With the advent of 6G networks, integrated sensing and communication (ISAC) is a key enabling technology for native perception in complex industrial Internet of Things (IoT) environments, which demands accurate ISAC channel models. However, most existing models mainly capture correlation at the cluster level, making it difficult to characterize finer-grained parameter relationships and intra-cluster variations, and lack an effective mechanism for bidirectional parameter coupling between communication and sensing channels in industrial IoT scenarios. This paper proposes a shared-cluster geometry-based stochastic ISAC (SC-GBSM-ISAC) channel model for industrial IoT. The model is parameterized using channel measurements at 38 GHz and 132 GHz in a high-clutter indoor factory (InF (A)) scenario. Compared with existing ISAC channel models, the proposed model has the following features. First, a closed-loop “sensing–communication–sensing” channel modeling framework is constructed based on 3GPP TR 38.901, which integrates bidirectional parameter interaction between communication and sensing channels. Second, by selectively reusing sensing-target clusters to construct shared clusters for communication-channel modeling, the model introduces a physically consistent shared-scattering structure while preserving the statistical characteristics specified in 3GPP TR 38.901. Third, by combining scatterer information inferred from the communication channel with a scenario-dependent sensing probability model, the sensing background component is reconstructed. Finally, measurement-simulation comparisons in an independent low-clutter InF (B) scenario demonstrate that the proposed model effectively reproduces bidirectional parameter coupling and primary spatio-temporal statistical metrics at 38/132 GHz, validating its accuracy and cross-scenario consistency in the measured scenarios.

Complex building structures fundamentally limit indoor wireless performance. While the wireless industry consistently pursues optimal channel capacity, architectural constraints remain significant factors, largely neglected by conventional workflows. To bridge the gap, this paper expands the inter-disciplinary building wireless performance (BWP) framework by introducing a layout-specific evaluation of area spectral efficiency (ASE). We propose ASE gain as a novel metric to quantify the impact of building layouts on indoor wireless channel capacity. Furthermore, we derive and validate an analytical model for the ASE gain against Monte Carlo simulations. This approach significantly reduces the computation time from hours to minutes with a relative error of less than 1%. Numerical results demonstrate that the ASE gain is significantly influenced by building layouts and wireless parameters. For example, in multi-rectangular layouts, reducing the reception threshold density of 10 dBWm−2decreases the variation range by 0.2059 at 1 GHz, whereas it increases the range by 0.1990 at 28 GHz. For non-rectangular polygonal layouts, the variation range is one order of magnitude larger than in simple rectangular rooms. This work highlights the importance of integrating the ASE gain into future building design. It complements existing metrics such as power gain and interference gain to ensure that buildings provide sufficient margin for the future deployment of high-density networks.

In this paper, we commence with the first principles of wave propagation and polarization characteristics during reflection and transmission, and then propose a ray tracing algorithm that can fully capture polarization properties for simulated paths. Simulation results reveal that conventional cross-polarized ratio (XPR) models for multipath components (MPCs), which assume a consistent average value, do not align well with reality. Additionally, the assumption of a constant XPR tends to either overstate or understate the impact of depolarization. This misrepresentation, combined with inaccurate modeling of depolarization in traditional channel models, leads to degraded performance of polarization shift keying (PolarSK) in wireless communication systems. Thirdly, the Average Bit Error Probability (ABEP) performance of different PolarSK modulation configurations is analyzed based on the data from the proposed ray tracing model under various system configurations. These analyses show that indoor building materials with strong depolarization effects lead to irreducible ABEP floors, which cannot be resolved by increasing diversity gain or adjusting constellation diagram. Finally, in order to mitigate these ABEP floors, we introduce an iterative algorithm designed to minimize the floor effect of the ABEP in PolarSK by simultaneously optimizing the complex dielectric constant and thickness of each layer in the multi-layer building material. Numerical results indicate that, for a target ABEP on the order of 10−6, the proposed algorithm achieves a substantial signal to noise ratio (SNR) gain of up to approximately 25 dB, and for different constellation diagrams and different MIMO systems, the ABEP floors are completely eliminated within the considered SNR range, without any SNR penalty. At the same time, we provide a detailed explanation of the physical causes behind the occurrence of the ABEP floor effect. Overall, we establish building wireless performance (BWP) as a fundamental and quantifiable design objective for indoor environments and, by focusing on indoor polarized wireless channels, provide practical design guidelines while revealing that their achievable performance is inherently constrained by building material characteristics and architectural structures, thereby enabling wireless friendly indoor building and channel design toward 6G.

In this paper, we propose an optimisation framework for deployment of multiple reconfigurable intelligent surfaces (RISs) to meet the wireless coverage demands for smart warehouses. Specifically, we are the first to formulate a unified network optimisation task that jointly considers RIS placement and beamforming to maximize overall network coverage with a deterministic channel model to accurately describe the multipath effect for the warehouse. To address this problem, we design a hybrid optimisation framework composed of three synergistic modules. (1) A Large Language Model (LLM) acts as a semantic planner that generates physically feasible multi-RIS configurations, jointly determining the placement and beamforming directions guided by structured prompts and environment-aware embeddings. (2) A Genetic Algorithm (GA) module performs local numerical refinements to enhance the precision of LLMgenerated solutions under physical constraints. (3) A Diversity Reflection and Correction (DiRect) module evaluates structural similarity among candidate configurations and triggers additional semantic regeneration to maintain exploration diversity. These three modules form an alternating iterative process in which LLM reasoning, GA-based evolution, and DiRect-driven regeneration collectively guide the optimisation toward high-coverage configurations. Extensive simulations validate the effectiveness and robustness of the proposed framework. Compared with traditional heuristics, reinforcement learning methods, and LLMguided baselines, our hybrid framework achieves 10%-15% higher coverage within 10-20 iterations. The performance consistently scales with the number of RISs and element sizes, and remains stable under varying transmitter positions, demonstrating strong adaptability to complex smart warehouse layouts. Overall, the proposed hybrid optimisation framework provides a scalable and physically grounded solution for RIS-assisted network deployment optimisation in realistic in.

With over 80% of mobile traffic occurring indoors, optimizing indoor wireless communication performance is crucial. For indoor communication systems, interior walls play a significant role in shaping system performance, yet their impacts on wireless communication performance at the link level remain insufficiently explored. In this paper, we propose an evaluation method for indoor wireless performance, considering wall materials and locations. Based on the evaluation method, the relationships between interior walls and signal propagation are investigated, considering line-of-sight (LOS) probability, blockage height, blockage density, and wall materials. The signal propagations are divided into four cases, including the direct path, the reflection path and/or penetration path through walls, and the multipath from scatterers. Moreover, spatial cross-correlation function (SCCF), spectrum efficiency (SE), and energy efficiency (EE) are analyzed to unveil the impacts of walls on channel characteristics and system performance. Our findings reveal that strategically adjusting the placement and materials of walls, while keeping network settings unchanged, can significantly enhance system performance.

To encourage further research and to facilitate fair comparisons in the development of deep learning-based radio propagation models, in the less explored case of directional radio signal emissions in indoor propagation environments, we have launched the ICASSP 2025 First Indoor Pathloss Radio Map Prediction Challenge. This overview paper describes the indoor path loss prediction problem, the datasets used, the Challenge tasks, and the evaluation methodology. Finally, the results of the Challenge and a summary of the submitted methods are presented.

Orthogonal Time Frequency Space (OTFS) modulation is expected to address the performance degradation of orthogonal frequency division multiplexing (OFDM) modulated signals, particularly due to issues like Doppler shifts in mobile communication environments. In this paper, we propose a lightweight deep learning-based framework for end-to-end joint channel estimation and data detection (JCEDD) in an OTFS communication system. To fully exploit the characteristics of OTFS modulation, we introduce a data padding preprocessing method and a slicing data augmentation technique. Furthermore, the performance of the proposed deep learning-based framework could be enhanced dramatically with only a small overhead compared to the superimposed pilot scheme. Ablation experiments demonstrate that the proposed data padding preprocessing method and the slicing data augmentation technique significantly improve the performance of the deep learning-based framework. Simulation results show that the proposed framework outperforms existing algorithms in terms of JCEDD performance, while maintaining a relatively low level of computational complexity.

The maximum indoor wireless network performance is determined by building design, making the evaluation of building wireless performance (BWP) essential during the design phase. In this study, we aim to propose a novel BWP evaluation framework that, for the first time, analyzes the influence of user equipment (UE) aperture antenna. The framework provides an analytical examination of how UE aperture antenna impacts BWP metrics, specifically interference gain (IG) and power gain (PG).We begin by refining the path gain model to incorporate the effects of aperture antenna, which allows us to derive analytical expressions for IG and PG. To validate the accuracy of the model, Monte Carlo simulations are first conducted. Subsequently, the study concludes by identifying the underlying causes of the discrepancy between two models, one accounting for aperture antenna effects and the other not. Taking the 1 GHz band as an example, numerical results demonstrate that the existing model underestimates IG by more than 50% due to the neglect of aperture antenna. At 28 GHz, the underestimation becomes more pronounced, with discrepancies increasing by a factor of 3 to 10. In contrast, the PG shows only minor variations between the two analytical models with peak deviations reaching up to 50%. These deviations, resulting from the omission of aperture considerations, may critically compromise key metrics of BWP. Therefore, our proposed model, which factors in aperture effects, is essential for accurately assessing BWP during the building design phase.

Unmanned-aerial-vehicle (UAV) communications assisted by aerial intelligent reflecting surfaces (A-IRSs) have emerged as a highly regarded technology aiming to increase the communication capacity of cellular networks and improve user communication quality. However, the performance of combining UAV-carried A-IRSs with heterogeneous cellular networks (HetNets) has not been fully investigated. In this article, we develop a novel framework to analyze the downlink (DL) performance of an UAV-carried cooperative A-IRSs-enhanced HetNet. First, we model the cascaded fading of the A-IRS-assisted communication channel as a mixture of gamma variables and approximate its distribution function by using the moment matching method. Next, we obtain the exact closed-form expressions of the user-association probability and the probability density function (PDF) of the distance between any user and its serving BS. Then, we derive the Laplace transform of the received signal and the interference power of all types of users for our proposed A-IRS deployment strategy. Finally, we derive analytic expressions of the DL coverage probability and the energy efficiency of UAV networks, which are validated by the simulation results. The results demonstrate that our proposed theoretical framework significantly enhances the precision of analysis, and aligns closely with real-world scenarios. We also show the performance enhancement brought by cooperative A-IRSs, the advantages of our proposed A-IRS deployment strategy within HetNet, as well as the impact of the interference introduced by A-IRSs on direct transmissions.

This paper proposes a novel meta-learning based hyper-parameter optimization framework for wireless network traffic prediction (NTP) models. The primary objective is to accumulate and leverage the acquired hyper-parameter optimization experience, enabling the rapid determination of optimal hyper-parameters for new tasks. In this paper, an attention-based deep neural network (ADNN) is employed as the base-learner to address specific NTP tasks. The meta-learner is an innovative framework that integrates meta-learning with the k-nearest neighbor algorithm (KNN), genetic algorithm (GA), and gated residual network (GRN). Specifically, KNN is utilized to identify a set of candidate hyper-parameter selection strategies for a new task, which then serves as the initial population for GA, while a GRN-based chromosome screening module accelerates the validation of offspring chromosomes, ultimately determining the optimal hyper-parameters. Experimental results demonstrate that, compared to traditional methods such as Bayesian optimization (BO), GA, and particle swarm optimization (PSO), the proposed framework determines optimal hyper-parameters more rapidly, significantly reduces optimization time, and enhances the performance of the base-learner. It achieves an optimal balance between optimization efficiency and prediction accuracy.

The emerging drones enabled air-to-ground (A2G) communications in millimeter-wave (mm-wave) bands are facing the Doppler effect that arises from the inevitable wobbling of the drones. The fast time-varying channel for drones A2G communications can make the channel state information (CSI) from channel estimation outdated, i.e., imperfect CSI. In this article, we introduce two detectors to demodulate the received signal and get the instantaneous bit error probability (BEP) of a mm-wave drones A2G link under imperfect CSI. Based on the designed detectors, we propose an adaptive modulation scheme to maximize the average transmission rate under imperfect CSI by optimizing the signal transmission time subject to the maximum tolerable BEP. A power control policy is proposed in conjunction with the adaptive modulation to minimize the transmission power while maintaining both the BEP under the threshold and the maximized average transmission rate. Numerical results show that the proposed adaptive modulation scheme in conjunction with the power control policy can maximize the temporally averaged transmission rate while reducing the power consumption by up to 50% for wobbling mm-wave drones A2G links.

In the coexisting new radio-based access to unlicensed spectrum (NR-U) and WiGig networks (CNWNs), directional-sensing-based listen-before-talk (LBT) mechanisms, i.e., directional LBT (dirLBT) and paired LBT (pairLBT), have been proposed to address the exposed node problem caused by traditional omnidirectional LBT (omniLBT) mechanism. In this paper, we are the first to leverage the stochastic geometry to analyze the large-scale CNWN performance when NR-U base stations (NBSs) adopt the directional-sensing-based LBT mechanisms. The analytical expressions for the downlink successful transmission probabilities (STPs) of CNWNs are derived and validated by Monte Carlo simulations. Based on these STPs, the area spectral efficiency (ASE) of CNWNs is derived. Equipped with these results, the effect of NBS sensing threshold, density and sensing beamwidth on the STP and ASE performance are analyzed numerically. Moreover, the STP and ASE performance are compared when NBSs adopt dirLBT, pairLBT and omniLBT mechanisms. Furthermore, the asymptotic ASE of CNWNs when NBS density approaches infinity is derived and validated. The results show that directional-sensing-based LBT mechanisms outperform the omniLBT mechanism in terms of ASE in the CNWNs, especially in ultra-densely deployed scenarios. Under our simulation environment, the dirLBT mechanism can improve the ASE by up to 82.5% as compared with the omniLBT mechanism. Additionally, the NBS sensing threshold for directional-sensing-based LBT should be higher than −73 dBm to achieve a better STP and ASE as compared with that without adopting LBT in NBSs. Besides, there exists an optimal NBS density to maximize the STP and ASE of CNWNs, and when NBS density becomes larger than$200,000$NBSs per$\text {km}^{2}$, deploying more NBS has limited enhancement on the ASE. These results indicate that directional-sensing-based LBT mechanisms should be employed in the ultra-densely deployed CNWNs, and the NBS sensing threshold and sensing beamwidth should be carefully chosen to ensure the superiority of directional-sensing-based LBT mechanisms.

With the proliferation of vehicular user equipment (V-UE) in the Internet-of-Vehicles (IoV) systems, cloud computing alone cannot process all V-UE tasks, especially those latency-sensitive ones. Although static roadside fog nodes have been employed to offload computation from V-UEs, mobile fog nodes carried by vehicles that have the potential to further improve the performance of computation offloading for vehicular tasks have not been sufficiently studied for IoV systems. In this paper, we consider a mixed cloud/vehicular-fog computing (VFC) system that employs vehicle-carried fog nodes (V-FNs) in addition to cloud servers to offload tasks from V-UEs. To minimise the maximum service delay (which includes the transmission delay, queueing delay, and processing delay) among all V-UEs, we jointly optimise the offloading decisions of all V-UEs, the computation resource allocation at all V-FNs, the allocation of resource block (RB) and transmission power for all V-UEs while considering the mobility of V-UEs and V-FNs. The joint optimisation is solved by devising a fireworks algorithm-based offloading decision optimisation scheme, in conjunction with a bisection method-based V-FN computation resource allocation scheme and a clustering-based communication resource allocation scheme. Simulation results show that our proposed schemes outperform the benchmarks in terms of service the maximum delay among all V-UEs.

