carrier frequency offsets (CFOs) in OFDMA uplink. The major idea is employing a linear precoder to establish time correlation, which offers a second-order moments based blind CFO estimation for each user. With a careful precoder design, the interference from adjacent users can be considerably mitigated in CFO estimation. Since the majority of interference power is from adjacent users, the proposed method shows significant robustness to multiuser interference (MUI) even without the aid of virtual subcarriers. Simulation results show that the proposed approach offers significant performance improvement in comparison with state-of-the-art approaches in multiuser environments.

This paper investigates spectrum sharing (in the form of code sharing) between two Universal Mobile Telecommunication System (UMTS) operators in the UMTS extension band (2500-2690 MHz) with equal and unequal number of proprietary carriers, respectively. The paper proposes a dynamic spectrum allocation (DSA) algorithm to address the problem of spectrum sharing between two operators on a non-pool basis. It also investigates the impact of adjacent channel interference (ACI) on the spectrum sharing gain. Additionally, an architecture that enables spectrum sharing to take place between two or more UMTS operators is presented. The simulated performance of the proposed DSA algorithm shows that under peak-hour loading, up to 32% increase in capacity can be obtained when compared to currently used fixed spectrum allocation (FSA).

In this paper, the efficiencies of different interference coordination schemes are evaluated for emerging wireless networks and the possible impact on intra-cell scheduling is studied through extensive simulations. The results show that pure fractional frequency reuse can provide similar improvement in the cell-edge throughput compared to power coordinated counterpart at a less cost in terms of overall throughput. Moreover, it can provide fairer distribution of throughput in both central as well as cell-edge areas. However, this scheme can not mange asymmetrical changes in the distribution of users across different cells in the entire system. As a result, a power coordination mechanism would be still necessary on top of such flexible frequency reuse schemes.

One of the key features of High-Speed Downlink Packet Access (HSDPA) is Adaptive Modulation and Coding (AMC). Link Adaptation at Node-B is done on the basis of Channel Quality Indicator (CQI) reports sent by the User Equipment (UE). However there is a delay of about 10 ms between computation of a CQI report at UE and the corresponding Transport Format and Resource Indicator (TFRI) selection at the Node-B. This delay significantly degrades the performance of link adaptation process. In order to compensate for this loss of performance, we propose predictive CQI reporting by the UE. A simple linear predictor based on NLMS (Normalized Least Mean Square) adaptation is shown through simulations to provide substantial gain in HSDPA link throughput. Results show that predictive CQI reporting provides larger gain in channel environments with medium to high Doppler and lack of multipath diversity.

In this paper, we propose a rate-adaptive bit and power loading approach for the OFDM-based relaying communications. The cooperative relay operates in the half-duplex amplify-and-forward mode. The source and the relay has the separate power constraints. The maximum-ratio combining is employed at the destination for maximizing the received SNR. Assuming the perfect channel knowledge available at all nodes, the proposed approach is to maximize the throughput (the number of bits/symbol) at the given power constraint and the target link performance. Unlike the water-filling method, the proposed approach does not need the iterative loading process, and can offer the sub-optimum performance. Computer simulations are used to test the proposed approach for various scenarios with respect to the relay location or the distributed power allocation.

A transmitter with cognitive capability can sense talk between the other transmitter-receiver pairs. When this transmitter knows full or partial message of the others, it can choose an efficient strategy to access the transmission medium. This is referred to as cognitive radio channel. This work aims to investigate multi-tone transmission over two-user cognitive radio channels where cross-talk interference is weak. Cognitive transmitter (Ttimes1) is assumed to have full knowledge of message that is sent by the other transmitter (Ttimes2) to its corresponding receiver (Rtimes2). Channel capacity is carefully analyzed for frequency-selective scenarios. Efficient power-allocation strategies at Ttimes1 are investigated for various wireless environments. It is shown that Ttimes1 can find an efficient resource-accessing strategy if the channel gain of Ttimes1-Rtimes1 (the corresponding receiver) link is larger than the channel gain of Ttimes2-Rtimes2 link. In this case, the cognitive transmitter Ttimes1 can offer better performance by employing equal power allocation approach. Otherwise, it is not worthy for Ttimes1 to access transmission medium of Ttimes2-Rtimes2 link.

A novel approach for implementation of opportunistic scheduling without explicit feedback channels is proposed in this paper, which exploits the existing ARQ signals instead of feedback channels to reduce the complexity of implementation. Monte Carlo simulation results demonstrate the efficacy of the proposed approach in harvesting multiuser diversity gain. The proposed approach enables implementation of opportunistic scheduling in a variety of wireless networks, such as the IEEE 802.11, without feedback facilities for collecting partial channel state information from users.

This paper independently derives the probability of any pair of uniformly-distributed nodes to be within transmission range of each other in a square-shaped area. It then explores, via simulation, some new applications of this expression. The applications are relevant for scenarios where node mobility is governed by the popular random walk or random waypoint mobility models (RWkMM and RWPMM). Under the RWPMM with pausing, at any time, some nodes will be mobile and some stationary. The positions of mobile nodes are drawn from a nonuniform distribution, while a uniform distribution applies to the stationary nodes. In various forms of the RWkMM, the node spatial distribution is uniform in its steady state. The studied applications include calculating the expected node degree and the node isolation probability. Simulation results show that the considered model is able to predict these connectivity-related properties near-perfectly under a paused RWPMM and with all mobility scenarios under the RWk with reflection model. With the RWPMM, the accuracy decreases as the fraction of time the nodes spend moving increases. However, it is still generally better than the simple pir2/A disk-covering model, which is often employed for calculating network connectivity-related properties in ad hoc networks. Further application of the considered methods is exemplified by calculation of an accurate upper bound on the per-node transmission capacity for contention-based networks, when the nodes are uniformly distributed.

