Low Earth orbit (LEO) satellite constellations are emerging as a core enabler of sixth-generation (6G) wireless systems, providing global coverage and high-capacity connectivity. However, dense multi-beam LEO deployments introduce severe inter-beam and inter-satellite interferences, while rapid orbital motion results in frequent handovers and highly dynamic channels that challenge the real-time optimization. To address these issues, this paper has proposed a digital twin (DT)-driven multi-LEO network architecture that integrates ray tracing-based virtual simulations with intelligent on-orbit control. Within this framework, we develop a DT-driven Efficient handover and Multi-Agent Twin delayed deep deterministic policy gradient (DEMAT) scheme, which jointly optimizes beam training, handover, power allocation, and beamwidth adaptation to maximize energy efficiency (EE) while satisfying user throughput requirements. DEMAT leverages bidirectional DT-LEO parameter exchange and federated learning-enhanced agents for cooperative and low-latency resource management. Extensive simulations validate its convergence and scalability under diverse network configurations such as various time frame intervals and user densities. Notably, DEMAT achieves up to a 59.6% EE improvement over the Deep Deterministic Policy Gradient (DDPG) baseline and more than 40% of EE compared to the other DT-based benchmarks, demonstrating superior adaptability and coordination for the next-generation non-terrestrial networks.

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multimodal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator.

Integrated sensing and communication (ISAC) systems are emerging as a key technology to optimize the use of wireless resources by simultaneously supporting communication and sensing functions. This paper introduces an innovative ISAC system that integrates Wi-Fi channel state information (CSI)-based sensing and frequency-modulated continuous-wave (FMCW) radar within the Wi-Fi frequency band to enhance energy efficiency (EE). Wi-Fi CSI-based sensing enables simultaneous communication and environmental sensing using existing infrastructure, but it often suffers from high power consumption. Conversely, FMCW radar offers lower power consumption with the capability of self-transmission and reception but is limited by bandwidth constraints when operating in Wi-Fi bands. To overcome these challenges, we formulate the resource allocation problem as an optimization task that maximizes EE while maintaining sensing accuracy, managing power consumption, and respecting bandwidth limitations. The proposed system utilizes a dueling deep Q-Network (DQN) with reward shaping to learn optimal resource allocation strategies, showing a 36% to 40% increase in EE compared to traditional DQN models, providing a viable direction for the advancement of ISAC technologies.

In this paper, a novel network architecture that deploys the multi-functional reconfigurable intelligent surface (MFRIS) in low-Earth orbit (LEO) is proposed. Unlike traditional RIS with only signal reflection capability, the MF-RIS can reflect, refract, and amplify signals, as well as harvest energy from wireless signals. Given the high energy demands in shadow regions where solar energy is unavailable, MF-RIS is deployed on LEO to enhance signal coverage and improve energy efficiency (EE). To address this, we formulate a long-term EE optimization problem by determining the optimal parameters of MF-RIS, including amplification, phase-shifts, energy harvesting ratios, and LEO transmit beamforming. To address the complex non-convex and non-linear problem, a federated learning enhanced multi-agent deep deterministic policy gradient (FEMAD) scheme is designed. Multi-agent deep deterministic policy gradient (DDPG) of each agent can provide the optimal action policy from its interaction with environments, whereas federated learning enables the hidden information exchange among multi-agents. In numerical results, we can observe significant EE improvements compared to the other benchmarks, including centralized deep reinforcement learning as well as distributed multi-agent DDPG. Additionally, the proposed LEO-MF-RIS architecture has demonstrated its effectiveness, achieving the highest EE performance compared to the scenarios of fixed/no energy harvesting in MF-RIS, traditional reflection-only RIS, and deployment without RISs/MF-RISs.

Integrated sensing and communication (ISAC) enables the simultaneous operation of communication and sensing functionalities, leading to improved efficiency and performance. Furthermore, multi-AP coordination (MAP-Co), which provides higher transmission coverage and capacity, has attracted significant attention. Therefore, in this paper, we propose an indoor ISAC scenario assisted by the MAP-Co scheme. By jointly optimizing transmit power, bandwidth, and subchannel selection, we aim to maximize transmission throughput while guaranteeing a minimum required human presence detection accuracy. To effectively solve the complicated task, we propose a novel multi-head self-attention-assisted reinforcement learning (MH-SARL) algorithm. In our algorithm, the communication head is designed to search for the optimal resource allocation policy to maximize transmission throughput, whilst the sensing head focuses on suggesting adequate spectrum utilization to satisfy the predefined sensing quality. Simulation results demonstrate the effectiveness of our proposed MH-SARL algorithm under different scenarios and parameter settings. Compared to other benchmarks in the open literature, MH-SARL can achieve the highest transmission rate of at least 5% increase with guaranteed sensing accuracy.

Earth exploration satellite service (EESS) plays a crucial role in environmental monitoring and weather forecasting by utilizing passive sensing technologies. However, the rapid expansion of terrestrial and satellite communication networks has introduced significant interference challenges, particularly in frequency bands that overlap with or are adjacent to EESS sensors. In this work, we develop a system model that explicitly characterizes EESS interference by considering reflected signal effects and spatial interference accumulation. Based on this model, we propose a EESS-aware resource allocation (EARA) framework that jointly optimizes power allocation and user association, while ensuring that interference to EESS sensors remains within acceptable limits. A non-convex joint optimization problem is formulated and efficiently solved leveraging the Lagrangian dual transform and Dinkelbach’s method. Simulation results demonstrate that the proposed EARA scheme achieves up to 26.3% higher sum rate compared to genetic algorithm and binary whale optimization algorithm, while strictly satisfying the ITU-defined interference threshold. This work establishes a foundation for future research on the coexistence of communication networks and passive Earth observation systems, offering practical strategies for interference mitigation and spectrum sharing in next-generation networks.