Metadata geolocation, i.e., mapping information collected at a cellular Base Station (BS) to the geographical area it covers, is a central operation in the production of statistics from mobile network measurements. This task requires modeling the probability that a device attached to a BS is at a specific location, and is presently addressed with simplistic approximations based on Voronoi tessellations. As we show, Voronoi cells exhibit poor accuracy compared to real-world geolocation data, which can, in turn, reduce the reliability of research results. We propose a new approach for data-driven metadata geolocation based on a teacher-student paradigm that combines probabilistic inference and deep learning. Our Deepmend model: ($i$) only needs BS positions as input, exactly like Voronoi tessellations; (ii) produces geolocation maps that are 56% and 33% more accurate than legacy Voronoi and their state-of-the-art VoronoiBoost calibration, respectively; and, (iii) generates geolocation data for thousands of BSs in minutes. We assess its accuracy against real-world multi-city geolocation data of 5, 947 BSs provided by a network operator, and demonstrate the impact of its enhanced metadata geolocation on two applications use cases.

Integrated Sensing and Communication (ISAC) has been considered a promising technology in the sixth generation (6G) system. An accurate channel model is a prerequisite for ISAC system design. This paper presents the first sensing and communication channel measurement campaign in typical indoor factory (inF) environments at 28 GHz and 38 GHz, in which over 720 spatial channel impulse responses are collected. The power-delay-angular profiles of multipath components are obtained, and the shared scatterers in communication and sensing channels are intuitively observed. A cluster centroid distance threshold-based cluster identification algorithm is novelly proposed to extract shared clusters from measured ISAC channels. Finally, the sharing proportional coefficient (SPC) is defined to measure the sharing feature of the ISAC channel. Results show that the channel SPC has an upward trend as the frequency increases. The observations presented in this work will offer promising support for ISAC wireless system evaluation.

Terahertz (THz) communication technology holds significant potential for applications in the industrial internet of things (IIoT). Accurately characterizing the THz channel is critical for designing and optimizing communication systems in IIoT scenarios. However, the significantly higher frequencies in the THz band impede the effective utilization of channel models designed for microwave or millimeter-wave frequency bands. To overcome this challenge, extensive measurement campaigns are necessary to thoroughly investigate the characteristics of THz channels in indoor IIoT scenarios. This paper presents a measurement-based channel characterization in indoor IIoT scenarios at a frequency range of 215–225 GHz. We first present VNA-based channel measurement campaigns in micro drilling-milling and large milling areas. The measured data are further processed to obtain the channel impulse response. The key prop-agation channel parameters, e.g., path loss, power delay angle profile, delay spread, and angular spread, are calculated and analyzed in the line-of-sight case. Results demonstrate a favorable spatio-temporal consistency in multipath signal propagation and the physical spatial environment. Furthermore, significant correlations are observed between the channel characteristics and scatterer distribution within IIoT scenarios. The findings of this paper will make substantial contributions to the design and development of THz communication systems in IIoT scenarios.

Human activity recognition and classification have been widely used in various fields. To make full use of wireless body area networks (WBANs), multiple human activities classification based on dynamic on-body propagation characteristics is presented in this article. Four on-body links are established to collect propagation data set for four groups of activities, which include hand activities, arm activities, leg activities and head activities. A total of 30 human activities are considered. The line-of-sight (LOS) and non-LOS (NLOS) on-body propagation characteristics are analyzed in detail by comparing measurement with full-wave simulation. Multiple human activities, including some fine-grained motions, can be accurately classified using the propagation data collected by the on-body antennas. Based on the similar propagation mechanism among different links, an interlink transfer learning framework is proposed by pretraining the deep convolutional neural network (DCNN) on one link before training it on other links. The results show that after pretrained in source domain, the interlink transfer learning can improve classification accuracy and accelerate model convergence in target domain with a small amount of training data, which alleviates the complex and time-consuming data collection. The results of this article are particularly useful for the deployment of WBAN integrated communication and sensing.

Heterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offering broader coverage, higher capacity, and lower latency, the escalating energy consumption poses sustainability challenges. In this paper a novel optimization approach for orthogonal heterogeneous networks is proposed to minimize transmission power while respecting individual users’ throughput constraints. The problem is formulated as a mixed integer geometric program, and optimizes at once multiple system variables such as user association, working bandwidth, and base stations transmission powers. Crucially, the proposed approach becomes a convex optimization problem when user-base station associations are provided. Evaluations in multiple realistic scenarios from the production mobile network of a major European operator and based on precise channel gains and throughput requirements from measured data validate the effectiveness of the proposed approach. Overall, our original solution paves the road for greener connectivity by reducing the energy footprint of heterogeneous mobile networks, hence fostering more sustainable communication systems.

In cell-free multiple input multiple output (MIMO) networks, multiple base stations (BSs) collaborate to achieve high spectral efficiency. Nevertheless, high penetration loss due to large blockages in harsh propagation environments is often an issue that severely degrades communication performance. Considering that intelligent reflecting surface (IRS) is capable of constructing digitally controllable reflection links in a low-cost manner, we investigate an IRS-enhanced downlink cell-free MIMO network in this paper. We aim to maximize the weighted sum rate (WSR) of all the users by jointly optimizing the transmit beamforming at the BSs and the reflection coefficients at the IRS. To address the optimization problem, we propose a fully distributed machine learning algorithm. Different from the conventional iterative optimization algorithms that require a central processing at the central processing unit (CPU) and large amount of channel state information and signaling exchange between the BSs and the CPU, in the proposed algorithm, each BS can locally design its beamforming vectors. Meanwhile, the IRS reflection coefficients are determined by one of the BSs. Simulation results show that the deployment of IRS can significantly boost the WSR and that the proposed algorithm can achieve a high WSR with a low computational complexity.

This paper proposes to leverage intelligent reflecting surface (IRS) to realize radio-frequency-chain-free uplink-transmissions (RFCF-UT) for an integrated sensing, communication and computation system. In this communication paradigm, IRS works as an information carrier, whose elements are capable of adjusting their amplitudes and phases to collaboratively portray an electromagnetic image like a dynamic quick response (QR) code, rather than a familiar reflection device. Meanwhile, a dual-functional radar-communication base station (BS) is used as a scanner to collect and recognize the information on each IRS carrying the data of its associated user equipment, while detecting the radar target. Based on the established system model, partial and binary data offloading strategies are respectively considered. By defining a performance metric named weighted throughput capacity (WTC), two maximization problems of WTC are formulated. According to the coupling degree of optimization variables in the objective function and the constraints, each optimization problem is firstly decomposed into two subproblems. Then, the methods of linear programming, fractional programming, integer programming and alternative optimization are developed to solve the subproblems. The simulation results demonstrate the achievable WTC of the considered system, thereby validating RFCF-UT.

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to implement downlink communications for a multi-cell multiple-input multiple-output (MIMO) network, which represents a brand-new communication framework. In such a network, one active macro cell base station (MBS) is deployed for radiating energy signal, while each IRS acts as a small cell base station to realize its own information transmission by modulating and reflecting the signal from the MBS. Under this paradigm, we investigate two optimization problems, namely weighted sum rate maximization problem and max-min fairness problem, aiming at satisfying different communication requirements. To seek their optimal solutions, Lagrangian dual transform and alternate methods are employed to optimize the active beamforming vector at the MBS and the passive beamforming vectors at all the IRSs. Additionally, an element clustering scheme is developed to reduce computation and control complexity, in which each element cluster works like a unit and all clusters can collaborate to communicate with users. Extensive simulations are conducted to evaluate the achievable communication performance and to verify the feasibility of the proposed IRS backscatter enabled downlink multi-cell MIMO network.

This paper presents the large-scale fading characteristics of Terahertz (THz) channel in indoor hotspot scenarios. A series of channel measurements at 219–224 GHz are conducted in a classroom and a hallway. In order to investigate the large-scale fading characteristics, the omnidirectional and best directional path loss are separately analyzed by close-in and floating-intercept models, and the Rician$K$-factor, root mean square delay spread and angular spread are analyzed to estimate the multi-path component richness, time and angle dispersion in various indoor scenarios. Further, these values are compared with what has been given in the Third Generation Partnership Project 38.901 in frequency bands lower than 100 GHz. In light of the results, the office area in hallway scenario shows the most severe path loss, and derives the highest time and angle dispersion. Besides, the measurement results enrich the datasets of THz channel propagation, which is helpful for the design and optimization of THz communication systems for sixth-generation.

This article proposes a novel methodology for interpolating path-loss radio maps, which are vital for comprehending signal distribution and hence planning indoor wireless networks. The approach employs generative diffusion models and proves to be highly effective in generating accurate radio maps with only a small number of measurements. The experimental outcomes demonstrate an average root-mean-square error of 4.23 dB using only 10 percent of the reference points, highlighting the ability of the generative diffusion model to achieve significant interpolation accuracy in radio map generation.

Deep learning (DL) has been recently leveraged for the inference of characteristics related to wireless communication channels, such as path loss (PL). This paper presents how a deep convolutional encoder-decoder, namely a path loss prediction net (PPNet) based on SegNet, can be trained to transform information related to an outdoor propagation environment into a PL heatmap. This work is a part of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing First Pathloss Radio Map Prediction Challenge. The DL model is trained with synthetic data generated with a high-performance ray tracing simulator and it is illustrated that PPNet can indeed learn to predict the PL distribution and that it generalizes well to previously unseen outdoor propagation environments.

Indoor cellular networks (ICNs) are anticipated to become a principal component of 5G and beyond systems. ICNs aim at extending network coverage and enhancing users' quality of service and experience, consequently producing a substantial volume of traffic in the coming years. Despite the increasing importance that ICNs will have in cellular deployments, there is nowadays little understanding of the type of traffic demands that they serve. Our work contributes to closing that gap, by providing a first characterization of the usage of mobile services across more than 4, 500 cellular antennas deployed at over 1,000 indoor locations in a whole country. Our analysis reveals that ICNs inherently manifest a limited set of mobile application utilization profiles, which are not present in conventional outdoor macro base stations (BSs). We interpret the indoor traffic profiles via explainable machine learning techniques, and show how they are correlated to the indoor environment. Our findings show how indoor cellular demands are strongly dependent on the nature of the deployment location, which allows anticipating the type of demands that indoor 5G networks will have to serve and paves the way for their efficient planning and dimensioning.

The 5G New Radio-based in unlicensed spectrum (NR-U) has been proposed to harmoniously coexist with the Wireless Gigabyte (WiGig) network in the 60 GHz unlicensed spectrum. It employs the directional listen-before-talk (dirLBT) mechanism to improve the throughput of the coexisting NR-U and WiGig networks (CNWNs). In this paper, we are the first to leverage the stochastic geometry to analyze the performance of the large-scale CNWNs. The medium access probabilities (MAPs) of NR-U base station (NBS) and WiGig access point (WAP) are both derived in closed-form. Based on these MAPs, the downlink successful transmission probabilities (STPs) of NR-U and WiGig networks, which is determined by the retaining probability of the serving NBS/WAP and the downlink coverage probability of NR-U/WiGig network, are given in analytical expressions. All these MAPs and STPs are validated by Monte Carlo simulations to verify the correctness of our proposed model. Moreover, the effect of NBS and WAP density on the mean STP of the large-scale CNWNs are analyzed numerically. The results show that the dirLBT adopted by NR-U outperforms omnidirectional LBT in terms of the mean STP, especially in ultra-densely deployed CNWNs scenario. Furthermore, there exists an optimal NBS density to maximize the mean STP. The results indicate that the dirLBT mechanism should be adopted in the densely deployed CNWNs with proper chosen of NBS density.

Edge computing network (ECN), which could process learning tasks at the edge, is considered as a potential solution to release the burden of the cloud. Meanwhile, to protect user privacy, federated learning (FL) is used in the ECN to establish models by multi-party collaborative learning on numbers of edge nodes (ENs). However, due to the frequent data interaction between the cloud server and distributed ENs, the reliability of data transmission and the privacy protection capability of the network cannot be guaranteed. In this paper, a distributed ECN is considered, to improve the learning efficiency in the multi-party FL while ensuring the reliability of ENs, a consortium blockchain enabled asynchronous federated learning (AFLChain) algorithm is proposed, which could dynamically allocate the learning tasks to ENs according to their computing capabilities. Moreover, an entropy weight-based reputation mechanism is introduced for the EN evaluation to further improve the performance of the AFLChain. Finally, the simulation results demonstrate the effectiveness of the proposed algorithms.

While 5G networks have accelerated the development of the Internet of Things (IoT), they have also introduced a large number of vulnerable IoT devices into the network, which would lead to severe Distributed Denial-of-Service (DDoS) attacks. The newly emerging DDoS attack methods generally have a shorter duration, which imposes higher requirements for the response time of DDoS mitigation technologies. Existing DDoS defense methods cannot achieve real-time detection due to the difficulty of reducing the delay of feature extraction and large-scale data processing. In this article, we focus on the timeliness of DDoS detection and mitigation. We hope that deploying effective defense countermeasures at the source side will block the majority of DDoS attack traffic in real time before it enters the data network (DN). To this end, we propose a real-time DDoS defense framework based on multidomain collaboration that combines multisource information to detect attack sessions with high accuracy in 5G networks. To operate the framework at line rate, we propose an optimal packet sampling strategy based on the accurate session size estimation, which can greatly reduce the detection overhead while ensuring good accuracy. In a typical scenario with an attack session size larger than 10, this method can achieve a 99% detection rate while reducing the packet inspection rate (PIR) to less than 37%.

The Age of Information (AoI), which evaluates the information freshness, and the energy efficiency (EE) play key roles in cellular IoT networks. This is due to that outdated data can hardly provide any useful information for delay-sensitive applications and the IoT devices usually have limited battery life. In particular, the AoI can be significantly affected by the transmission latency, which becomes the bottleneck for the AoI performance in ultradensely deployed cellular IoT networks. Moreover, it is desirable for cellular IoT networks to achieve low AoI with high EE. The data compression (DC) can decrease the AoI and improve the EE by reducing the transmission latency. Therefore, in this work, the AoI and EE performance in a large-scale densely deployed uplink cellular IoT network are jointly analyzed with the DC technology. Specifically, the closed-form results of AoI are derived and validated by Monte Carlo simulations. Based on these results, the AoI–EE ratio is defined to evaluate the tradeoff between the AoI and the EE. Equipped with these results, the effects of compression ratio (CR) and status update packet generation rate (SUPGR) on both the AoI and the AoI–EE ratio are analyzed numerically. The results show that by jointly optimizing the CR and SUPGR, the AoI can be decreased by up to 82% and the AoI–EE ratio can be reduced by up to 83% as compared with the case that only adjusts the SUPGR without the DC. It indicates that the DC should be widely adopted in IoT devices, which can improve the information freshness with low-energy consumption, especially in an ultradensely deployed scenario.

Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-preserving distributed machine learning in the Internet of Things (IoT). However, the communication overhead caused by partial model aggregations will increase the model training latency. In this article, a multilayer blockchain-enabled hierarchical FL (HFL) network is proposed for low-latency model training while ensuring data security. Meanwhile, we theoretically analyze the bottleneck of the model accuracy with the total data distance due to the imbalanced data distribution. Moreover, the mathematical expression of the model error with respect to IoT devices (IDs) association and local data distribution is provided, then the upper bound of the model error is represented by the total data distance. To further improve the learning performance, the distance-aware HFL (DAHFL) algorithm is investigated, which optimizes ID association strategy based on dual-distance, and allocates computing and communication resources alternatively. Finally, the working process of the blockchain-enabled HFL system is exhibited by the blockchain simulation platform and the efficiency of the proposed DAHFL algorithm is demonstrated by the simulation results.