A novel approach for extension of soft decode and forward strategy into higher order MQAM modulation is proposed. A new soft mapping function is introduced to map soft bits onto a point in the complex plane. It is shown that the proposed soft decode and forward (SDF) scheme attains the same level of diversity as amplify and forward (AF) scheme while improves the overall system spectrum efficiency through forwarding with higher order modulations. Simulation results show that new scheme outperforms DF for different links conditions.

Future mobile networks providing multitude of services with large range of QoS requirements has made the importance of Radio Resource Management (RRM) ever more significant. Traditional scheduling schemes have approached resource allocation mainly from a cell point of view to large extent ignoring effects of multi-cell architecture. Recently, Load Matrix (LM) concept has been proposed which facilitates joint management of interference within and between cells for allocation of radio resources. In this paper, we assess the Load Matrix scheduling performance when combined with traditional schedulers. In addition, a new LM algorithm for efficient RRM, called Global Proportional Fair (GPF) is proposed. The results show that GPF algorithm with an appropriate window size outperforms traditional scheduling schemes in terms of both throughput and fairness. It combines the throughput advantage of Max C/I and the fairness advantage of Proportional Fair scheduler.

In this paper, we investigate the optimum pilot placement along time direction for the chunk-based OFDMA uplink. The proposed optimization is performed within a single chunk, where the channel estimation is based on the least-square linear interpolation. Unlike the state-of-the-art optimum pilot placements, the proposed scheme takes into account the impact of interpolation error in addition to the noise. It is established that in the presence of the large Doppler shift, the impact of interpolation error shows the dominative effect in the large SNR range, the optimum pilot spacing should be 1/radic3 of the chunk length regardless of the channel realization. However, in the most practical scenarios, the impact of the interpolation error is too small to affect the overall performance compared to the noise. Therefore, the optimization of pilot placement should be focused on minimizing the noise.

In this paper, we analyze the block error rate (BLER) and bit error rate (BER) of soft decode-and-forward (SDF) using distributed Turbo codes, which is proposed recently to mitigate error propagation caused by decoding error in the relay node. Union bound (UB) in fading channels is derived and compared with simulation results. In order to get tight bounds for block fading case, limit-before-average technique is used. Furthermore, we extend our analysis to the space-time cooperation framework. The analysis and simulation show that the derived bound is very tight.

It has been pointed out that Slepian-Wolf (SW) coding is efficient to compress data with side information available at the receiver. However, most papers assume that the compressed information is perfectly known to the receiver. In this paper, we consider more practical assumptions that the channel between the relay and the destination is not perfect and error protection need to be implemented. Accordingly, a soft Slepian-Wolf decoding structure is proposed. The new structure not only supports soft Slepian-Wolf decoding within one level, but it also allows soft information passing between different levels. We also consider the relationship between the codes for error protection and the codes for compression and propose a joint decoding and decompressing algorithm to further improve the performance.

This paper investigates the downlink handover (soft/softer/hard) performance of wideband code division multiple access (WCDMA) based 3rdgeneration universal mobile telecommunication system (UMTS), as it is known that the downlink capacity of UMTS is very sensitive to the extent of overlap area between adjacent cells and power margin between them. Factors influencing the handover performance such as the correlation between the multipath radio channels of the two links, limiting number of Rake fingers in a handset, imperfect channel estimation, etc. that cannot be modeled adequately in system-level simulations are investigated via link-level simulations. It is also shown that the geometry factor has an influence on the handover performance and exhibits a threshold value (which depends on the correlation between the multipath channels associated with the two links in a handover) above which the performance starts degrading. The variation of the handover gain with the closed loop power control (CLPC) step-size and space-time transmit diversity (STTD) is also quantified. These comprehensive results can be used as guidelines for more accurate coverage and capacity planning of UMTS networks.

The wireless hybrid enhanced mobile radio estimators (WHERE) consortium researches radio positioning techniques to improve various aspects of communications systems. In order to provide the benefits of position information available to communications systems, hybrid data fusion (HDF) techniques estimate reliable position information. Within this paper, we first present the scenarios and radio technologies evaluated by the WHERE consortium for wireless positioning. We compare conventional HDF approaches with two novel approaches developed within the framework of WHERE. Yet, HDF may still provide insufficient localization accuracy and reliability. Hence, we will research and develop new cooperative positioning algorithms, which exploit the available communications links among mobile terminals of heterogeneous wireless networks, to further enhance the positioning accuracy and reliability.

Adaptive modulation and coding (AMC) is one of the important features of high-speed downlink packet access (HSDPA). The link adaptation is done at the Node-B on the basis of channel quality indicator (CQI) reports sent by the user equipment (UE). However the performance of AMC scheme is degraded due to the large delay between computation of CQI report at UE and the corresponding transport format and resource indicator (TFRI) selection at the Node-B. An improved link adaptation approach, named CQI averaging herein, is proposed in this paper. In this approach, a number of consecutively received CQI reports from UE are mapped to equivalent signal to interference ratio (SIR) values and passed through a moving average filter. The resulting average is used to derive the CQI index (named Average CQI herein) to be followed at Node-B transmitter. This approach provides a significant gain, especially for medium vehicular speeds. The gain is more pronounced for channels that provide less multipath diversity. Simulation results are used to relate desirable filter length of the moving average filter with the characteristics of the radio channel. Subsequently, an algorithm for adaptation of filter length based on the statistics of CQI reports is proposed.