In this article, a time-efficient mechanism is proposed to solve the joint unmanned aerial vehicle (UAV) base station deployment and user/sensor association (UDUA) problem aiming at maximizing the downlink sum transmission throughput and reducing the time of online computations. In this work, two relevant subproblems are decoupled from the joint UDUA problem: the first one is denoted as the user association subproblem, for certain UAV base station (UAV-BS) positions, this subproblem is settled for finding the strategy which matches aerial and ground nodes optimally. The second subproblem is the positioning of UAV-BSs in order to obtain the best possible solution to the user association subproblem from all possible positioning combinations for the UAV-BSs. In the proposed mechanism, the Kuhn–Munkres algorithm is used to solve the first subproblem as an equivalent bipartite matching problem. For the UAV-BS deployment subproblem, when the two user distributions own a high similarity, we theoretically prove that little performance decline will be introduced when the new user distribution’s optimal strategy is compared with choosing the optimal UAV-BS deployment strategy of stored user distributions. Based on our mathematical analyses, the similarity level between user distributions is well defined and becomes the key to solve the second subproblem. According to experimental findings, the proposed UDUA mechanism, when compared to benchmark approaches, can provide near-optimum system performance with regard to average downlink total transmission throughput and failure rate with significantly decreased computing time.

The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in the user equipment (UE) to the edge to reduce the processing latency. However, it incorporates the task transmission latency, which may become the bottleneck of latency requirements. Data compression (DC) has been considered as one of the promising techniques to reduce the transmission latency. By compressing the computation tasks before transmitting, the transmission delay is reduced due to the shrink transmitted data size, and the original computing task can be retrieved by employing data decompressing (DD) at the edge nodes or the centre cloud. Nevertheless, the DC and DD incorporate extra processing latency. For the F-RAN system, the latency performance has not been investigated considering the DC and DD processes. Therefore, in this work, the successful data compression probability (SDCP), i.e., the probability of the task execution latency being smaller than a target latency and the signal to interference ratio (SIR) of the received signal being higher than a threshold, is defined to analyse the latency performance of the DC-enabled F-RAN. Moreover, to analyse the impact of compression offloading ratio (COR), which determines the proportion of tasks being compressed at the edge, on the SDCP of the F-RAN, a novel hybrid compression mode is proposed based on the queueing theory. Based on this, the closed-form result of SDCP in the large-scale DC-enabled F-RAN is derived by combining the Matern cluster process and M/G/1 queueing model, and validated by the Monte-Carlo simulation. Based on the derived SDCP results, the effects of COR on the SDCP is analysed numerically. The results show that the SDCP with the optimal COR can be enhanced with a maximum value of 0.3 and 0.55 as compared with the cases of compressing all computing tasks at the edge and at the UE, respectively. Moreover, for the system requiring the minimal latency, the proposed hybrid compression mode can alleviate the requirement on the backhaul capacity.

Reconfigurable intelligent surface (RIS), which provides indirect line-of-sight (LoS) transmission paths between the receiver and its blocked transmitter, has been proposed as one of the promising technologies for network performance enhancement. Recent works indicate that both the numbers of RISs and unit cells per RIS have a significant impact on network performance. However, with a given total number of unit cells, the joint analysis of these two factors considering blockage effect has not been investigated. In this paper, the coverage probability of a three-dimensional downlink millimeter wave RIS-assisted network is analyzed. We first derive the acceptable area where a LoS RIS is capable to satisfy the signal-to-noise ratio threshold at the receiver. Next, we model the centers of building blockages and human-body blockages as two independent Poisson point processes and derive the probability that indirect LoS transmissions exist. Then, we derive and validate the analytical upper and lower bounds of the coverage probability as the functions of network parameters and blockage densities. We also derive and validate the closed-form coverage probability when RISs are much closer to UE than BS. Finally, we propose a general network cost model for RIS-assisted network. Results show that in terms of coverage enhancement, densely deployed small-scale RISs outperform sparsely deployed large-scale RISs in scenarios with dense blockages or short transmission distances, while sparsely deployed large-scale RISs are preferable in scenarios with sparse blockages or long transmission distances.

In this paper, we propose a meta-learning based cell-level network traffic prediction framework (ML-TP), which can provide the proper initial weight vector for the learning model of a new prediction task based on the prediction task’s meta-features. In the ML-TP, each prediction task forms a base-task, and a multi-layer long short-term memory (LSTM) network is constructed as its base-learner. Through fast Fourier transform (FFT) analyses of real-world network traffic data, we find that the five most dominating frequency components can capture the cell-level traffic variations, and hence can be used as a base-task’s meta-features. We prove that the well-trained weight vector of a previous base-task’s base-learner is likely to be a proper initial weight vector of a new base-task’s base-learner if the meta-features of the two base-tasks are close to each other in the Euclidean space. Accordingly, we propose a K-nearest neighbours (KNN) algorithm based meta-learner to deal with the meta-task in the ML-TP. Numerical tests show that the ML-TP can significantly increase the base-learners’ after-training prediction accuracy and learning efficiency in terms of the number of base-samples and the number of epochs needed in each base-learner’s fine-tuning progress.

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

This paper proposes an intelligent reflecting surface (IRS) backscatter based uplink coordinated transmission strategy for a radio cellular network, where IRS serves as a transmitter enabling uplink transmission from each user to the associated base station (BS). To be specific, the considered network is made up of multiple cells, each of which consists of one multi-antenna BS and its served users. While one multi-antenna power beacon (PB) is deployed to radiate energy-bearing electromagnetic wave, the radio signal received by each IRS is modulated to send its connective user’s information to the associated BS. Based on such a network framework, this paper aims to maximize the weighted sum rate (WSR) under the constraints of total transmit power and reflecting coefficient by joint optimization of active beamforming at the PB, passive beamforming at the IRSs and uplink user scheduling. To address this challenging problem, fractional programming (FP), alternative optimization and weighted bipartite matching are employed to convert the logarithm objective function into a more tractable form and to handle the optimization variables. The simulation results demonstrate the achievable WSR of the considered network.

We consider a heterogeneous network (HetNet), where multiple access points (APs) of potentially different transmission capacities serve users simultaneously via beamforming in the same spectrum band. We propose a beamforming framework that exploits multi-agent deep reinforcement learning (DRL) for the HetNet to maximize the system downlink sum-rate. In our framework, each AP acts as an agent, which is equipped with an online policy deep neural network (DNN) and an online Q-function DNN. The former generates an AP’s beamforming vector based only on local observations in a time slot, while the latter evaluates the appropriateness of this beamforming vector. We present a distributed-updating-centralized-rewarding scheme to train the policy DNNs and Q-function DNNs of all the APs in an online trial-and-error way. Under this scheme, all the APs take the system downlink sum-rate in a recent time slot (informed by a central controller) as their identical one-step reward. Trained by the experience items with centralized rewards in every time slot, the weight vectors of each AP’s local DNNs will be updated in the direction to the global optimum. Simulation results demonstrate that the proposed framework converges fast and outperforms the benchmark beamforming methods in terms of the system downlink sum-rate performance.

Cell densification through the installation of smallcells and femtocells in indoor environments is an emerging solution to enhance the operation of wireless networks. The deployment of new components within the heart of the radio access network calls for expedient tools that assist and ensure their optimal placement within the existing network infrastructure. In this paper, we introduce metrics that can characterize indoor wireless network performance (IWNP) in terms of coverage and capacity, and we evaluate them via physics-based propagation models. In particular, we exploit a deterministic propagation model, i.e., a ray-tracer, as well as a novel machine learning-based propagation model. We demonstrate that data-driven propagation models can be leveraged for the rigorous evaluation of the IWNP metrics, yielding a remarkable computational efficiency compared to the conventional deterministic models. The use of physics-based site-specific propagation models allows for the particularities of each indoor geometry to be taken into account, and also makes feasible the consideration of uncertainties related to the indoor environment. In this case, the IWNP metrics are expressed as stochastic quantities and a stochastic solution is derived through an efficient polynomial chaos expansion representation, enabling on-the-fly computation of the IWNP metrics statistics.

The power consumption is a critical bottleneck of the unmanned aerial vehicle (UAV) air-to-ground (A2G) link due to the limited on-board battery. Meanwhile, the fast time-varying channel for millimeter-wave (mm-wave) A2G links may lead to the aged channel state information (CSI) under inevitable UAV wobbling. In this paper, we propose two optimization problems to evaluate the energy efficiency performance of the mm-wave UAV A2G link under wobbling. Then, we design two algorithms to maximize the energy efficiency of a mm-wave UAV A2G link by optimizing the transmission time and the UAV hover height subject to bit error probability while considering aged CSI. The numerical results present the spectral-energy efficiency trade-off and the optimum hover height of the mm-wave UAV A2G link under aged CSI.

Recently, the topic of indoor outdoor detection (IOD) has seen its popularity increase, as IOD models can be leveraged to augment the performance of numerous Internet of Things and other applications. IOD aims at distinguishing in an efficient manner whether a user resides in an indoor or an outdoor environment, by inspecting the cellular phone sensor recordings. Legacy IOD models attempt to determine a user’s environment by comparing the sensor measurements to some threshold values. However, as we also observe in our experiments, such models exhibit limited scalability, and their accuracy can be poor. Machine learning (ML)-based IOD models aim at removing this limitation, by utilizing a large volume of measurements to train ML algorithms to classify a user’s environment. Yet, in most of the existing research, the temporal dimension of the problem is disregarded. In this article, we propose treating IOD as a multivariate time-series classification (TSC) problem, and we explore the performance of various deep learning (DL) models. We demonstrate that a multivariate TSC approach can be used to monitor a user’s environment, and predict changes in its state, with greater accuracy compared to conventional approaches that ignore the feature variation over time. Additionally, we introduce a new DL model for multivariate TSC, exploiting the concept of self-attention and atrous spatial pyramid pooling. The proposed DL multivariate TSC framework exploits only low power consumption sensors to infer a user’s environment, and it outperforms state-of-the-art models, yielding a higher accuracy combined with a smaller computational cost.

Radio sensing has become increasingly important, as the demand for “smartness” is drastically increasing. Unlike conventional sensing, radio sensing uses existing radio signals or devices to passively sense the ambient environment for low cost and wide deployment. In this article, a comprehensive overview of radio sensing using the recent fifth-generation (5G) signals is provided. 5G systems have many merits, such as high frequency, large bandwidth, massive antenna array, and dense network, making them ideal for radio sensing. In the overview, basic theories and concepts of 5G radio sensing are first introduced. Then, different state-of-the-art 5G sensing works are discussed based on their applications. These applications show that 5G radio sensing represents a step change in radio sensing. After that, several open challenges in 5G radio sensing are illustrated with relevant insights. These insights manifest that 5G radio sensing has great potentials to explore.

Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to Internet of Things devices (IDs) in wireless systems with limited or no infrastructure. In this article, a UAVs-assisted fog-enabled Internet of Things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs. Besides, there are two types of IDs, namely, requested-IDs (R-IDs), which has task offloading requirement, and free-IDs (F-IDs), which could offload tasks for R-IDs with idle computation resources. Two offloading links are considered: 1) the device-to-device (D2D) link and 2) the ground-to-air (G2A) link, which are responsible for both the uplink and downlink offloading procedure. To minimize the total network overhead, we jointly optimize the UAV trajectory, transmission power, and computation offload radios, while satisfying Quality-of-Service (QoS) requirements of R-IDs. The optimization problem is nonconvex, and the UAV-assisted task offloading optimization algorithm is proposed to obtain the local optimal solutions, which decomposes the original problem into two parallel subproblems and solved alternately. Finally, simulation results demonstrate that the proposed algorithm could achieve superior performance in terms of the network overhead compared with algorithms in the literature.

The authors regret the errors in[1, eqs. (13), (27), (34), and (35)]. The corrections for these equations are given as follows:

Modelling the mobility of people in a city depends on counting data with inherent problems of overdispersion. Such dispersion issues are caused by massive amounts of data with zero values. Though traditional machine learning models have been used to overcome said problems, they lack the ability to appropriately model the spatio-temporal correlations in data. To improve the modelling of such spatio-temporal correlations, in this work we propose to model the citizens mobility, for the Chinese city of Guangzhou, by means of a Zero-inflated Poisson likelihood in conjunction with Gaussian process priors generated from convolution processes. We follow the idea of chaining the likelihood’s parameters to latent functions drawn from Gaussian process priors; this way allowing a higher flexibility to model heteroscedasticity. Additionally, we derive a stochastic variational inference framework that allow us to use two types of convolution process models in the context of large datasets: correlated chained Gaussian processes with a convolution processes model, and correlated chained Gaussian processes with variational inducing kernels. We present quantitative and qualitative results comparing the performance between Negative Binomial and Zero-inflated Poisson likelihoods, both in combination with three types of Gaussian process priors: a linear model of coregionalisation, and our two proposed methods based on a convolution processes model and variational inducing kernels.

With the dense deployment of small cells, the impact of height difference between base stations (BSs) and user equipment (UE) on the performance of heterogeneous networks (HetNets) becomes significant. The traditional two-dimensional models are no longer sufficient to capture the three-dimensional (3D) features of dense HetNets. On the other hand, deploying multiple antennas on BSs is a promising approach to improve network capacity. In this paper, we propose a 3D model for a$K$-tier HetNet with multi-antenna BSs, where different tiers share the same frequency band but may differ in BS height, BS density, number of antennas per BS, BS transmit power, association bias, and path loss exponent. We analytically derive the per-tier association probability under both the strongest received signal and the closest BS cell-association strategies. Based on that, we derive the expressions for the downlink ergodic rate, area spectral efficiency (ASE) and energy efficiency. The numerical results reveal that in the presence of macrocell BSs, for low to medium small-cell BS (SBS) densities, the closest BS cell-association strategy leads to low ergodic rate, ASE and energy efficiency regardless of the SBS height; while at very high SBS densities, under both cell-association strategies, SBSs should be deployed at the same height as UE to achieve high ergodic rate, ASE and energy efficiency. Moreover, we find that for a given SBS height, there exists an optimal combination of SBS density and number of antennas per SBS that maximizes the system energy efficiency.

Three-dimensional (3-D) massive multiple-input-and-multiple-output (MIMO) systems, which explore degrees of freedom in both the vertical and the horizontal dimensions, are a promising technology to enhance spectral efficiency in the next-generation communication systems. As an emerging modulation technology with millimetre wave (mm-wave) communication in massive MIMO systems with limited radio-frequency (RF) chains, the spatial scattering modulation (SSM) makes use of beamspace domain resources to further improve the spectral efficiency. However, most published works on the SSM only focus on two-dimensional (2-D) MIMO systems. In this paper, we generalize the SSM system to 3-D space. First, we design a novel generalized 3-D SSM system by considering both vertical and horizontal angles to determine scattering paths, which are selected to convey information bits. Then, we propose a whitening filter based optimal detection algorithm to detect the received symbols with correlated noise, where the correlation is generated by the combination of the received signals from the large-scale planar receiving antenna array. Next, we design a 2-D fast Fourier transform (FFT) based transceiving approach to improve the hardware friendliness. After that, we propose a low-complexity detector based on the linear minimum mean square error (MMSE) detection algorithm. Moreover, we derive the union upper bound on average bit error probability (ABEP) in a closed form and the asymptotic performance expression for the generalized 3-D SSM system. Finally, we analyse the impact of transmission environment on the ABEP performance. Numerical results show that the generalized 3-D SSM system outperforms the conventional 2-D SSM system, which reduces the ABEP by$10\times $with the same signal-to-noise ratio (SNR) level under the typical indoor environment.

Recently, deep learning-based algorithms have brought impressive results in deblurring tasks. However, as an image prior proved important in image restoration tasks, self-similarity was not exploited in motion deblurring. To tackle this problem, we propose an Attention Self-Similarity Capture (ASSC) module, which takes full advantage of self-similarity by capturing long-range feature dependencies. Besides, to achieve a trade-off between performance and efficiency, we design an Enhanced Spatial Attention (ESA) module, which can dynamically adapt to the spatially-varying motion blur. We employ patch-hierarchical architecture composed of the two modules mentioned above with parameter-free feature flow between different levels. Moreover, we build two large-scale datasets, GOPRO-Supplement and SONY-Extension, to expand the public GOPRO dataset’s scene and resolution. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on both the public GOPRO dataset and our datasets.

Uplink transmission delay has been considered as a main component of the end-to-end delay in the fog radio access networks (F-RAN). However, existing analysis of the transmission delay ignore the packet retransmission delay, i.e., mean local delay (MLD), which is the main component of transmission delay. In addition, the MLD has not been investigated in a clustered F-RAN. Therefore, in this work, we leverage the Matern cluster process (MCP) to analyse MLD in a large-scale F-RAN. To derive the MLD, we obtain the uplink coverage probability (CP) firstly. The MLD can be derived by the moment result of the CP, which is defined as the probability of received signal-to-interference-ratio (SIR) is larger than a threshold. To obtain the moment result of the CP, the analytical result of the CP and its approximation with a lower computational complexity are derived. However, the moment result of CP is difficult to be validated via simulations. Therefore, we derive the meta distribution, which is calculated directly from the moment result of the CP, and validate its correctness by Monte Carlo simulations. Equipped with this, the analytical results of MLD in closed-form are derived. Based on these results, the effect of UE activity factor and the uplink power control (PC) factor on the MLD are analysed. The results show that the fog access points (FAP) density has no effect on the MLD.

The rapid growth of Internet of Things (IoT) applications poses a great challenge to the computation capability of smart mobile equipments (SMEs). Fog Computing, as a promising technology, provides fast computing services for resource-limited SMEs in various applications. In this paper, we consider a blockchain-based fog computing network consisting of SMEs, fog nodes (FNs) and the cloud server. To optimize the delay and energy consumption of processing computation-intensive tasks, two offloading models are introduced, namely, task offloading to the device-to-device (D2D) cooperation group and to a nearby FN. Additionally, the blockchain technology is enabled to prevent malicious nodes from modifying with transaction information by maintaining a continuous tamper-proof ledger database. To reduce the delay and energy consumption of the traditional consensus mechanism, we propose the voting-based delegated proof of stake consensus mechanism, in which the FNs with the top half of votes will form a verification set, and the FNs will take turns being the manager to generate new blocks. Furthermore, to minimize the network cost, we jointly optimize task offloading decision, transmission rata allocation and computing resource allocation under various constraints. Finally, the effectiveness of the proposed scheme is demonstrated.

Blockchain technology is used to improve the security of users data in the network. However, the traditional blockchain structure is not suitable for the fog network due to the low throughput and scalability limitations. To solve the above issues, in this paper, we propose a sharding blockchain to improve the security of the fog network, while increasing the throughput. Sharding blockchain will divide the network into several shards, and each of them could process the transactions parallelly. In addition, the normalized entropy of the fog network could be calculated by the main opinion and secondary opinion in the consensus result. Then, the main-chain layer could calculate the probability of malicious fog nodes (FN) in the network and the maximum number of shards. Finally, the S-type fog nodes assignment algorithm (S-NA) is proposed to optimize the association between shards and FNs, which combines the greedy algorithm and the max-min fair algorithm. Simulation results verify the efficiency of the proposed S-NA algorithm.

In mobile computing scenarios, federation learning allows users to jointly train global models in a decentralized manner without exposing private data. However, due to the heterogeneity of the network and devices, the traditional global model often fails to fit the user data distribution, which is inconsistent with the primary condition of federation learning, resulting in accuracy decreasing of global models. Besides, the security of federated learning is decreasing with the increase of malicious attacks. To address the aforementioned issues, in this paper, we explore the cosine similarity of model gradients and design a clustered mechanism to improve learning efficiency. Furthermore, we combine the clustered federated learning with the blockchain-supported fog computing networks, which could verify local models uploaded by users and generate the traceable global models to improve the learning efficiency. Finally, we conduct experiments on several frameworks with the real-world dataset FEMNIST, and the experimental results demonstrate the efficiency and robustness of the blockchain-enabled clustered federated learning framework.

With the emergence of a large number of computational resource-intensive applications and various content delivery services, there is an explosion of data growth in the Internet of Vehicles (IoV). To improve the transmission performance of the IoV, caching content on the edge of the network is considered as a potential solution to reduce the content transmission delay. In this article, we investigate the content caching decisions optimization method in the IoV to minimize the content fetching delay for vehicles, which is based on the vehicle-to-vehicle (V2V) collaboration. A delay-aware content caching (DCC) algorithm in the IoV is proposed, which consists of vehicle associations, content caching, and precaching decisions optimization. First, a delay-aware vehicle associations (DVAs) algorithm is proposed to optimize the vehicle associations. Consequently, based on the vehicle associations results, the content caching decisions are optimized in two network scenarios according to the existence of the handover vehicles. Finally, a practical scenario of Shanghai with time-varying traffic flow is used for simulations and the effectiveness of the proposed DCC algorithm is verified.

Hardware impairment is inevitable in many wireless systems. It is particularly severe in low-cost applications due to the imperfect components used. In this paper, the channel estimation and non-coherent detection problems of hardware impaired signals are studied for a single-carrier, single-antenna and single-hop system. Specifically, three different cases are investigated: signals with additive distortion only, signals with in-phase and quadrature imbalance only, and signals with both impairments. The maximum likelihood and Gaussian approximation methods are used to derive the new non-coherent detectors for amplitude modulated signals, while the maximum likelihood and moment-based methods are employed to design the new channel estimators for all signals. Numerical results show that the new non-coherent detectors outperform the existing non-coherent detectors in the presence of hardware impairment. The performance gain can be as high as 8 dB. They also show that the new channel estimators have much higher accuracy than the existing estimator. In some conditions, the accuracy of the new estimator is about 100 times that of the existing estimator.

This paper presents a part of our ground-breaking work on evaluation of buildings in terms of wireless friendliness at the building-design stage. The main goal is to devise building design practices that provide for a good performance of wireless networks deployed in buildings. In this paper, the interference gain (IG) and power gain (PG) are defined as two figures of merit (FoM) of the wireless performance of buildings. The FoMs bridge the gap between building design and wireless communications industries. An approach to derive exact closed-form equations for these FoMs is proposed for the first time. The derived analytic expressions facilitate straightforward and more computationally efficient numerical evaluation of the proposed FoMs as compared to Monte Carlo simulations for well-known indoor propagation models. It is shown that the derived closed-form expression can be readily employed to evaluate the impact of building properties, such as the sizes and the aspect ratios (ARs) of rooms, on the wireless performance. The proposed approach sheds light to architects on evaluation and design of wireless-friendly building layouts.

Mobile data traffic has been largely generated indoors. However, indoor cellular networks have been studied either on a two-dimensional (2D) plane or as an intractable optimization problem for a multi-story building. In this paper, we develop a tractable three-dimensional small-cell network model for a multi-story building. On each story, the small-cell base stations (BS) are distributed following a 2D homogeneous Poisson point process. We analytically derive the downlink coverage probability, spectral efficiency (SE) and area spectral efficiency for the indoor network as functions of the story height, the penetration loss of the ceiling and the BS density. Our tractable expressions show that a higher penetration loss of the ceiling leads to a higher coverage probability and a higher SE. Meanwhile, with the increase of the story height or the BS density, the downlink coverage probability first decreases and then increases after reaching a minimum value, indicating that certain values of story height and BS density should be avoided for good indoor wireless coverage.

Modelling cellular traffic pattern plays a critical role to efficiently satisfy consumers’ demand, which is skewed distributed and fast-varying. Although current methods can indicate the traffic fluctuation of each cell, it is still not enough as optimisation techniques require to know the traffic distribution inside cells. In this paper, Neural Network is used to improve the resolution to intra-cell level by modelling the hotspots of geo-tagged Twitter. This paper is based on our previous work, which has already quantified the linear relationship between Tweets and mobile traffic. Here, a similarity measurement is designed to quantify how two patterns are similar to each other. We applied this measurement on the geo-tagged Tweets and found that in one of three periods (day, evening, and night), the hotspots distributions are more similar than the other periods. Then for each period, Generative Adversarial Networks (GAN) is used to train a generator for modelling the intra-cell hotspots distribution. Such a trained generator can also continuously generate convincing artificial-data to expand the data set. The similarity measurement gives high similarity (above 0.8) between generated artificial-data and the real test data.

To better characterize indoor small cell networks (SCN), we consider the blockages caused by interior walls and employ the bounded path loss model to derive the expression for energy efficiency (EE) of a downlink massive multiple-input multiple-output (MIMO) SCN. Our EE expression demonstrates that a higher penetration loss of interior walls leads to a higher EE. For the purpose of maximizing EE, we propose a novel genetic algorithm (GA) based scheme to jointly optimize the number of antennas per base station (BS), the number of users per cell, and the transmission power per antenna. Numerical results show that our proposed scheme can achieve almost the identical EE as the optimal greedy search algorithm, while significantly reducing the computational time.

Fog computing is an advanced technique to enhance the Quality of Service (QoS), decrease network latency and energy consumption for Internet-of-Things devices (IDs). In this article, to minimize the overhead of the fog computing network, including the task process delay and energy consumption, while ensuring multiply QoS requirements of different types of IDs, we propose a QoS-aware resource allocation scheme, which jointly considers the association between fog nodes (FNs) and IDs, transmission and computing resource allocation to optimize the offloading decisions while minimizing the network overhead. First, an analytic hierarchy process-based evaluation framework is established to find the preference of QoS parameters and the priority of different types of ID tasks. Second, we introduce a resource block (RB) allocation algorithm to allocate RBs to IDs based on the IDs priority, satisfaction degree, and the quality of RBs. Moreover, a QoS-aware bilateral matching game is introduced to optimize the association between FNs and IDs. Finally, the offloading decisions are based on the previous steps to minimize the network overhead. The simulation results demonstrate that the proposed scheme could efficiently ensure the loading balance of the network, improve the RB utilization, and reduce the network overhead.

Recently, a fog computing network that widely deploys fog nodes (FNs) at the edge of the network has been able to provide better communication performance and powerful computation support to the resource-limited Internet-of-Things (IoT) devices. In this article, we analyze the energy-efficient (EE) resource allocation problem in fog computing networks with the candidate FNs mechanism to ensure the network loading balance under the transmission performance constraints. In the scenario, the associated computation capability allocated to IoT devices from FNs is related to the historical energy consumption and the current energy consumption. The FN that reports nonzero computation capability is considered as the candidate FN and included in the candidate set. Moreover, a candidate FN-based EE resource allocation (CF-EE) algorithm is proposed to maximize network EE, which is converted into the Lyapunov optimization for each time slot. The optimal resource allocation can be obtained by minimizing the upper bound of the Lyapunov drift function and the penalty term to guarantee the loading balance and network stability. Finally, the optimization problem is decomposed into two suboptimization problems: 1) transmission resource allocation optimization and 2) power allocation optimization, and solved separately. The simulation results demonstrate that the proposed CF-EE algorithm can achieve a considerable performance improvement compared with algorithms in the literature.

The tremendous popularity of Internet-of-Things (IoT) applications and wireless devices has prompted a massive increase of indoor wireless traffic. To further explore the potential of indoor IoT wireless networks, creating constructive interactions between indoor wireless transmissions and the built environments becomes necessary. The electromagnetic (EM) wave propagation indoors would be affected by the EM and physical properties of the building material, e.g., its relative permittivity and thickness. In this article, we construct a new multipath channel model by characterizing wall reflection (WR) for an indoor Line-of-Sight (LOS) single-user multiple-input-multiple-output (MIMO) system and derive its ergodic capacity in the closed-form. Based on the analytical results, we define the wireless friendliness of building material based on the spatially averaged indoor capacity and propose a scheme for evaluating the wireless friendliness of building materials. Monte Carlo simulations validate our analytical results and manifest the significant impact of the relative permittivity and thickness of building material on indoor capacity, indicating that the wireless friendliness of building materials should be considered in the planning and optimization of indoor wireless networks. The outcomes of this article would enable appropriate selection of wall materials during building design, thus enhancing the capacity of indoor LOS MIMO communications.

A novel generalized polarization-space modulation (GPSM) is proposed for polarized multiple-input multiple-output (MIMO) systems with a limit number of radio frequency (RF) chains. In the spatial domain, multiple dual-polarized (DP) transmit antennas are activated, and then combinations of those indices are used to convey information. While in the polarization domain, depending on the random input bits, only one polarization state is selected for each active DP transmit antenna to transmit information following the rule of the polarized shift keying. At the receiver, the maximum likelihood detector is employed as a benchmark to detect information bits being used to select the polarization state and activated DP antennas. In the detector, imperfect channel state information (CSI) is taken into account. Two less computationally complex detectors, i.e., a linear detector and a sphere decoding (SD) detector are proposed to relieve the computational burden. Sacrificing the average bit error probability (ABEP) performance, the proposed linear detector can reduce the computational complexity significantly. The proposed SD detector can achieve the optimum ABEP performance, while reducing computational complexity by reducing the search space. A closed-form union upper bound (UUB) on the ABEP of the GPSM system with imperfect CSI at the receiver is analytically derived and validated through simulations. From the UUB, a loose asymptotic bound on the ABEP, which sheds light on deriving the diversity gain and the coding gain, is derived. Numerical results show that the signal-to-noise ratio loss caused by increasing the number of transmit antennas is less than 3 dB while the spectral efficiency is increased by 7 b/z/Hz. Therefore, the GPSM can be a promising candidate of down link massive MIMO systems to achieve a high spectral efficiency with a limit number of RF chains.

Computation offloading can be widely used in Internet of Things (IoT), since most IoT devices do not have enough resource for complex computing. According to the target of computing results, computation offloading can be divided into source driven pattern and destination driven pattern. Currently, most studies focus on the former, where the results should be returned to the task source after computing. Another widely existing pattern, i.e., destination driven computation offloading, where the results should be transmitted to some other node instead of the task source, is rarely noticed. In this paper we propose a scheme of Destination Driven Step-by-step Computation Offloading along the Route (DDSCOR), which optimizes the delay from the source node releasing the task to the results arriving the destination node. Specifically, the proposed scheme combines the data transmission and computation offloading together, dividing the computing task into several procedures and offloading them to appropriate nodes along the data transmission route. Simulation results show that the proposed scheme can approximately achieve the optimal offloading- transmission combined route, and tremendously reduce the overall latency in resource limited IoT.

We investigate the effect of bursty traffic in an LTE and Wi-Fi aggregation (LWA)-enabled network, where part of the LTE traffic is offloaded to Wi-Fi access points (APs) to boost the performance of LTE networks. A Wi-Fi AP maintains two queues containing data intended for the LWA-mode user and the native Wi-Fi user, and it is allowed to serve them simultaneously by using superposition coding (SC). With respect to the existing works on LWA, the novelty of our study consists of a random access protocol allowing the Wi-Fi AP to serve the native WiFi user with probabilities that depend on the queue size of the LWA-mode data. We analyze the throughput of the native Wi-Fi network, accounting for different transmitting probabilities of the queues, the traffic flow splitting between LTE and Wi-Fi, and the operating mode of the LWA user with both LTE and Wi-Fi interfaces. Our results provide fundamental insights in the throughput behavior of such aggregated systems, which are essential for further investigation in larger topologies.

The 1000 fold capacity enhancement is one of the key requirements in the future 5G networks, stimulating the interest in jointly adopting several advanced techniques (e.g. multiple antennas and heterogeneous networks (HetNets)). Analysis of the performance of the HetNets jointly with multiple antennas becomes crucial. In this paper, we analyse the K-tier multi-antenna HetNets from a downlink coverage perspective. The coverage probability is derived using the Gil-Pelaez inversion theorem under the stochastic geometry framework. Moreover, a closed form approximated result is obtained for observing the influence of the normalized range bias (NRB). The result shows that our proposed result closely match the Monte Carlo simulation, and the approximation result is effective for searching the optimal NRB.