System architecture evolution (SAE) is one of the key challenges for the long term evolution(LTE) of 3G research, charted by 3GPP, aiming to develop a new core network for improving the IP based packet-switched network performance. This paper considers different mobility management schemes for different SAE core network architectural options proposed in 3GPP, and evaluates their performance according to signalling cost by using analytical modelling based on the random walk model. Our analysis shows that the mobility management performance of the different options on both Idle and Active mode. Regarding the architecture, options 2 and 3 have the best trade-off in total signalling cost performance in both modes and signalling load on each network node for system scalability. For mobility management, Proxy MIPv6 has shown that it is able to effectively reduce the signalling cost for mobility management for all architecture options.

Doubly differential modem turns out to be a promising technology for coping with unknown frequency offsets with the pay of signal-to-noise ratio (SNR). In this paper, we propose to compensate the SNR loss by employing the detection-forward cooperative relay. The receiver can employ two kind of combiners to attain the achievable spatial diversity-gain. Performance analysis is carefully investigated for the Rayleigh-fading channel. It is shown that the SNR-compensation is satisfied for the large-SNR range.

In this paper, we investigate the adaptive bit and power loading approach for the OFDM-based relaying communications. The relaying protocol is half-duplex outage symbol-level-selection detection-and-forward. The source and the relay has the separate power constraint. The maximum-ratio combining is employed at the destination to achieve the maximum distributed spatial diversity-gain. Assuming the perfect channel knowledge available at all nodes, we propose a sub-optimum rate-adaptive bit and power loading approach to maximize the number of bits/symbol for the target link performance. The proposed approach offers lower complexity for implementation as well as the integer number of bits/symbol. Computer simulations are used to examine the proposed approach for various scenarios with respect to the relay location and the distributed power allocation.

Cooperative communications can exploit the distributed spatial diversity-gain to improve the link performance. In this paper, we investigate the application of adaptive modulation concept to the decode-and-forward (DF) based cooperative network. With the relay nodes geographically close to the destination, we assume the perfect channel feedback is available only at the relay nodes, and propose a class of novel modulation-adaptive cooperation schemes (MACSs). The proposed schemes are first investigated in the single-relay scenario, and then extended to the multi-relay scenario. Simulation results show that the proposed schemes can offer the significant throughput-improvement in comparison with conventional DF systems.

This paper studies the area of link adaptation for multicarrier code division multiple access (MC-CDMA) radio interface and adapts the rate and transmission power jointly over temporal as well as spectral domains. On one side, frequency domain spreading lends a high degree of frequency diversity to MC-CDMA. On the other, it has been identified as the main hurdle when it comes to spectral domain rate allocation in MC- CDMA This paper presents band based MC-CDMA (BB-MC- CDMA) architecture as an efficient scheme to practically realize the spectral domain rate allocation in MC-CDMA without undergoing the drawbacks of other published techniques for this purpose. BB-MC-CDMA asks for the elimination of frequency interleaving, usually considered to be an essential ingredient of MC-CDMA. It also suggests an intelligent reduction in the spreading factor in a channel aware fashion. Furthermore, based on BB-MC-CDMA architecture, joint rate and power adaptation over time and frequency (JRPATF) has been shown to outperform the frequency domain power allocation only.

The authors present the use of a genetic algorithm (GA) model as a solution approach to the dynamic spectrum allocation (DSA) problem considered as a difficult combinatorial optimisation problem. The proposed multi-objective GA model enhances overall spectral efficiency of the network, while optimising its own spectrum utilisation to generate accessible spectrum opportunities for other radio technologies. A novel two-dimensional encoding technique is defined to represent solutions in the problem domain and the technique enables significantly shorter convergence times. A simulation tool has been developed to model the GA-based DSA and to compare the new scheme with the conventional fixed spectrum allocation (FSA) scheme under both uniform and non-uniform traffic distributions. The proposed scheme significantly outperformed the FSA scheme both in terms of spectral efficiency gain and spectral utilisation.

Timing and frequency synchronisation for the uplink of OFDMA systems is discussed. The uplink synchronisation procedure is presented and a novel timing and frequency offset estimation scheme is proposed. The timing and frequency offsets are estimated by identifying the differential phases of the training subcarriers in frequency and time dimensions, respectively. The frequency offset is estimated ahead of the timing offset, after which intercarrier interference compensation is carried out based on the estimated frequency offset. Finally, the timing offset is estimated after eliminating the frequency offset's influence. The principle of best linear estimation is applied. Both the case of a single user and that of multiple users simultaneously accessing the network are considered. In contrast to other methods, the proposed scheme has moderate complexity and allows flexible subcarrier assignment schemes. The analyses and simulation results demonstrate the accuracy of the proposed scheme in the uplink channels.

Future mobile communication systems will be designed to support a wide range of data rates with complex quality of service matrix. It is becoming more challenging to optimize the radio resource management and maximise the system capacity whilst meeting the required quality of service from user's point of view. Traditional schemes have approached this problem mainly focusing on resources within a cell and to large extent ignoring effects of multi-cell architecture. This paper addresses the influence of multi-cell interference on overall radio resource utilisation and proposes a novel approach, setting a new direction for future research on resource scheduling strategies in a multi-cell system. It proposes a concept called load matrix (LM) which facilitates joint management of interference within and between cells for allocation of radio resources. Simulation results show significant improvement in the resource utilization and overall network performance. Using the LM strategy, average cell throughput can be increased as much as 30% compared to a benchmark algorithm. Results also show that maintaining cell interference within a margin instead of a hard target can significantly improve resource utilization.

In multi-hop mobile ad hoc networks (MANETs) it is difficult to provide throughput guarantees due to the lack of central co-ordination, mutual contention between nodes, collisions, node mobility and an error-prone wireless channel. Existing QoS-aware admission control protocols for multi-hop MANETs either ignore the effect of collisions, or do not consider the increase in collision rate upon traffic admission. However, we have found that such effects have a significant impact on the achievable throughput. In this work we therefore present a novel low-complexity passive observation-based admission control scheme for ensuring that sessions' throughput requirements are upheld in a contention-based, collision-prone multi-hop MANET. We show, via simulations, that in large networks, even at offered traffic loads greatly exceeding the network capacity, at least 88% of admitted sessions maintain their desired throughput throughout their duration. For all traffic loads, average packet delay and packet loss ratio remain below 55ms and 1% respectively.

The rapid growth of WLANs provides an opportunity for appropriately equipped user terminals to access the evolutionary 3GPP services via high speed WLAN at low cost. Interworking of UMTS and WLANs aims to extend 3 GPP services and functionality to the WLAN environment, thus effectively making it a complementary access technology. One of the research challenges for this interworking is the design of mobility management mechanisms to support seamless service continuity. In this work, first we undertake a holistic review of the mobility problem, considering handovers at different protocol layers. Then we propose a cross-layer mechanism for better coordination between different layers to improve the handover performance and thereby meet the requirements of seamless service continuity. Finally we present the results from an experimental study of the proposed mechanism.

Channel estimation for the chunk-based OFDMA uplink communications is based on the non-ideal interpolation schemes, e.g., polynomial interpolation. The channel estimation performance depends on the modeling mismatch, pilot pattern and the signal-to-noise ratio. In this paper, we first investigate the channel estimation performance for the polynomial interpolation, which takes into account the impact of modeling mismatch and thermal noise. This is then followed by a novel sub- optimum pilot-placement scheme, which can considerably improve channel estimation and overall system performance for the consecutive chunks scenario, i.e., each user has several consecutive chunks along the frequency direction.

MFTP is a multicast transport protocol optimized for reliable file transfers. MFTP is rate based, which means that the transmission rate can be configured. By limiting the transmission rate one can reserve bandwidth for other protocols. MFTP uses a NACK based error correction scheme where the server is almost constantly sending data and very seldom has to wait for acknowledgements from the clients. MFTP sends a file in passes. The first pass represents the complete file while subsequent passes represent data identified as not received by clients in NACK messages. This makes MFTP specially suited for transmissions over links with long delays, for example satellite links. This paper analyses MFTP multicast performance over Inmarsat BGAN multicast satellite system. The full interaction between the MFTP server and MFTP clients deployed over the Inmarsat BGAN down link multicast delivery mechanism along with BGAN uplink access mechanism is considered. The performance evaluation is based on a BGAN multicast system level simulator. The analyses aims to evaluate and optimize MFTP performance in Inmarsat BGAN system in terms of file transfer delay and system throughput as a function of available capacity, client population size, data product size, channel error characteristics, and MFTP protocol settings.

Cooperative communications can exploit the distributed spatial diversity to improve the multiple-link performance. In this paper, the average symbol error probability (SEP) is carefully investigated for the three-node relaying structure, in which the relaying protocol is amplify-and-forward, and the modulation scheme can be N-PSK or N-QAM. We first exploit the optimistic condition to derive the lower bound of SEP. This is the generalization of the existing Anghel-Kaveh formulation. Then, the upper bound is also provided based on the exponential approximation to the Bessel function. This bound turns out to be tighter than the Anghel-Kaveh upper bound. Numerical results are provided to confirm the analytical analysis.

In this paper, we investigate the optimum pilot placement for the chunk-based OFDMA uplink. The proposed scheme is optimized for the single-chunk scenario, i.e., the channel estimation is carried out for each individual chunk, and the channel estimator is based on the least-square linear interpolation. Unlike the state-of-the-art optimum pilot placements, the proposed scheme takes into account the impact of modeling mismatch in addition to the noise. It is established that the optimum pilot placement for the large-SNR range should minimize the impact of the modeling mismatch. The optimum pilot spacing along the frequency direction is 1/radic3 of the chunk bandwidth. The pilot spacing should be increased (up to the chunk width) with decreasing the signal-to-noise ratio. Computer simulations are provided, and show a good match with the analytical result.

In order for third-generation partnership project (3GPP) networks to cope with the rapid growth of IP data traffic, the location management requires further enhancement to accommodate the burst data traffic characteristics of packet switched (PS) services. The universal mobile telecommunication system (UMTS) location management solutions for PS services in 3GPP networks has been investigated, and an inactivity counter mechanism in PS domain to reduce the location management cost of inactivate users has been proposed. An analytical model accommodating diverse call and mobility characteristics of user equipment to evaluate the performance of the inactivity counter mechanism has also been developed. Using the proposed model, the performance of the inactivity counter mechanism in UMTS terrestrial radio access network recommended by 3GPP is investigated, the cost reduction obtained by the proposed mechanism when compared with the original location strategy used in the PS domain is then analysed, which also shows the effect of system parameters and users' mobility and service patterns on the location management costs.

Novel low-density signature (LDS) structure is proposed for synchronous code division multiple access (CDMA) systems for an uplink communication over AWGN channel. It arranges the system such that the interference pattern being seen by each user at each received sampled chip is different. Furthermore, new near-optimum chip-level iterative multiuser decoder is suggested to exploit the proposed structure. It is shown via computer simulations that, without forward error correction (FEC) coding, the proposed LDS structure could achieve near single-user performance with up to 200% loading condition. As the proposed iterative decoding converges relatively fast, the complexity is kept much more affordable than that of optimum multiuser detection (MUD) with conventional structure.