Road accidents and traffic congestion are two critical problems for global transport systems. Connected vehicles (CV) and automated vehicles (AV) are among the most heavily researched and promising automotive technologies to reduce road accidents and improve road efficiency. However, both AV and CV technologies have inherent shortcomings, for example, line of sight sensing limitation of AV sensors and the dependency of high penetration rate for CVs. In this paper we present a cooperative connected intelligent vehicles (CAV) framework. It is motivated by the observation that vehicles are increasingly intelligent with various levels of autonomous functionalities. The vehicles intelligence is boosted by more sensing and computing resources. These sensor and computing resources of CAV vehicles and the transport infrastructure could be shared and exploited. With resource sharing and cooperation CAVs can have comprehensive perception of driving environments, and novel cooperative applications can be developed to improve road safety and efficiency (RSE). The key feature of the cooperative CAV system is the cooperation within and across the key players in the road transport systems and across system layers. For example, the various levels of cooperation include cooperative sensing, cooperative RSE applications and cooperation among the vehicles and among the vehicles and infrastructure. We will present the potentials that could be brought by cooperative CAV, the roadmap for research and development, the preliminary research results and open issues.

The Long Term Evolution-Unlicensed (LTE-U) scheme has been proposed to exploit the unlicensed spectrum, especially the 5 GHz band, for cellular networks to further increase capacity. Since the 5 GHz band has already been used by WiFi networks, the carrier sense adaptive transmission (CSAT) scheme has been proposed LTE-U access points (LAPs) to harmoniously coexist with WiFi access points (WAPs), where duty cycles are used by LAPs to leave certain time slots that only allow WAPs to access the unlicensed band. However, the performance of the CSAT scheme has not been sufficiently analyzed for multiple unlicensed channels (UCs) in a large-scale network. In this work, we derive the explicit expressions of downlink successful transmission probabilities (STPs) of LAP users and WAP users for a large-scale multi-UC network using stochastic geometry tools. Based on the derived STPs, the fairness between the LTE-U network and the WiFi network, which is defined as the minimum throughput of LTE-U and WiFi users, are analysed versus the duty cycle and the LAP density. Furthermore, the optimal duty cycle is obtained based on the derived STPs in the duty-cycle and non-duty-cycle durations. Our results show that for a given number of UCs and WAP density, and with the optimal duty cycle, the optimal LAP density increases with the increasing number of UCs for maximizing the fairness, but leads to a poorer minimum throughput performance.

Generalized likelihood ratio test is an effective method for target detection in passive radar systems. The distribution of its decision variable in the presence of a direct path is unknown but is required for the calculation of the detection threshold and the detection probability. In this letter, several new approximations to this distribution are proposed by using moment matching. Numerical results show that the generalized extreme value approximation works consistently well for both null and alternative hypotheses with large or small signal-to-noise ratios. On the other hand, the Gaussian and logistic approximations only work well in the null hypothesis.

In recent years, the explosion of wireless data traffic has resulted in a trend of large scale dense deployment of small cells, with which the rising cost of energy has attracted a lot of research interest. In this paper, we present a novel sleeping mechanism for small cells to decrease the energy consumption of heterogeneous networks. Specifically, in the cell-edge area of a macrocell, the small cells will be put into sleep where possible and their service areas will be covered by the range-expanded small cells nearby and the macrocell; in areas close to the macrocell, the user equipments associated with a sleeping small cell will be handed over to the macrocell. Furthermore, we use enhanced inter-cell interference coordination techniques to support the range expanded small cells to avoid QoS degradation. Using a stochastic geometry-based network model, we provide the numerical analysis of the proposed approach, and the results indicate that the proposed sleeping mechanism can significantly reduce the power consumption of the network compared with the existing sleeping methods while guaranteeing the QoS requirement.

In indoor environments, the performance of ultra-dense cellular networks is significantly affected by blockages, especially for ultra-high frequencies. However, previous works either ignore or simplify such effects for analysing the networks. On the basis of stochastic geometry, this paper proposes a mathematically tractable approach to analyse ultra-dense networks in indoors, which considers both the effects of wall blockages and the distance-based path loss. The effects of wall blockages are firstly investigated by modelling the walls as a Boolean scheme of straight lines on a finite plane. Then a path loss model incorporating both the blockage-based and distance-based path loss is applied to analyse the performance of indoor networks. Finally, the analytical result is validated by comparing it with Monte Carlo simulations. The simulation results also show that the optimum transmitter density is finite for indoor ultra-dense networks with blockages, although the coverage probability benefits from the increase of transmitter density.

Device-to-device (D2D) communications in cellular networks enable direct transmissions between user equipments (UEs). If the source UE (SUE) and the destination UE (DUE) are far away from each other or the channel between them is too weak for direct transmission, then multi-hop D2D communications, where relay UEs (RUEs) forward the SUE's data packets to the DUE, can be used. In this paper, we propose an energy-efficient optimal adaptive forwarding strategy (OAFS) for multi-hop D2D communications. OAFS adaptively chooses between the best relay forwarding (BRF) mode and the cooperative relay beamforming (CRB) mode with the optimal number of RUEs, depending on which of them provides the higher energy efficiency (EE). To reduce the computational complexity for selecting the optimal RUEs for CRB mode, we propose a low-complexity sub-optimal adaptive forwarding strategy (SAFS) that selects between the BRF and the CRB with two RUEs by comparing their EE. Furthermore, a distributed forwarding mode selection approach is proposed to reduce the overhead for forwarding mode selection. The analytical and simulation results show that OAFS and SAFS exhibit significantly higher EE and spectral efficiency than BRF, CRB, direct D2D communications, and conventional cellular communications. SAFS is almost as energy- and spectral-efficient as OAFS.

Long Term Evolution (LTE)-Wireless Local Area Network (WLAN) Path Aggregation (LWPA) based on Multipath Transmission Control Protocol (MPTCP) has been under standardization procedure as a promising and cost-efficient solution to boost Downlink (DL) data rate and handle the rapidly increasing data traffic. This paper aims at providing tractable analysis for the DL performance evaluation of large-scale LWPA networks with the help of tools from stochastic geometry. We consider a simple yet practical model to determine under which conditions a native WLAN Access Point (AP) will work under LWPA mode to help increasing the received data rate. Using stochastic spatial models for the distribution of WLAN APs and LTE Base Stations (BSs), we analyze the density of active LWPA-mode WiFi APs in the considered network model, which further leads to closed-form expressions on the DL data rate and area spectral efficiency (ASE) improvement. Our numerical results illustrate the impact of different network parameters on the performance of LWPA networks, which can be useful for further performance optimization.

Licensed-assisted access (LAA) is a promising technology to meet the exponential increase of traffic demand by exploiting the 5GHz unlicensed spectrum. However, without proper coexistence schemes, the performance of WiFi in coexistence with LAA will be degraded significantly. Equipped with listen-before-talk (LBT) schemes, LAA applies a channel access mechanism similar to distributed coordination function (DCF) in WiFi, which is not optimal in terms of spectrum efficiency. In this paper, we propose an joint optimization scheme to find the optimal combination of WiFi and LAA contention windows (CWs) that maximizes LAA throughput, while guarantees WiFi throughput above a predefined threshold. The accuracy and efficiency of the proposed scheme is evaluated by comparing with the exhaustive search: almost the same combination of CWs are achieved with much lower complexity by using the proposed scheme than the exhaustive search. Numeric results show that the proposed adaptive scheme is more effective in dense scenario, where high system (up to 40%) and LAA throughput gain (up to 100%) are achieved. The trade-off between LAA (system) throughput and WiFi throughput is also revealed.

Device-to-device (D2D) communications in cellular networks facilitate direct transmissions between user equipment (UEs). In the case that direct transmission is not favourable due to long distances or poor channel conditions between the source UE (SUE) and the destination UE (DUE), multi-hop D2D communications can be deployed, where relay UEs (RUEs) forward the data received from SUE to the DUE. In this paper, we propose an adaptive forwarding strategy for multi-hop D2D communications to optimally choose between the best relay forwarding (BRF) mode and the cooperative relays beamforming (CRB) mode with the optimal number of RUEs, depending on which of them provides the higher instantaneous energy efficiency (EE). We analyse the associated average EE taking into account the circuit power consumption and the overhead for obtaining channel state information (CSI), forwarding mode selection and cooperative beamforming. In order to reduce the overhead for mode selection, we propose a distributed adaptive forwarding mode selection strategy. Simulation results reveal that multi-hop D2D communications utilizing the proposed adaptive forwarding strategy achieves significantly higher EE than the BRF, CRB with the optimal number of RUEs, direct D2D communications, and conventional cellular communications.

License-assisted access (LAA) is a candidate feature in 3GPP Rel-13 to meet the explosive growth of traffic demand. The main idea of LAA is to deploy LTE in the unlicensed band (mainly the 5GHz band), which is abundant with available spectrum. However, the major concern is the coexistence between WiFi and LAA in the same band. This paper presents a new framework to evaluate the downlink performance of coexisting LAA and WiFi networks. By using Markov chain, analytical models are established based on WiFi distributed coordination function (DCF) and two listen-before- talk (LBT) schemes. These two LBT schemes are Cat 3 and Cat 4 LBT, which mainly differ in medium access schemes in terms of backoff procedure. Unlike most existing works, which focus on the impact on WiFi performance posed by LAA, the performance of LAA is also evaluated. Our analysis shows that throughput of a WiFi network can be enhanced by adding or replacing WiFi access points (APs) with LAA E-UTRAN Node Bs (eNBs), at the expense of different levels of WiFi performance degradation. A trade-off between WiFi protection and LAA-WiFi system performance enhancement is observed. WiFi throughput and delay are less affected by Cat 4 LBT scheme, while Cat 3 LBT scheme provides higher LAA-WiFi system throughput. The choice of LBT schemes relies on the network planning priority, WiFi performance protection and LAA system performance requirements.

LTE License-Assisted Access (LTE-LAA) in unlicensed spectrum has drawn a lot of attention due to its appealing data traffic offloading potential. However, the coexistence of LTE and WiFi technologies in the same unlicensed bands needs careful design to avoid severe interference between them. In this paper, we propose a coexistence scheme to create opportunities for LTE-LAA small cells and WiFi devices to transmit simultaneously. We combine Multiple Signal Classification (MUSIC) direction of arrival (DOA) estimation with null steering techniques to avoid collisions between LTE-LAA and WiFi transmissions. We assume that the LTE-LAA small cells are equipped with the latest 802.11 receivers for monitoring WiFi transmissions and for capturing simultaneous transmission timing. The performance of the proposed scheme in terms of collision avoidance and channel occupancy time ratio is evaluated via simulations. The results show that with DOA estimation and null steering, LTE-LAA small cells can transmit simultaneously with nearby WiFi devices without causing significant interference to them. As a result, LTE-LAA small cells can gain much more channel access opportunities and longer channel occupancy time while being “invisible” to coexisting WiFi networks.

Channel-width adaptation can significantly improve the connectivity, capacity and reduce the power consumption in wireless networks. The OFDMA-based channel-width adaptation based on traffic demand has been studied in wireless networks with sufficient spectral resources. However, the traffic demand may not be fully satisfied in resource-limited scenarios. In this paper, we study the channel-width adaptation problem in wireless mesh networks (WMNs) with limited spectral resources. More specifically, considering diverse quality-of-service (QoS) requirements, a QoS-aware channel-width adaptation scheme is proposed. First, resource allocation with QoS-aware channel-width adaptation is modelled as an optimization problem. Genetic algorithm is employed to get a near-optimal solution. Then, in order to reduce the computational complexity, a greedy algorithm is developed to suit the highly dynamic traffic demand. Simulation results show that the proposed low-complexity algorithm can guarantee the QoS support in resource-limited scenarios.

The Reduced Power Subframes (RPS) are encouraged to be applied in the LTE-Advanced Heterogeneous Networks (HetNets), to reduce the capacity loss caused by the Almost Blank Subframes (ABS). However, the RPS are supposed to be used in the macrocells only. In fact, the RPS can also be used in femtocells to mitigate the interference that the macrocell edge users suffers, but its performance is not investigated yet. In this paper, we introduce the RPS both in the macrocells and the femtocells. The results of the Signal to Interference Ratio (SIR) coverage probability under the stochastic geometry framework are derived in a closed-form which is verified through Monte Carlo simulation. Based on these results, the macrocell edge users' SIR coverage and the average rate coverage of the network (the average fraction of users achieving a target rate) are analysed numerically. Our proposed scheme enhanced both the SIR of the macro edge users and the average rate coverage probabilities.

In recent years, the explosion of data traffic usage in wireless industry has resulted in a trend of hyper-dense small cells. With the large scale deployment of small cells, the rising cost of energy has attracted lots of interests. In this paper, we present a novel activation and sleep mechanism for small cells to decrease the energy consumption in heterogeneous networks (HetNets). The main idea of our proposed method is explained as follows: in the cell-edge area of a macrocell, the service area of a sleeping small cell will be covered by a range expanded small cell nearby; in areas close to the macro BS, UEs associated with a sleeping small cell will be handed over to the macrocell. Furthermore, we use enhanced inter-cell interference coordination (eICIC) techniques to support range expanded small cells to avoid QoS degradation. The simulation results indicate that the proposed sleeping mechanism can significantly reduce the energy consumption of the network compared with the conventional methods while guaranteeing the QoS requirement.

Energy efficient wireless communications have recently received much attention, due to the ever-increasing energy consumption of wireless communication systems. In this paper, we propose a new energy-efficient cooperative relaying scheme that selects a subset of relays before data transmission, through proactive participation of available relays using their local timers. We perform theoretical analysis of energy efficiency under maximum transmission power constraint, using practical data packet length, and taking account of the overhead for obtaining channel state information, relay selection, and cooperative beamforming. We provide the expression of average energy efficiency for the proposed scheme, and identify the optimal number and location of relays that maximize energy efficiency of the system. A closed-form approximate expression for the optimal position of relays is derived. We also perform overhead analysis for the proposed scheme and study the impact of data packet lengths on energy efficiency. The analytical and simulation results reveal that the proposed scheme exhibits significantly higher energy efficiency as compared to direct transmission, best relay selection, all relay selection, and a state-of-the-art existing cooperative relaying scheme. Moreover, the proposed scheme reduces the signaling overhead and achieves higher energy savings for larger data packets.

In this paper, the spatial modulation (SM) technique is employed at the source and relay nodes in a full-duplex two-way relay channel (FD-TWRC) to support spectral-efficient bi-directional communications while guaranteeing a low cost implementation. Maximum likelihood detectors are employed at each node that is subject to an intrinsic self-loop interference. We first propose a tight upper bound on the average bit error probability (ABEP). Then, based on the ABEP upper bound, an asymptotic ABEP expression is derived in the high signal-to-noise ratio (SNR) regime. Exploiting the asymptotic ABEP, an exact SNR threshold for the selection between FD-TWRC-SM and half-duplex (HD)-TWRC-SM is derived in a closed form, which sheds light on when it is beneficial to select the FD (or HD) mode. In addition, the power allocation (PA) among sources and relay is investigated, through which an optimal PA factor in terms of ABEP is obtained. All analytical results derived in this paper are verified by Monte Carlo simulations, from which some new insights are obtained on the performance of FD-TWRC-SM.