This paper describes a mechanism of forwarding secure state information associated to communication sessions, between middleboxes belonging to different Radio Access Networks (RANs). The transfer of state information among RANs could support service integrity and continuity by maintaining a mobile user's multimedia sessions which may otherwise be dropped and also minimize security vulnerabilities. The paper demonstrates how the context transfer protocol could be employed for this purpose to forward certain security information from the old to the new middlebox to support multimedia session maintenance during mobility and also at the same time notify the previous middlebox to close unnecessary open ports for improved security and resolve vulnerability. A number of test scenarios are used to demonstrate how middleboxes could intervene with multimedia sessions during mobility and show how context transfer can provide a solution for improving the performance in the multimedia session re-establishment as well as enhancing middlebox security.

In mobile ad hoc networks (MANETs), accurate throughput-constrained Quality of Service (QoS) routing and admission control have proven difficult to achieve, mainly due to node mobility and contention for channel access. In this paper we propose a solution to those problems, utilising the Dynamic Source Routing (DSR) protocol for basic routing. Our design considers the throughput requirements of data sessions and how these are affected by protocol overheads and contention between nodes. Furthermore, in contrast to previous work, the time wasted at the MAC layer by collisions and channel access delays, is also considered. Simulation results show that in a stationary scenario with high offered load, and at the cost of increased routing overhead, our protocol more than doubles session completion rate (SCR) and reduces average packet delay by a factor of seven compared to classic DSR. Even in a highly mobile scenario, it can double SCR and cut average packet delay to a third.

This paper studies adaptive power allocation among sub-carriers in MC-CDMA. Due to intrinsic nature of MC-CDMA; carrier based power allocation schemes cause MAI (multiple access interference) enhancements, hence fail at higher system loads. We propose a band based dynamic link adaptation (BBDLA) scheme that preserves orthogonality (among users) by spreading user's signal only over a band of adjacent N sub-carriers (Nc) of the channel. Hence, it allows band based power allocation without causing any MAI. However, with only N orthogonal users supported on a particular band, BBDLA essentially proposes a hybrid of FDMA with MC-CDMA where Bands and transmit powers are optimally assigned to users by base station (in accordance with their channel state). Optimum band allocation for BBDLA is found to be computationally intractable hence a sub-optimal heuristic approach is proposed with equal power distribution among all assigned bands for each user. Effect of Bcover choice of N is studied and BBDLA with suitably chosen N, is shown to outperform other published carrier based power allocation schemes while it maintain almost single user BER performance up to 62% of full system loading.

In this paper we present network-centric and user- centric routing mechanisms designed for the wireless world initiative new radio (WINNER) multi-hop cellular air interface. We study the pros-and-cons of each mechanism and describe the interactions between routing and main radio resource management (RRM) functionalities.

It is foreseen that the next generation of cellular network would integrate the relaying or multihop scheme. In a multihop cellular architecture, the users are not only able to communicate directly to the base station (BS) but can also use some relay stations to relay their data to the BS. These relay stations can either be the users' mobile station (i.e. mobile relay station) or some low cost station placed at specific location (i.e. fixed relay station). The main objective of this paper is to compare the difference between the relaying network architecture with mobile or fixed relay station as well as their performance gain. At first, the different advantages and disadvantages for each relay station method are given. Then, the capacity gains of the two methods are compared with a dynamic system level simulator. For this we integrate the relay station into conventional cellular networks using the UMTS FDD air interface. The results show that under certain conditions the uplink capacity gain of 35% is readily achievable for both form of relay station.

It is foreseen that the next generation of cellular network would integrate the relaying or multihop scheme. In a multihop cellular architecture, the users are not only able to communicate directly to the base station (BS) but can also use some relay stations to relay their data to the BS. In such architecture, it may happen that a relayed user handover to another relay station during its communication: this process is called the inter-relay handoff. The main objective of this paper is to study how frequent the inter-relay handoff occurs and its impact on the relaying system performance. For this, different algorithms to decide when a user should inter-relay handover are proposed and tested through a dynamic system level simulator. We compare the capacity gain for the different algorithm with the conventional cellular networks using the UMTS FDD mode. The result showed that with an appropriate inter-relay handoff scheme, the uplink capacity gain of 35% is readily achievable.

Recently, the notion that a logical next step towards future mobile radio networks is to introduce multihop relaying into cellular networks, has gained wide acceptance. Nevertheless, due to the inherent drawbacks of multihop relaying, e.g., the requirement for extra radio resources for relaying hops, and the sensitivity to the quality of relaying routes, multihop cellular networks (MCNs) require a well-designed radio resource allocation strategy in order to secure performance gains. In this paper, the optimal radio resource allocation problem in MCNs, with the objective of throughput maximization, is formulated mathematically and proven to be NP-hard. Considering the prohibitive complexity of finding the optimal solution for such an NP-hard problem, we propose an efficient heuristic algorithm, named integrated radio resource allocation (IRRA), to find suboptimal solutions. The IRRA is featured as a low-complexity algorithm that involves not only base station (BS) resource scheduling, but also routing and relay station (RS) load balancing. Specifically, a load-based scheme is developed for routing. A mode-aware BS resource-scheduling scheme is proposed for handling links in different transmission modes, i.e., direct or multihop. Moreover, a priority-based RS load balancing approach is presented for the prevention of the overloading of RSs. Within the framework of the IRRA, the above three functions operate periodically with coordinated interactions. To prove the effectiveness of the proposed IRRA algorithm, a case study was carried out based on enhanced uplink UMTS terrestrial radio access/frequency-division duplex with fixed RSs. The IRRA is evaluated through system level simulations, and compared with two other cases: 1) nonrelaying and 2) relaying with a benchmark approach. The results show that the proposed algorithm can ensure significant gains in terms of cell throughput

The statistics-based channel estimators can estimate the channel state information (CSI) in the static channel but can only obtain the averaged CSI (ACSI) in the time-varying (TV) channel. This letter investigates both first-order statistics (FOS) and second-order statistics (SOS)-based channel estimators for the pseudo random postfix (PRP) orthogonal frequency-division multiplexing system in the slowly TV channel. It is shown that the FOS-based approach outperforms the SOS-based approaches in the ACSI estimation. Using estimated ACSIs for the channel equalization, simulation results indicate that the SOS-based approaches converge to the FOS-based approach in the high signal-to-noise ratio range.

Network scenarios beyond 3G assume the cooperation of operators with wireless access networks of different technologies in order to improve scalability and provide enhanced services to their mobile customers. While the selection of an optimised delivery path in such scenarios with multiple access networks is already a challenging task for unicast delivery, the problem becomes more severe for multicast services, where a potentially large group of heterogeneous receivers has to be served simultaneously via shared resources. In this paper we study the problem of selecting the optimal bearer paths for multicast services with groups of heterogeneous receivers in wireless networks with overlapping coverage. We propose an algorithm for bearer selection with different optimisation goals, demonstrating the existing tradeoff between user preference and resource efficiency.

Multimodality is a fact of human communication, increasingly our ways to communicate change and humans do interact with machines (be it a mundane ATM transaction, the calling of an automised call center, or the setting/disarming of a residential alarm system). However, these interactions are mostly limited to single input and output schemes, thus loosing a lot of additional information a human communication partner would sense, Multimodality was perceived to exactly tackle this point. This paper describes a framework and approach to operate multimodal interaction mechanisms in both the fixed as well as the mobile environments. The paper describes a scheme that facilitates the dynamic binding and release of user-interface devices (such as screens, keyboards, etc.) to support multimodal interactions in mobile environments and to enable the user to 'make use' of any possible user interface device available (and allowed), thus supporting the individuals changing communication environment. The principles and basic functionality of an adaptive multimodal human interface-device binding engine are outlined.

The inappropriate or under-use of spectrum offers new perspective for a secondary usage of spectrum between heterogeneous radio access technologies. This spectrum sharing can be achieved at the expense of an appropriate management of the co-channel interference between the different radio access technologies. This paper proposes an extended dynamic channel allocation (eDCA) scheme for providing distributed and real non SIR (signal to interference ratio) based spectrum sharing. The trunking gain performance of eDCA is analyzed and compared to simple fixed channel allocation (FCA), FCA with channel borrowing and DCA for FDMA based systems. Sensitivity analysis of the performance is provided. In particular, results show that careful inter-radio access technologies reuse frequency distance coordination is required between heterogeneous radio systems to ensure that pooling frequencies results in gain to the systems. The appropriate capacity management relies on the activation of the right strategy in the right traffic load condition

A key requirement for ease of migration from legacy to ambient networks is the elimination of dependencies between functionalities. Currently, in the case of designs for QoS and mobility in IP networks, it is apparent that there is a strong coupling between the two functions. One way to reduce this coupling is to remove the need to have QoS state within the network. This can be done if applications are able to manage the QoS parameters themselves. The basic idea is that the application is responsible for keeping the network in a congestion-free state in order to minimize the loss and delay experienced by the application, much as TCP does today for best effort traffic. On-line estimation of end-to-end packet loss can be used to monitor wireless link performance and to help adaptive applications to make better use of network resources. Existing work has focused on measuring and modeling packet loss in the Internet but most of these technologies do not address end-to-end path performance. This paper proposes an on-line stripe packet-pair probing approach to estimate the packet loss rate that applications may suffer. The approach uses 2-order Markov chain to model the loss rate and loss burstiness. To reduce the computational complexity, we employ maximum entropy to estimate the parameters. The paper validates the model via existing loss traces

As wireless communications are enhanced towards "beyond 3G" (B3G), multicast transmission is attracting more and more attention because it offers a more cost-effective provision of the multimedia content than the traditional unicast transmission. In this context, a problem emerges regarding the multimedia content delivery in a B3G environment, as users are reachable through several overlapping wireless access networks, which potentially are multicast-enabled. A mechanism is proposed in this paper to address this problem. The objective is to determine the best access network and the QoS level for each user in order to maximize the overall utilities in the system. This is achieved by three functions, namely, service scheduling (SS), network selection (NS) and QoS adaptation (QoSA), SS utilizes multicast transmission capability to save radio resource by scheduling a copy of the multimedia content to a group of users. NS and QoSA enable intelligent network resource allocation in overlapping multicast-enabled networks while respecting users' preferences and QoS expectation. Simulation results confirm the significant benefit of the proposed mechanism.

In this paper, an integrated radio resource allocation (IRRA) algorithm is proposed for enhanced uplink UTRA-FDD with fixed relay stations (RSs). It comprises three entities: load based route manager (LBRM), base station resource scheduler (BSRS), and relay station load balancer (RSLB). LBRM is responsible for the selection of user transmission routes, BSRS is for the scheduling of user transmission time and rates based on the available base station (BS) capacity, and RSLB is for the fine-tuning of user resource assignments (e.g. assigned rates) so as to prevent the overloading of RSs. The three entities operate periodically, synchronously, and with coordinated interactions. By system level simulations, the proposed algorithm is evaluated and compared with two other cases: 1) non-relaying, 2) relaying with a benchmark approach. The results show that IRRA can ensure significant gains in terms of cell throughput.