Although the almost blank subframes (ABSFs) proposed in heterogeneous cellular networks can enhance the performance of the cell range expansion (CRE) user equipments (UEs), it significantly degrades the macro-cell total throughput. To address this problem, the low power subframes (LPSFs) are encouraged to be applied in the macro-cell center region by the further-enhanced inter-cell interference coordination. However, the residual power of the LPSF, which interferes with the CRE UEs, and the proportion of the LPSF affect the downlink throughput together. To achieve a better rate coverage probability, the appropriate LPSF power and the proportion are required. In this paper, the analytical results of the overall signal to interference and noise ratio coverage probability and the rate coverage probability are derived under the stochastic geometric framework. The optimal region bias ranges for maximizing the rate coverage probability are also analyzed. The results show that the ABSF still outperform the LPSF in terms of rate with the optimal range expansion bias, but lead to a heavier burden on the backhaul of the pico-cell. However, with a static range expansion bias, the LPSF provide better rate coverage than the ABSF. In addition, in a low-range expansion scenario, the reduced power of the LPSF has negligible effect on the rate coverage with the optimal resource partitioning.

We consider a cooperative multi-path relay channel (MPRC) where multiple half-duplex relays assist in the packet transmissions from a source to its destination. A virtual full-duplex (FD) relaying scheme is proposed that allows the source to transmit a new packet simultaneously with the selected best relay, with the rest of the relays attempting to decode this new packet. Thus, a new source packet can be served in each time slot, as in FD relay systems. Taking into account the effect of inter-relay interference (IRI) that is caused by simultaneous relay and source transmissions, a Markov chain analytical model is used to characterize the decoding performance at the relays, based on which the overall outage probability of MPRC is obtained in closed-form expressions. The asymptotic performance analysis reveals that in low rate scenarios, a close-to-full diversity order is achieved by the proposed scheme while substantially improving the spectrum efficiency. In high rate scenarios, the decoding performance of relays is limited by IRI and the system outage performance experiences an error floor. Simulation results demonstrate the performance gains of the proposed scheme by comparisons with existing half-duplex and FD relay systems in the literature.

Abstract In this paper, we investigate the inbound handover confusion in the two‐tier macrocell‐small cell networks with help of mobility prediction. Instead of studying the mobile user's (MU) movement, we propose an analytical model for the activity status of small cells, which is to exploit the statistical property of inbound handover events that would happen in small cells. We design the cell status prediction algorithm to obtain the optimal prediction outcome of the next status for the small cells. On avoiding the handover confusion, we develop a dynamic allocation approach of physical cell identifier according to the prediction results. We design (i) the cell status prediction‐based strategy, by which the dedicated PCIs will be assigned to the small cells with busy activity status while the other small cells share the public PCIs, and (ii) an integrated strategy in order to fully exploit the usage of PCI. We formulate the preference relation for small cells via reference signal received quality relation integrated with status prediction information using Bayesian average method. Simulation results reveal that the proposed algorithms yield higher accuracy than the conventional methods; in the meantime, handover confusions can be reduced significantly during the inbound handover. Copyright © 2016 John Wiley & Sons, Ltd.

Exponentially growing demand for mobile data is pushing the existing mobile networks to the limit. In addition to offloading traffic onto WiFi access points (APs), dual-band femtocells (DBFs) that can simultaneously access both the licensed and unlicensed bands have been proposed for cellular networks to further exploit the unlicensed spectrum. However, the coexistence between DBFs and WiFi in the unlicensed spectrum is not without difficulty, given the potentially severe co- channel interference. In this paper, we first derive the probability of coverage overlap between DBF and WiFi APs by modelling the network nodes using a spatial bivariate Poisson point process, and then propose a novel framework for DBFs to effectively coexist with WiFi APs in the unlicensed spectrum considering various possibilities of coverage overlap between them. The performance of the proposed framework in terms of DBF channel occupancy in the unlicensed band is evaluated through both theoretical analysis and simulations.

Energy efficiency of wireless communications has received a lot of attention recently owing to the tremendous energy demands resulting from widely deployed wireless networks and mobile personal devices. In this paper, we propose an energy-efficient cooperative relaying scheme for location-aware clustered relays. Energy efficiency is achieved through a low-overhead timer-based relay selection algorithm and by removing the destination feedback of channel state information (CSI) that is usually required for cooperative beamforming. In order to avoid possible collisions between relay transmissions, we propose a distributed mechanism for the selected relays to appropriately insert guard intervals before their transmissions. We analyse the system energy efficiency considering the energy utilized to obtain CSI, to select best relays and to perform cooperative beamforming. Moreover, we investigate the impacts of the number of available relays, the number of selected relays and the location of relay cluster on energy efficiency. The simulation results reveal that the proposed cooperative relaying scheme achieves higher energy efficiency than direct communication, best relay selection, and all relay selection for relay clusters located close to the source. Independent of relay cluster location, the proposed scheme exhibits significantly higher energy efficiency compared to an existing cooperative relaying scheme.

Previous work shows that Average Bit Error Probability (ABEP) of SM systems under Non Line of Sight (NLOS) scenario is lower than that under Line of Sight (LOS) scenario because of a lower spatial correlation with a given Signal to Noise Ratio (SNR). However, when transmit power is constrained as a constant, the SNR under LOS scenario is larger than that under NLOS scenario benefiting from the strong LOS ray. Spatial correlation and SNR are two contradictory factors which both impact the performance of SM systems. In order to analyze performances of SM systems with a constant power under LOS and NLOS indoor scenarios, 15150 complex MIMO channel matrices are measured in a typical building of Shenzhen Graduate School, Harbin Institute of Technology. Based on measured data and motivated by the Minimum Distance Lower Bound (MDLB) approach, Euclid distances between received symbols of SM systems, as a monotonic function of ABEP, is analyzed. It is found that the effect of the SNR is much more important than the spatial correlation in the indoor environment so that SM system under LOS scenario shows a better performance than NLOS when the transmitting power is a constant. Furthermore, it is observed that bottlenecks of ABEP under LOS and NLOS scenarios are different. Under the LOS scenario, the bottleneck of ABEP is the antenna index error. On the contrary, under the NLOS scenario, the bottleneck of ABEP is the joint error. Using two extreme cases, such phenomena is analytically explained in the paper.

Reliability is one of the most concerned issues in wireless communications for smart grid, especially in the home area network(HAN) where the smart meter data are collected. In this paper, we model the HAN as a wireless mesh network that deploys cooperative transmissions for improving link reliability. We then define a new reliability performance metric that depends not only on the channel model but also on the locations of smart meters. Based on the reliability performance metric, we propose a smart meter deployment strategy to maximize the communication reliability. The performance of the proposed HAN deployment strategy is applied to a real home environment. The simulation results show that the proposed deployment strategy is effective in guaranteeing reliability for smart-grid communications.

A cooperative multi-path relay channel (MPRC) with multiple decode-and-forward relay terminals is considered in this paper. In order to enhance the system reliability, the source is encouraged to transmit at a higher power such that more relays can successfully decode the source symbol. This, however, may lead to unnecessary energy waste. For reliable symbol delivery while improving the system energy efficiency, we propose power adaptation schemes at the source and relays depending on the availability of channel state information (CSI). In order to minimize the energy consumption for successfully delivering a source symbol, both the source and relay adaptively select a suitable transmit power from a finite set of discrete power levels. The outage probability and energy efficiency of the cooperative MPRC are analyzed and simulated. Simulation results demonstrate a tradeoff between the outage performance and energy efficiency. Under the same outage performance, the system energy efficiency can be significantly improved by the proposed power adaptation schemes compared to a benchmark case with blind source transmissions.

The most promising approach to enhance network capacity for the next generation of wireless cellular networks (5G) is densification, which benefits from the extensive spatial reuse of the spectrum and the reduced distance between transmitters and receivers. In this paper, we examine the performance of different schedulers in ultra dense small cell deployments. Due to the stronger line of sight (LOS) at low inter-site distances (ISDs), we discuss that the Rician fading channel model is more suitable to study network performance than the Rayleigh one, and model the Rician K factor as a function of distance between the user equipment (UE) and its serving base station (BS). We also construct a cross-correlation shadowing model that takes into account the ISD, and finally investigate potential multi-user diversity gains in ultra dense small cell deployments by comparing the performances of proportional fair (PF) and round robin (RR) schedulers. Our study shows that as network becomes denser, the LOS component starts to dominate the path loss model which significantly increases the interference. Simulation results also show that multi-user diversity is considerably reduced at low ISDs, and thus the PF scheduling gain over the RR one is small, around 10\% in terms of cell throughput. As a result, the RR scheduling may be preferred for dense small cell deployments due to its simplicity. Despite both the interference aggravation as well as the multi-user diversity loss, network densification is still worth it from a capacity view point.

Energy efficiency has become a crucial performance metric in wireless communications due to the increasing energy consumption resulting from increasingly widely deployed wireless communication systems and devices. We propose a timer based relay selection scheme that selects a subset of available relays to perform cooperative beamforming for data transmission in an energy-efficient way. We analyse the resulting energy efficiency taking into account the additional energy consumed by the overhead required to obtain channel state information, to select relay subset and to perform cooperative beamforming. We also study the impact of the number of cooperating relays, their location and different data packet length on energy efficiency. The simulation results show that for relays located close to the source the proposed scheme is more energy efficient than direct communication, conventional best relay selection, all relay selection and an existing cooperative relaying scheme. Moreover, through simulation it is revealed that increasing data packet length leads to significant energy savings.

Heterogeneous cloud radio access networks (H-CRANs) are potential solutions for improving both spectral and energy efficiencies by embedding cloud computing into heterogeneous networks. The interference among remote radio heads (RRHs) can be suppressed with centralized cooperative processing in the base band unit (BBU) pool, while the inter-tier interference between RRHs and macro base stations (MBSs) is still challenging in H-CRANs. In this paper, to mitigate this inter-tier interference, a contract-based interference coordination framework is proposed, in which three scheduling schemes are involved, and the downlink transmission interval is divided into three phases accordingly. The core idea of the proposed framework is that the BBU pool covering all RRHs is selected as the principal that would offer a contract to the MBS, and the MBS as the agent decides whether to accept the contract or not according to an individual rational constraint. An optimal contract design that maximizes the rate-based utility is derived when perfect channel state information (CSI) is acquired at both principal and agent. Furthermore, contract optimization under the situation in which only partial CSI can be obtained from practical channel estimation is addressed as well. Monte Carlo simulations are provided to confirm the analysis, and simulation results show that the proposed framework can significantly increase the transmission data rates over baselines, thus demonstrating the effectiveness of the proposed contract-based solution.

Rapid growth in provisioning of cellular data at low or same energy expenditure is one of the most significant objectives of Information and Communication Technology (ICT) division. The exponential growth of cellular data traffic affects not only the capacity requirements of the cellular networks, but also affects the quality of service. In order to fulfill the increasing demands in cellular communications, femtocells have been proposed as a user based solution in LTE networks. This paper aims to investigate the impact of femtocells deployment on the energy efficiency of LTE/LTE-Advanced networks. The investigation focuses on the energy related issues in the LTE/LTE-Advanced networks and the effectiveness of femtocells in overall energy consumption and throughput. A detailed analysis is carried out on femtocells as an effective solution to save energy, offloading traffic from macrocell and reduce the expenditures of cellular networks.

WiFi access points (APs) are nowadays ubiquitous in multi-level buildings to provide uninterrupted network access to mobile users. WiFi signals are the widely recognized option to provide feasible indoor positioning system (IPS). However majority of IP research concentrates on 2D positioning compared to vertical or floor level determination. To date the most reliable floor determination techniques based on WiFi signals use fingerprinting approaches. However, fingerprint (FP) method is computationally extensive due to its large database size. This paper proposed a new computationally efficient floor determination system based on reduced database. The performance of proposed algorithm is tested using real-world measurement and is compared against other few available floor determination algorithms. The experiment shows that the proposed method outperforms other floor positioning methods by utilizing only 14% of original FP database size.

Bit Error Rate (BER) is a key parameter used to evaluate the performance of radio communication systems. In this paper, a realistic BER of radio MRC diversity systems is predicted based on the Multi-Resolution Frequency Domain ParFlow (MRFDPF) model. The prediction takes into account the correlations among diversity branches and makes no assumptions of the correlation model. The predicted realistic BER for MRC diversity systems can be very useful to many wireless applications, such as, adaptive modulation and coding (AMC) scheme, or optimal power allocation.

Beyond 3G mobile networks will be data service centric. User equipment (UE) stays in the Connected State relatively long while supporting data services, so efficient power‐saving mechanisms for the Connected State are becoming more significant in order to prolong battery life of a handset. Discontinuous reception (DRX) has been a dominant approach for power saving in the Idle State and Connected State. In this study, a novel counter‐driven adaptive DRX (CDA‐DRX) scheme is proposed and analysed. The CDA‐DRX scheme is intended to present a generic and easy‐to‐implement algorithm to adaptively and autonomously adjust DRX cycle to keep up with changing user activity level. The scheme distinguishes itself from all other researches by minimising signalling overhead and easily balancing packet delivery latency and power consumption. Numerical analysis and system simulation showed that this scheme produces a better tuned DRX operation than the ones proposed in literature or suggested in the standards.

Small cells are becoming essential to offer a promising solution for dramatic mobile traffic growth in future mobile networks. Especially, massive enterprise femtocells are expected to provide high capacity to fulfill high data demands in buildings in order to offload indoor traffic from outdoor macro cells in that over 80% of mobile data traffic is generated indoors. However, as one of the major challenges, inter-femtocell interference gets worse in 3D in-building scenarios because of the presence of numerous interfering sources and needs to be considered in the early network planning phase. The indoor network planning and optimization tool, iBuildNet®, makes accurate prediction of indoor wireless RF signal propagation possible to guide actual indoor femtocell deployments. In this paper, a semi-static resource allocation scheme based on fractional frequency reuse (FFR) is developed to avoid interference on cell edge users for dense indoor femtocell deployments. The minimum number of subbands used in the scenario is worked out with a node-coloring heuristic algorithm. Extensive simulations in a multi-floor building are performed over iBuildNet®to evaluate the performances of the developed scheme. The simulation results illustrate that the developed scheme outperform the traditional FFR-3 scheme that is popular in macro-cell networks, in terms of cell edge user data rate and average user data rate.

Dense femtocell networks have emerged as a key technology in residential, office building or hotspot deployments that can significantly improve the coverage and capacity of next-generation wireless networks. But, in the co-channel deployment scenarios, dense inter-femto interference might significantly deteriorate the overall system performance. In this paper, a new cooperative strategy in the dense femtocell networks with backhaul capacity constraints is proposed where multiple dominant interference from dense femtocells is classified into a number of groups, and different cooperative algorithms for these groups are used to mitigate the mutual interference and then obtain better trade-off between performance gains and needed cost. Simulation results show the proposed scheme yields great gains in terms of the average cell throughput, reaching up to 30% relative to the classical of non-cooperative transmissions.

In a recent paper [1], we presented a generalized theoretical analysis for the diffuse multipath power in mobile wireless channels accounting for three-dimensional (3-D) multi-modal (i.e., clustered) scattering. In this paper, a parameterized case-study stemming from [1] is analyzed with multi-modal arrival of diffuse multipath power within four angular sectors. Those sectors are parametrically defined with respect to the mobile receiver's direction of motion. One sector per each quadrant is considered and the parametric probability density function (PDF) of the angle of arrival (AOA) is derived. From this PDF, the second order statistics of mobile channels with multi-modal scattering are derived, namely, the temporal autocorrelation function (ACF), the power spectral density (PSD), or Doppler spectrum, the level crossing rate (LCR) and average fade duration (AFD). Extension to generalized multi-modal arrival within any arbitrary number of angular sectors can be treated similarly.

Femtocell has been considered as a promising technology in wireless communications to extend indoor service coverage and enhance overall network capacity. Two-tier networks, where the current cellular networks, i.e., macrocells, overlapped with a large number of randomly distributed femtocells, can potentially bring significant benefits. If femtocell access points (FAPs) operate within the same frequency band as macrocells, the cross-tier interference (CTI) creates a distinct impact on the system performance. This paper studies the uplink (UL) interference at FAP caused by approaching macrocell users (MUEs). It is noted that the CTI is more significant to the closed subscriber group (CSG) femtocells. We propose an incentive mechanism (IM) for CSG femtocells to alleviate the UL interfference from approaching MUEs, thus preventing the femtocell users performance from degrading and protecting the neighbor FAPs that might also suffer from the UL interference. Meanwhile, the macrocell also benefits from the IM, in terms of energy saving and UL spectral efficiency. Simulation results show that close to the ideal performance when no CTI presents can be achieved with IM, demonstrating that the proposed scheme is very effective in dealing with uplink CTI.