Multimedia Broadcast/Multicast Service (MBMS) is introduced in the Release 6 of the 3rdGeneration Partnership Project (3GPP) and is characterized by relatively longer interleaving depth. This paper investigates the exploitation of the longer interleaving depth by chip block interleaving to obtain intra-bit diversity. Both terrestrial and satellite modes of MBMS delivery mechanisms are studied and it is found out that chip block interleaving provides a reduction in the transmission power (2.5 dB in terrestrial flat Rayleigh fading channel, 0.75 dB in 4-tap 3GPP Case 3 radio channel, 1.5 dB in satellite line of sight Ricean flat fading channel and 0.7 dB in 9-tap satellite multipath fading due to intermediate module repeaters) at moderate speeds of 30-50 km/h and a moderate interleaving depth of 20 ms for 64 kbps data service. Moreover, it is also shown that for terrestrial mobile radio channels, the gain provided by chip block interleaving with block length equal to half the spreading factor and using only one transmit antenna is equal to that provided by Space Time Transmit Diversity (STTD) that requires two transmit antennas.

Dynamic spectrum allocation (DSA) seeks to exploit the variations in the loads of various radio-access networks to allocate the spectrum efficiently. Here, a spectrum manager implements DSA by periodically auctioning short-term spectrum licenses. We solve analytically the problem of the operator of a CDMA cell populated by delay-tolerant terminals operating at various data rates, on the downlink, and representing users with dissimilar "willingness to pay" (WtP). WtP is the most a user would pay for a correctly transferred information bit. The operator finds a revenue-maximising internal pricing and a service priority policy, along with a bid for spectrum. Our clear and specific analytical results apply to a wide variety of physical layer configurations. The optimal operating point can be easily obtained from the frame-success rate function. At the optimum, (with a convenient time scale) a terminal's contribution to revenues is the product of its WtP by its data rate; and the product of its WtP by its channel gain determines its service priority ("revenue per Hertz"). Assuming a second-price auction, the operator's optimal bid for a certain spectrum band equals the sum of the individual revenue contributions of the additional terminals that could be served, if the band is won

The paper addresses TCP performance enhancing proxy techniques broadly deployed in wireless networks. Drawing on available models for TCP latency, we describe an analytical model for the latency and the buffer requirements related to the split-TCP mechanism. Although the model applicability is broad, we present and evaluate the model in the context of geostationary satellite networks, where buffering requirements may become more dramatic. Simulation results are compared with the analytical model estimates and show that the model captures the impact of various parameters affecting the dynamics of the component connections traversing the terrestrial and the satellite network.

This letter proposes a novel carrier frequency offset (CFO) estimation method for generalized multicarrier code-division multiple access (CDMA) systems in unknown frequency-selective channels utilizing hidden pilots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear regression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator. Simulation results are presented together with the theoretical analysis, and a good match between them is observed.

We investigate the use of fixed-point methods for predicting the performance of multiple TCP flows sharing geostationary satellite links. The problem formulation is general in that it can address both error-free and error-prone links, proxy mechanisms such as split-TCP connections and account for asymmetry and different satellite network configurations. We apply the method in the specific context of bandwidth on demand (BoD) satellite links. The analytical approximations show good agreement with simulation results, although they tend to be optimistic when the link is not saturated. The main constraint upon the method applicability is the limited availability of analytical models for the MAC-induced packet delay under non-homogeneous load and prioritization mechanisms.

One of the key research issues in wireless systems is how to improve the system capacity. MIMO has been proven as an effective method to achieve this. Previously, the focus of MIMO-OFDM research in high performance metropolitan area network (HIPERMAN) systems was on space time coding (STC) and beamforming. Recently, multi-user detection (MUD) has emerged as a novel approach in MIMO-OFDM-based HIPERMAN systems. In this paper, we have proposed a new MAC design, which includes the new and flexible MAC frame structure and an efficient dynamic resource allocation algorithm, in order to accommodate the MUD techniques in uplink transmission in HIPERMAN systems. The performance of the new MAC design has been evaluated via simulation means. The simulation results show that the new MAC design based on MUD can significantly increase the system capacity.

Session initiation protocol (SIP) has been selected as the official end-to-end IP signalling protocol for establishing multimedia sessions in the IP-based universal mobile telecommunication systems (UMTS) network. Since a satellite component has been identified within UMTS, there is a need also to support SIP-based session establishment over satellite-UMTS (S-UMTS) to achieve an end-to-end seamless IP-based terrestrial/satellite network integration. To improve the session setup performance, a link layer retransmission based on the radio link control acknowledgement mode (RLC-AM) mechanisms is utilised. However the current RLC-AM procedure is found to be inefficient when applied to transactional-based applications such as session establishment. As such this paper proposes a scheme to alleviate this inefficiency and simulation results reveal that this scheme achieves a good compromise between delay and radio resource utilization under various channel conditions.

It has been proven that the centralised method provides optimum control for radio resource management. In this paper, we propose a soft capacity method by means of a homogenous near-optimum SIR target algorithm for CDMA cellular systems based on distributed power control (DPC) methods. Simulation results prove that the system capacity (or resources) is maximised if the SIR target management at the load controller is considered. Moreover, during high cell load, by removing users experiencing the worse link loss, a gain in the capacity is observed as well as reducing the noise rise in the system compare to the classical DPC fixed SIR target.

SIR estimation for fast closed loop power control (CLPC) of a WCDMA FDD downlink with space time transmit diversity (STTD) is analysed. It is shown that without STTD encoding/decoding of the DPCCH (dedicated physical control channel) pilot symbols of the two antennas, the SIR estimation suffers from inter-antenna interference. Without modifying the DPCCH pilot patterns in the current 3GPP standard, near-best performance is achieved by using all the pilot symbols for signal power estimation, but only the STTD decoded shaded symbols for interference estimation, to avoid the inter-antenna interference.

We study the choice of block interleavers in a MIMO-OFDM based broadband fixed wireless access system for the HIPERMAN and IEEE 802.16a standards with space-frequency trellis coding under typical channel conditions. It has previously been shown that a given code offers varying levels of error performance depending on the degree of frequency selectivity of the channel. Code performance was comparatively better in channels with higher frequency selectivity or delay spreads, while performance was particularly degraded in channels with low frequency selectivity. In order to extract the maximum possible frequency diversity and close this gap in performance, we investigate the use of appropriate interleavers. As different channels favour interleavers of different depths, we recommend suitable interleaver depths for the typical channels considered. With these interleavers the performances can be significantly improved and can be made comparable despite the variation in channel frequency selectivity.

A scalable resource allocation (ScRA) algorithm is developed to improve the mobile network radio resource utilization. The traditional mobile dimensioning is based on the "busy hour" traffic intensity, and this static resource allocation (StRA) methodology does not seem to be able to provide efficient radio resource utilization for the future/present multiservices environment, with their expected spatially and temporally varying loads. This is a hindrance for the introduction of wireless IP based services, for which the demand is rapidly increasing. This paper provides an extension analysis by incorporating single slot FIFO and single slot round robin (single slot RR), blocked-call cleared (BCC) and blocked-call delayed (BCD) strategies in the ScRA scheme. By employing the ScRA scheme in an example GSM and GPRS network, we specifically investigate and evaluate the system throughput for both the circuit-and packet-switched networks. The findings show that single slot FIFO ScRA and single slot RR ScRA schemes obtained no difference in system throughput. On the other hand, when BCD is implemented in the ScRA scheme, there is significant throughput gain.

The elasticity of transmission control protocol (TCP) traffic complicates attempts to provide performance guarantees to TCP flows. The existence of different types of networks and environments on the connections' paths only aggravates this problem. In this paper, simulation is the primary means for investigating the specific problem in the context of bandwidth on demand (BoD) geostationary satellite networks. Proposed transport-layer options and mechanisms for TCP performance enhancement, studied in the single connection case or without taking into account the media access control (MAC)-shared nature of the satellite link, are evaluated within a BoD-aware satellite simulation environment. Available capabilities at MAC layer, enabling the provision of differentiated service to TCP flows, are demonstrated and the conditions under which they perform efficiently are investigated. The BoD scheduling algorithm and the policy regarding spare capacity distribution are two MAC-layer mechanisms that appear to be complementary in this context; the former is effective at high levels of traffic load, whereas the latter drives the differentiation at low traffic load. When coupled with transport layer mechanisms they can form distinct bearer services over the satellite network that increase the differentiation robustness against the TCP bias against connections with long round-trip times. We also explore the use of analytical, fixed-point methods to predict the performance at transport level and link level. The applicability of the approach is mainly limited by the lack of analytical models accounting for prioritization mechanisms at the MAC layer and the nonuniform distribution of traffic load among satellite terminals.

Uplink and downlink load equations for a snapshot of users in a UMTS network are presented in unified and general matrix form. Capacity and coverage constraints of the system are presented in the form of inequalities to he satisfied by users and network loadings. It is shown that the static simulation of the network is the joint solution of the load equations and capacity and coverage constraints carried out with an iterative method.

A suboptimum search algorithm to suppress the interference level in conjunction with a primary stage of iterative linear multiuser detector for the mobile uplink is proposed. The initial stage is improved through analytical analysis. The structure of the iterative method and the suboptimum search algorithm are well suited together and makes them collaboratively work without encountering a high level of complexity. Considering the power profile of the users in the suboptimum search algorithm leaded to even less complexity, yet keeping the performance almost the same. The performance of the structure is obtained by simulations and has been compared to parallel interference cancellation method.

In this paper, we introduce the practical blind interference suppression receiver, for joint acquisition and demodulation of CDMA signals. Regarding to the time delay estimation, a new and practical method of handling the mismatch in multi-path fading channel is proposed. Furthermore, by efficient exploiting the channel diversity and mismatch handling, significant improvement in the BER performance of final receiver, which is the outcome of acquisition mode, is obtained.

It has been proved that the cross-correlation of shadowing has a great impact on the performance of mobile communication systems. In this paper, an approach of generating multiple cross-correlated shadowing components is proposed. This method is suitable for dynamic system-level simulations as it considers cross-correlation matrix of a user in interest as the function of its location throughout the simulation steps. It is also verified by simulations that this approach can model both auto-correlation and cross-correlation properties of shadowing components in a very accurate manner.

This paper reports a simulation study of the interaction between the transmission control protocol (TCP) and bandwidth on demand (BoD) mechanisms in broadband satellite networks. It forms part of a broader effort aiming at the identification of the proper mechanisms and algorithms to efficiently carry TCP traffic over these networks. We investigate two aspects of this rather complicated interaction, related to the possible BoD configurations and the TCP tuning parameters, focusing on GEO satellite networks. The impact on TCP performance and system efficiency is evaluated and effects that cannot be easily captured by analysis are highlighted.