In order to mitigate inter-cell interference and cope with network, traffic, and channel fluctuations in space and time, Orthogonal Frequency Division Multiple Access (OFDMA) based cellular networks should be equipped with dynamic frequency and power allocation algorithms. Although dynamic frequency and power allocation has been well studied separately, very few results have been reported on dynamic joint frequency and power allocation. In this paper, we propose a dynamic frequency and power allocation algorithm for the DownLink (DL) of OFDMA-based cellular networks, where frequency resources are allocated to cells by a central broker to minimize long-term cell-edge inter-cell interference. Moreover, each cell independently assigns its available frequency resources and power to users in a way that its total DL transmission power is minimized while meeting the throughput demands of its users. Simulation results show that the proposed algorithm provides significant improvements in network outage performance, capacity, and throughput as compared with existing schemes.

Beyond 3G mobile networks will be data service centric. It is anticipated that UE will stay in the Connected State relatively long while supporting data services, so efficient power saving mechanisms for the Connected State are more significant in order to prolong the active time of a handset. Discontinuous Reception (DRX) is a dominant approach for power saving in the Idle State and the Connected State. In this paper, a novel Counter-Driven Adaptive DRX (CDA-DRX) scheme is proposed and analyzed. It intends to present a generic and easy-to-implement adaptive DRX scheme that keeps up with the change of user activity level. This autonomous and adaptive power saving scheme distinguishes itself from all other researches surveyed so far from literature by minimizing signaling overhead and easy balancing between packet delivery latency and power saving performance according to QoS requirement of established data services.

In the context of heterogeneous networks, femtocells are very promising. In order to properly simulate their behavior and their impact on the macrocell layer, it is necessary to be able to simulate the radio coverage of femtocells. Hence ParFlow is a possible deterministic model that can be used for such simulation. In this paper two implementations of ParFlow are presented: time domain and frequency domain. The performance are compared and the advantages/drawbacks of each model are investigated.

This article investigates the problem of the allocation of modulation and coding, subcarriers and power to users in LTE. The proposed model achieves inter-cell interference mitigation through the dynamic and distributed self-organization of cells. Therefore, there is no need for any a prior frequency planning. Moreover, a two-level decomposition method able to find near optimal solutions is proposed to solve the optimization problem. Finally, simulation results show that compared to classic reuse schemes the proposed approach is able to pack more users into the same bandwidth, decreasing the probability of user outage.

In this paper, the issue of multi-user radio resource scheduling on the downlink of a Long Term Evolution (LTE) cellular communication system is addressed. An optimization model has been proposed in [1], where radio resources for multiple users are jointly allocated at the air-interface. It has been shown that an optimal solution to such a problem may provide reasonable gain over a simply greedy approach. However, the complexity of such an optimal approach could be prohibitively high. By exploiting meta-heuristic method such as Simulated Annealing (SA), the results in this paper show that significant reduction in complexity can be obtained while achieving near optimal solutions.

In this paper, we present a generalized theoretical analysis for multipath scattering in mobile wireless channels where scattered power arrives into three dimensions (3-D). Moreover, multipath power can arrive into any arbitrary number of discrete angular sectors under an anisotropic propagation regime. A new expression for the azimuth and elevation angle of arrival (AOA) joint distribution of the arriving multipath power is presented. In the general case of multi-modal arrival, we show that the azimuth and elevation AOA are statistically dependent. The latter issue is further investigated by providing certain conditions for statistically independent azimuth and elevation AOA. Due to space limitations, in a follow up paper, a parameterized case study will be analyzed with multi-modal arrival into four angular sectors.

Data services, especially multimedia services drain battery quickly. Hence, it becomes more and more important to reduce power consumption in order to prolong active time of a mobile station. Three types of sleep mode schemes were defined in 802.16e for power saving. Sleep window size is either fixed or exponentially increased in these schemes. Past researches showed that power saving performance can be improved by carefully adjusting the sleep window size. Past researches also tried to balance the power saving performance and packet delivery latency, which contradict with each other. In this paper, a novel counter-driven adaptive sleep mode scheme is proposed and analyzed. It intends to present a generic and easy-to-implement adaptive sleep window scheme that keeps up with the change of user activity level. This autonomous and adaptive power saving scheme distinguishes itself from all other researches surveyed so far in the literature by minimizing signaling overhead and easily balancing between packet delivery latency and power saving performance according to QoS requirement of established data services.

In this paper we propose to use finite difference propagation methods to evaluate the wide band properties of the fast fading. For this purpose we adapted the MR-FDPF propagation model to simulate large bandwidth by combining numerous narrow band simulations. The results are compared with a channel sounder measurement campaign covering a band width of up to 70 MHz. It is verified that fading characteristics in wireless channels varies with frequency and the MR-FDPF method is capable for simulating this variation of fadings for wide band systems.

In this paper, a hybrid model to compute the indoor-to-outdoor path-loss for femtocells is proposed. In this approach, indoor propagation is computed with the FDTD (Finite-Difference Time-Domain) model, thus providing high accuracy inside the building where the femtocell is deployed. On the other hand, outdoor propagation is computed with an empirical approach. Performance of the model is evaluated with on-site radio measurements, and it is shown that the proposed approach reaches a reasonable compromise: it overcomes the low prediction accuracy of using only an empirical model, while reducing the computational load of using FDTD for the whole scenario.

Link adaptation is an important technique in achieving good system spectral efficiency. However, due to the effect of frequency selective fading, the SNRs of adjacent sub-carriers in an OFDMA system can potentially be different. To provide a reasonable signalling overhead, the smallest unit of channel quality feedback is represented in terms of a group of sub-carrier SNRs, or effective SNR for short. The knowledge of the PDF or CDF of the effective SNR can be very useful in performance analysis of systems involving frequency selective feedback. However, closed-form expressions of these statistical quantities are very difficult to obtained. In this paper, we propose to approximate these quantities using the method of Pearson system. Numerical results show that the proposed approximation is very accurate.

This paper proposes a RSSI-aware (rETT) routing metric for WMN. The embedded RSSI information from the mesh nodes are extracted, processed, transformed and incorporated into the routing process. The performance of the rETT metric is then optimised and the results compared with the fundamental hop count metric and the link quality metric by Expected Transmission Count (ETX). The implementation results on OLSR show that rETT improvement on network throughput is more than double (120%) compared to hop count and a 21% improvement compared to ETX. Also, an improvement of 33% was achieved in network delay compared to hop count and 28% better than ETX. This work provides a valuable insight into adapting RSSI information in mesh network routing and the implementation method ensures that the results are realistic.

The Probability Density Function (PDF) or Cumulative Distribution Function (CDF) of the effective Signal to Noise Ratio (SNR) is an important statistical characterization in the performance analysis of an Orthogonal Frequency Division Multiple Access (OFDMA) system using Exponential Effective SNR Mapping (EESM). However, the exact closed form of PDF is extremely difficult to obtain. A general approximation method known as Moment Matching Approximating (MMA) is used to approximate the distribution of effective SNR by a simple expression. In this paper, the approximation by Gaussian, Generalized Extreme Value (GEV) and Pearson distribution are studied. Results show that Gaussian approximation is very useful when the number of sub-carriers is sufficiently large. Both GEV and Pearson approximation are accurate enough in approximating the distribution of effective SNR in a general case.

In an orthogonal frequency division multiple access (OFDMA) system such as LTE and WiMAX, EESM provides an efficient way to estimate the link performance of a resource block/sub-channel contains a number of sub-carriers by a single value (effective SNR). In order to learn the performance of an OFDMA system involving EESM, it is of interest to investigate the statistics of effective SNR over frequency selective channels. In this paper, general expressions including the MGF and average of EESM effective SNR over arbitrary number of sub-carriers are proposed. Subsequently, the analytical result of average effective SNR over Nakagami-m fading channel is also derived. The numerical results show that the approaches are quite useful in characterizing the average effective SNR over frequency selective channels.

This work presents a novel approach for the avoidance of cross-tier interference in two-tier networks comprised of Orthogonal Frequency Division Multiple Access (OFDMA) macrocells and femtocells. This new technique is based on the use of Intracell HandOvers (IHOs), and it makes possible that either a macrocell or a femtocell can reassign its sub-channel or power allocation upon the detection of cross-tier interference. Simulation results show that this approach is able to cope with the cross-tier interference issues intrinsic to closed access femtocells, and the increased number of HandOvers (HOs) resulting from open access femtocells.

Novel methods of obtaining the moment generating function (MGF) and moments of the exponential effective SNR mapping (EESM) signal-to-noise ratio (SNR) over N jointly distributed fading channels are presented. Based on these methods, novel explicit expressions of the MGF and moments over arbitrary number of correlated Nakagami-m fading channels are proposed. Numerical evaluation of these expressions shows that the proposed approach can be a useful and efficient analytical tool in characterizing the statistics of EESM over correlated Nakagami-m fading channels.

Abstract Due to the TDMA (time division multiple access)/time division duplex (TDD) specialization in the uplink (UL) of code division multiplex access (CDMA) systems, some advanced techniques, such as smart antenna (SA) and multi‐user detection (MUD), are utilized conveniently in time division‐synchronous code division multiplex access (TD‐SCDMA) systems. These advanced techniques have great impacts on the capacity and radio resource management (RRM) schemes. In this paper, the UL capacity and load models specified for TD‐SCDMA systems are proposed, in which the impacts from SA and MUD techniques are considered, and the UL load can be estimated based on the total received power in the base station. According to the proposed theoretical capacity and load evaluation models, the call admission control (CAC) strategies suitable for TD‐SCDMA systems are presented. Since there are two kinds of SA schemes (i.e., tracking beam antenna (TBA) and switched beam antenna (SBA)) utilized in TD‐SCDMA systems, the efficient CAC algorithms suitable for these two SA schemes are designed and evaluated, which are based on principles of the interference increase estimation. All simulation results show that the proposed CAC strategies can work efficiently and improve performances of TD‐SCDMA systems dramatically. Copyright © 2009 John Wiley & Sons, Ltd.

With the emergence of new technologies and services, it is very important to accurately identify the factors that affect the design of the wireless access network and define how to take them into account to achieve optimally performing and cost-efficient networks. Till now, most existing efforts have been focused the basic access capability. This article describes a way to deal with the trade-offs generated during the LTE Access network design, and presents a service-oriented optimization framework that offers a new perspective for this process with consideration of the technical and economic factors of this kind of networks. We propose a mixed integer programming model with the use of the method of the Pareto front and Multiobjective Tabu Search. An example of deployment is presented.

The problem of allocating resources for user transmissions on the downlink of a Long Term Evolution (LTE) cellular communication system is studied. A novel optimal multiuser scheduler is proposed and its performance is evaluated. Numerical results show that the system performance improves with increasing correlation among OFDMA sub-carriers. It is found that a limited amount of feedback information can provide a relatively good performance. A sub-optimal scheduler with a lower computational complexity is also proposed, and shown to provide good performance. The sub-optimal scheme is especially attractive when the number of users is large, as the complexity of the optimal scheme may be unacceptably high in many practical situations.

During the network planning process, it is very important to identify the key network design factors and define how to take them into account to achieve optimally performing networks. The introduction of Relay Stations (RSs) in OFDMA-based networks will bring numerous advantages such as coverage extension and capacity enhancement due to the deployment of new cells and the reduction of distance between transmitter and receiver. However, the use of RSs will also bring new challenges to the network designers, for example, interference management in multihop communications. This work establishes the fundamentals of a new approach that will adapt the tilt, azimuth, and frequency assignment in OFDMA networks - in the form of subchannel planning - according to the changing conditions of the traffic and the radio channel in a novel two-step process for network design. An example of deployment is presented in this paper.

This paper introduces a dynamic system-level simulator for WIMAX (Wireless Interoperability for Microwave Access) networks that can be used to analyze the behavior of several network procedures. Moreover, the performance of a new approach to the frequency assignment problem, called DFP (Dynamic Frequency Planning), is studied using this simulation platform. DFP is able to run from a few times a day down to a frame by frame basis, balancing the radio resources between neighbouring BSs (Base Stations) and providing interference mitigation. We demonstrate the efficacy of DFP compared to off-the-shelf FRSs (Frequency Reuse Schemes) in terms of average network capacity and delay.

Adaptive multi-user resource allocation is one of the key features towards high speed wireless network based on Orthogonal Frequency Division Multiplexing Access (OFDMA). According to IEEE 802.16e (WiMAX) standard radio, resource allocation problem has to be performed on a two-dimensional space (frequency and time) with the constraint that each transmitted burst should occupy a contiguous portion of the frame. The paper introduces two different strategies and it investigates the advantages, drawbacks and challenges of the radio resource allocation procedure in two dimensions.

This work presents 2 novel approaches for the self-organization of Orthogonal Frequency Division Multiple Access (OFDMA) femtocells, in which the femtocell is able to dynamically sense the air interface and tune its sub-channel allocation in order to reduce inter-cell interference and enhance system capacity. In the sensing phase, these techniques make use of either messages broadcast by the femtocells or measurements reported by the users, while in the tuning phase, they provide a good solution for the frequency assignment problem. Results shows that it is recommend to use information collected at the user position (measurement reports), when devising self-organization algorithms for tuning the parameters of femtocells.

Femtocells are a promising solution for the provision of high indoor coverage and capacity. Furthermore, OFDMA-based femtocells have proven to be highly versatile when dealing with cross-layer co-channel interference thanks to the allocation of frequency subchannels. However, concerns still exist related to the impact of the different access methods to femtocells in an overlayed network. Femtocells based on a Closed Subscribers Group, where only device owners are allowed connectivity introduce severe interference to macrocell users. On the other hand, Open Access femtocells where any user can connect, does not bring many advantages to the femtocell owner. In this paper, an intermediate access method based on a limited access is proposed. The performance of the model is evaluated throughout system-level simulations and it is shown that limited access contributes to seriously reduce cross-layer interference while guaranteeing a minimum performance to the femtocell subscribers.

In order to avoid inter-cell interference, OFDMA networks are flexible in terms of radio resource management techniques, supporting different frequency reuse schemes (FRSs), which in turn, may decrease inter-cell interference and increase network performance. However, because most of them are based on fix patterns, these FRSs cannot cope with the uneven distribution and dynamic behavior of the traffic throughout the day. This work introduces a novel approach to the frequency assignment problem called dynamic frequency planning (DFP) tailored to OFDMA networks. The proposed approach dynamically adapts the radio frequency parameters to the environment taking the user and channel conditions into account. Moreover, a variant of DFP, called vertical DFP, based on the fractional frequency reuse schemes (FFRSs) concept is proposed. In comparison to the traditional FRSs, these techniques notably mitigate inter-cell interference and enhance network performance.

A novel expression of the moment generating function (MGF) of the exponential effective SNR mapping (EESM) signal-to-noise ratio (SNR) over two correlated but not necessarily identically distributed Nakagami-m fading channels is presented. Based on the MGF, a novel expression for the average effective SNR is also presented. Numerical evaluation of these expressions shows that the proposed approach can be a useful and efficient analytical tool in analyzing the characteristics of EESM over correlated Nakagami-m fading channels.

Finite-difference electromagnetic methods have been used for the deterministic prediction of radio coverage in cellular networks. This paper introduces an approach that exploits the spatial power distribution obtained from these techniques to characterize the random variations of fading due to multipath propagation. Although the presented method is equally applicable to any finite-difference algorithm able of computing electromagnetic field patterns (e.g. PSTD, ParFlow, ...), it has been exemplified here by means of FDTD. Furthermore, in order to test the reliability of this approach, the predicted fading distributions are compared against real measurements in a residential indoor-to-outdoor scenario. Finally, the practical usability of this fading prediction approach is tested throughout implementation in a WiMAX femtocells system-level simulator.

In this paper, we investigate the performance of heterogeneous networks with multi-antenna cooperative relays. Specifically, threshold-based maximum ratio combining (MRC) and selection combining (SC) schemes are adopted for decoding at the relays and the end-to-end (E2E) error rate performance is analyzed by assuming a Nakagami channel model. Numerical results show that the deployment of multi-antennas can reduce the number of required relay nodes, and thus significantly reduce the system cost. On the other hand, the selection of optimal decoding threshold depends on the number of relay nodes, number of antennas, as well as the average SNR value at the receiver. It is also demonstrated that when the BER requirement is not high, the SC relaying scheme is sufficient to provide satisfactory performance, such that the complexity of relays can be effectively reduced.

The network planning aims to allow the maximum number of users and throughput in a network. This imposes a demanding performance requirement on the new relay-based architecture for mobile WiMAX, in which the capacity estimation is complex. In the network design process, where interference avoidance plays an important role, WiMAX supports reconfiguration of the subchannel usage with some frequency reuse schemes and subchannel allocation techniques already proposed in the standard and existing literature. However, these fixed strategies are not the most suitable for multihop scenarios, where some of the advantages that relay can provide are network load balancing or capacity improvement in variable conditions. The scope of this paper is first, to describe and evaluate the main two frame structures proposed in the IEEE 802.16j draft to allow multihop communications; second, to analyze the capacity improvement provided by a new optimization framework for frequency planning which considers the relay WiMAX specific characteristics that influence this process. In addition, we introduced efficient capacity calculations that will allow intensive system level simulations in a Mobile WiMAX multihop environment. Numerical results are given for various configurations allowing interesting observations about WiMAX radio efficiency. These results can be used as an order of magnitude of the real case of IEEE 802.16j network design.

Multiuser scheduling is an important aspect in the performance optimisation of a wireless network as it allows multiple users to efficiently access a shared channel by exploiting multiuser diversity. For example, the 3GPP cellular standard supports multiuser scheduling in the high speed downlink packet access (HSDPA) feature. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. Multiuser scheduling is studied assuming the availability of perfect CSI, which would require a high bandwidth overhead. A more realistic imperfect CSI feedback in the form of a finite set of channel quality indicator values is assumed, as specified in the HSDPA standard. A global optimal approach and a simulated annealing (CSA) approach are used to solve the optimisation problem. Simulation results suggest that the performances of the two approaches are very close even though the complexity of the simulated annealing (SA) approach is much lower. The performance of a simple greedy approach is found to be significantly worse.

In the traditional medium access control (MAC) protocols for wireless sensor networks (WSNs), energy consumption is traded for throughput and delay. However, in future WSNs, throughput and delay performance had better not be sacrificed for energy conservation. Here first, an incompletely cooperative game-theoretic heuristic-based constraint optimisation framework is introduced to achieve the goals of throughput, delay and energy conservation simultaneously. Then a simplified game-theoretic MAC (G-MAC) protocol is presented, which can be easily implemented in WSNs. Simulation results show that compared with two typical MAC protocols for WSNs, sensor MAC and timeout MAC, G-MAC can increase system throughput, and decrease delay and packet-loss-rate, while maintaining relatively low energy consumption.

In this paper, a multiobjective optimization framework that tackles the problem of mobile WiMax access network design is presented. In the first stage of the network development, the most important issue is to find an appropriate solution to the base station location from a given set of candidate sites. The network can be considerably improved if the base station location solution found during the planning phase is designed to achieve optimal performance, and also reliable and cost-effective mobile WiMax networks. The design process is done through computer simulation to predict the network performance, and it is often carried out by planning and optimization tools similar to those used in the development of 2nd generation cellular networks (2G) with a few adaptations. The multiobjective optimization framework gives network providers a new perspective in mobile WiMax access network design, providing a clear and comprehensive description of different options and solutions to achieve an optimal base station location. Also, it analyzes the network performance with the mobile WiMax-specific parameters that most importantly affect the access network design process. It simplifies the problem and translates it into a formal optimization routine with consideration of economic factors in mobile WiMax networks. This has been done by using the method of the Pareto front, and the use of an optimization strategy based on a modified version of the metaheuristic Tabu search adapted to multiobjective optimization.

The challenge of scheduling user transmissions on the downlink of a long term evolution (LTE) cellular communication system is addressed. A maximum rate algorithm which does not consider fairness among users was proposed in . Here, a multiuser scheduler with proportional fairness (PF) is proposed. Numerical results show that the proposed PF scheduler provides a superior fairness performance with a modest loss in throughput, as long as the user average SINRs are fairly uniform. A suboptimal PF scheduler is also proposed, which has a much lower complexity at the cost of some throughput degradation.

A novel expression of the moment generating function (MGF) of the exponential effective SNR mapping (EESM) signal-to-noise ratio (SNR) over two correlated but not necessarily identically distributed Nakagami-m fading channels is presented. Based on the MGF, a novel expression for the average effective SNR is also presented. Numerical evaluation of these expressions shows that the proposed approach can be a useful and efficient analytical tool in analyzing the characteristics of EESM over correlated Nakagami-m fading channels.

Abstract In this paper, based on the analysis of the experimental data using a new post‐processing method for time‐domain channel measurements, a new double‐cluster statistical model for UWB systems with a bandwidth lower than 1 GHz in non‐line‐of‐sight (NLOS) indoor propagation environment is proposed. By using the proposed model, both the model itself and the parameter estimation of the corresponding model are simplified. By defining the polarity of a particular model parameter, the model has the flexibility to deal with both ‘soft NLOS’ and ‘hard NLOS’ indoor propagation environments. Therefore, the channel impulse responses (CIRs) generated by the proposed model ‘resemble’ the measured CIR better than the SV (Saleh‐‐Valenzuela)/IEEE 802.15.3a model not only in terms of the average values, but also in terms of the cumulative distribution functions (CDFs) of the small‐scale statistics. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract IEEE 802.11 is one of the most influential wireless LAN (WLAN) standards. Point coordination function (PCF) is its medium access control (MAC) protocol with real‐time traffic (rt‐traffic) quality‐of‐service (QoS) guarantees. In PCF, it is very likely that non‐real‐time traffic (nrt‐traffic) will use the contention free period (CFP) that should be dedicated to traffic having higher priority such as rt‐traffic. Therefore, a modified PCF protocol called MPCF, which is based on hub polling and an integrated QoS differentiation, is presented in this paper. With the integrated QoS differentiation, MPCF can prioritize bandwidth requests according to service classes and QoS requirements. With hub polling, MPCF can reduce the bandwidth for control frames and improve the network throughput. A simple and accurate analytical model is derived and presented in this paper to calculate the system throughput of MPCF. Simulation results show that MPCF protocol is much better than PCF in terms of system capacity and rt‐traffic QoS guarantees. Copyright © 2008 John Wiley & Sons, Ltd.

There are several factors that influence the design of optimal IEEE 802.16e networks. In the first stage of the network development, the most important issue is to find an appropriate solution for the base station location from a given set of candidate sites. The network can be considerably improved if the base station location solution found during the planning phase is designed to achieve optimal performance and cost-efficient WiMAX networks. This work presents a framework that gives the operators a new perspective in mobile WiMAX network design by simplifying the problem and translating it into a formal optimization routine with consideration of economic factors. We propose a mixed integer programming model that considers the WiMAX specific features and services, and present optimization strategies based on four meta-heuristics, Tabu Search, Variable Neighborhood Search, Genetic Algorithm and Simulated Annealing. Experimental results are provided to compare the performance of those heuristic algorithms allowing new interesting observations about the quality and robustness of the different strategies in WiMAX.

Game theory is a useful and powerful tool for performance analysis and optimization of wireless mesh networks (WMNs). Based on incompletely cooperative game theory, a mesh router can estimate the game state (e.g., the number of competing nodes), and broadcast this information to its clients. Then all the clients play a cooperative game based on the estimated game state, and achieve the optimal equilibrium strategy. For each client to implement the game independently as it cannot get the game state accurately or timely sometimes, particle swarm optimization is introduced into the game, and a game-theoretic particle swarm optimization scheme (G-PSO) for WMNs is presented in this paper. Simulation results show that G-PSO can increase system throughput and decrease delay, jitter and packet-loss-rate.

Bit or frame error rates are commonly used as performance measures in wireless communication systems. However, in emerging applications such as voice over IP (VoIP), the bit rate outage probability is often a more useful performance measure. In this paper, an accurate method is proposed for approximating the probability distribution of the downlink received signal-to-interference ratio (SIR) in the high speed downlink packet access (HSDPA) channel of a 3GPP network. Based on this distribution, a power adjustment scheme is proposed to minimize the weighted sum of bit rate outage probabilities for multiple users. It is shown that the proposed method can greatly improve outage probability fairness without incurring a very large degradation in throughput.

The problem of efficiently allocating resources to high speed uplink packet access (HSUPA) users in a 3GPP network is studied. A discrete optimization problem is formulated in which user channel qualities and buffer sizes are used at the base station to jointly allocate uplink resources. To circumvent the possibility of local optima, a linearized version of this formulation is also presented. It is shown that knowledge of user buffer sizes can significantly improve the overall achievable bit rate.

The Finite-Difference Time-Domain (FDTD) is not just one of the most accurate propagation prediction methods but also one of the most computationally intensive models, especially when simulating big areas such as urban environments. To overcome this problem, one common approach is to perform simulations at a much lower frequency than that of the real system, and to calibrate the simulation parameters to adjust the frequency response of the materials. In this study a theoretical analysis of the consequences of applying such frequency reduction is performed. To compare the theoretical study some numerical experiments are conducted and matched to the analytical results. Finally, the error of the simulations at lower frequencies is evaluated, allowing us to obtain an explanation for the observed behavior.

In IEEE 802.11 WLANs, the fundamental MAC mechanism - DCF, only supports best-effort service, and is unaware of QoS. IEEE 802.11e EDCA supports service differentiation by differentiating contention parameters. This may introduce the problem of selfish traffics with rational nodes. Hence, game-theoretic EDCA (G-EDCA) is proposed in this paper to solve the problem. Moreover, a simple and run-time estimation mechanism is presented to implement G-EDCA in current WLANs. Extensive simulations are also carried out to compare the performances of DCF, EDCA and G-EDCA. The simulation results show that G-EDCA performs better than DCF and EDCA in terms of system throughput and QoS.

In WSNs, energy conservation is the primary goal, while throughput and delay are less important. So used energy is traded for throughput and delay. In this paper, a novel concept of incompletely cooperative game theory is used in WSNs to simultaneously achieve all the goals. In the game, each node adjusts its equilibrium strategy to the estimated game state. After discussing the utility function of the game, the equilibrium strategy for the game in WSNs is presented, Moreover, a simplified game-theoretic MAC protocol (G-MAC) is provided for WSNs, by using an auto degressive backoff mechanism, which is easy to be implemented. Simulation results show that the incompletely cooperative game can increase system throughput, and decrease delay and packet-loss-rate, while still maintaining reasonable energy consumption, and that G-MAC supports the game effectively.

The fundamental medium access control mechanism in IEEE 802.11 wireless LANs (WLANs)–distributed coordination function (DCF) only supports the best-effort service and does not support quality-of-service (QoS) differentiation. Enhanced distributed channel access (EDCA) in IEEE 802.11e supports delay differentiation. A new approach, EDCA+, is proposed to enhance QoS over WLANs. It simultaneously achieves bandwidth, delay and jitter differentiation by distinguishing the minimum contention window, the maximum backoff stage or persistent factor and packet-loss rate differentiation by distinguishing the retry limit. Analytical models are proposed to analyse the performance of EDCA+ in terms of throughput, bandwidth, delay, jitter and packet-loss rate. Extensive simulations are also carried out to evaluate the accuracy of the proposed performance models and to compare the performance of DCF, EDCA and EDCA+. The simulation results show that EDCA+ performs better than DCF and EDCA in ensuring integrated QoS, and that the proposed analytical models are valid.

Recently, game theory becomes a useful and powerful tool to research mobile ad hoc networks (MANETs). Wireless LANs (WLANs) can work under both infrastructure and ad hoc modes, and are the most widely used MANETs. In this paper, we propose a novel concept of incompletely cooperative game theory and use it to improve the performance of WLANs. In this game, firstly, each node estimates the current state of the game (i.e., the number of competing nodes) by detecting the channel. Secondly, each node changes its equilibrium strategy by tuning its local contention parameters based on the estimated game state. Finally, the game is repeated finitely to get the optimal performance. Our simulation results show that the incompletely cooperative game can increase system throughput, and decrease delay, jitter and packet-loss-rate.

To obtain a good trade-off between accuracy and the computational load of WCDMA (wideband code-division multiple access) network planning and optimisation, link-level performance factors such as the impact of soft handover and fast power control need to be taken into account in system-level simulations. However, they have not been investigated together in previous works. In this paper, we propose a mixed integer programming model considering these factors in both uplink and downlink, and present optimisation strategies based on three meta-heuristics, namely simulated annealing (SA), evolutionary SA (ESA) and variable neighbourhood search (VNS). The base station location problem is modelled as a simplified p-median problem. Experimental results are provided to compare the performance of different algorithms with respect to the solution quality and the level of robustness.

Radio networks of universal mobile telecommunication system (UMTS) need accurate planning and optimisation, and many factors not seen in second generation (2G) networks must be considered. However, planning and optimisation of UMTS radio networks are often carried out with static simulations, for efficiency and to save time. To obtain a good trade-off between accuracy and computational load, link-level performance factors need to be taken into account. The authors propose a mathematical model for UMTS radio network planning taking into consideration fast power control, soft handover and pilot signal power in both uplink and downlink. Optimisation strategies are investigated based on three meta-heuristics: genetic algorithm, simulated annealing (SA) and evolutionary-SA. The base station location problem is modelled as a simplified p-median problem, and parameter tuning of these meta-heuristics are presented. Extensive experimental results are used to compare the performance of different algorithms in terms of statistical measurements.

Directional distributed co-ordination function (D-DCF), a modified medium access control protocol of IEEE 802.11, is proposed to support hybrid antennas such as smart adaptive array antennas and normal omni-directional antennas, in one wireless local area network cell. Nodes equipped with smart antennas follow D-DCF and nodes equipped with normal antennas follow DCF. D-DCF based on a hybrid virtual carrier sense mechanism maintains compatibility with DCF. In D-DCF, before sending any data-frames, the sender and receiver node transmit a pilot sequence by means of an omni-directional request-to-send/clear-to-send handshake mechanism. Based on the pilot, the directional beam can be formed by the smart antenna. Then the node can transmit its data-frame in the directional mode. The other nodes save the transmission time between the sender and receiver in the omni-directional mode in their network allocation vectors. When the sender and receiver communicate in the directional mode, the other nodes can access the channel to send their data-frames. Hence, D-DCF supports space division multiplexing. Moreover, D-DCF fully supports time division duplex, namely both the forward and backward transmission in one access period. Simulation results show that D-DCF can support the hybrid antenna system effectively and provide much higher network throughput, lower delay, jitter and packet-loss-rate than DCF does.

This paper proposes an adaptive routing protocol called ARFA for an integrated cellular and ad hoc heterogeneous network with flexible access (iCAR-FA). Based on the presentation of the iCAR-FA physical characteristics, the paper details the design issues and operation of ARFA. Detailed numerical analysis on route request rejection rate, which is along with some general evaluations, has indicated the effectiveness and efficiency of the ARFA protocol.