In recent years, indoor human presence detection based on supervised learning (SL) and channel state information (CSI) has attracted much attention. However, existing studies that rely on spatial information of CSI are susceptible to the environmental changes, which degrade prediction accuracy. Moreover, SL-based methods require time-consuming data labeling for retraining models. Therefore, it is imperative to design a continuously monitored model using a semi-SL (SSL)-based scheme. In this article, we conceive a bifold teacher-student (BTS) learning approach for indoor human presence detection in a scenario with two adjoining rooms. The proposed SSL-based primal-dual teacher-student network intelligently learns spatial and temporal features from labeled and unlabeled CSI datasets. Additionally, the enhanced penalized loss function leverages entropy and distance measures to distinguish drifted data, i.e., features of new datasets affected by time-varying effects and altered from the original distribution. Experimental results demonstrate that the proposed BTS system accomplishes an averaged accuracy of around 98% after retraining the model with unlabeled data. BTS can sustain an accuracy of 93% under the changed layout and environments. Furthermore, BTS outperforms existing SSL-based models in terms of the highest detection accuracy of around 98% while achieving the asymptotic performance of SL-based methods.

Total isotropic sensitivity (TIS) measurement is strongly required by the industry, but the procedure takes a long time. We explore a machine learning (ML) approach to speed up TIS test procedure. The experiments are conducted using 5G devices and frequency bands. The results show that our methodology can improve measurement efficiency by 35% to 65%, while still maintain high accuracy within 1 dB deviation from standard procedure. Disconnection is a critical issue during TIS measurement, so we design a calibration mechanism to reduce the risk of disconnection. Our approach can be applied widely to different system configurations. It supports not only 5G but also previous generations on different frequency bands.

The innovation of Wi-Fi 6, IEEE 802.11ax, was be approved as the next sixth-generation (6G) technology of wireless local area networks (WLANs) by improving the fun-damental performance of latency, throughput, and so on. The main technical feature of orthogonal frequency division multiple access (OFDMA) supports multi-users to transmit respective data concurrently via the corresponding access points (APs). However, the conventional IEEE 802.11 protocol for Wi-Fi roaming selects the target AP only depending on received signal strength indication (RSSI) which is obtained by the received Response frame from the APs. In the long term, it may lead to congestion in a single channel under the scenarios of dense users further increasing the association delay and packet drop rate, even reducing the quality of service (QoS) of the overall system. In this paper, we propose a multi-agent deep Q-Iearning for fast roaming (MADAR) algorithm to effectively minimize the latency during the station roaming for Smart Warehouse in Wi-Fi 6 system. The MADAR algorithm considers not only RSSI but also channel state information (CSI), and through online neural network learning and weighting adjustments to maximize the reward of the action selected from Epsilon-Greedy. Compared to existing benchmark methods, the MADAR algorithm has been demonstrated for improved roaming latency by analyzing the simulation result and realistic dataset.

Simultaneously transmitting and reflecting reconfig-urable intelligent surface (STAR-RIS) capable of manifesting the wireless channel provides the capability of signal reflection and refraction. However, conventional STAR-RIS has its limitation owing to signals impinging from one side of the surface, sup-porting either uplink (UL) or downlink (DL) users. Therefore, a novel concept of double-sided STAR-RIS (DS-STAR) becomes a promising solution, enabling signals impinging from both sides of the surface. In this paper, we consider multiple DS-STARs in a full-duplex (FD) enabled multi-input-multi-output (MIMO) system. We aim for maximizing joint UL/DL data rate by configuring transmit beamforming of the base station (BS) and UL users as well as configuration of DS-STARs, while ensuring quality-of-service (QoS) for both the UL/DL users. To tackle the complex problem, a genetic algorithm (GA) enhanced multi-agent Q-learning (G-MAQ) scheme is designed. MAQ considers a QoS-aware reward with each parameters as a sub-agent, whereas GA is applied to automatically optimize the hyperparameters of MAQ. In numerical results, we observe the significant im-provement of G-MAQ compared to that without hyperparameter optimization. Moreover, the proposed architecture of DS-STARs in FD networks achieves the highest rate compared to single-sided STAR-RIS, RIS and deployment without RIS/STAR-RIS. Additionally, the proposed G-MAQ scheme of DS-STAR FD sys-tems outperforms the other existing methods in open literature.

Integrated sensing and communication (ISAC) has become a key technology in the sixth-generation (6G) wireless networks, catering to the growing need for ubiquitous sensing and communication tasks. Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can harness both reflective and refractive signals delivered. Due to orientation limitation of STAR-RISs, the multi-dual STAR-RISs (MD-STAR) is conceived to facilitate full-plane services in ISAC systems. In this paper, we intend to solve active beamforming of dual-function radar-communication (DFRC)-enabled BSs and passive beamforming of MD-STAR in ISAC systems, aiming for maximizing the achievable sum rate constrained by the maximum position error bound (PEB) as well as hardware limitation of MD-STAR. In order to solve this complex problem, we propose a two-layered multi-agent federated Q-learning (TMFQ) scheme. The inner layer Q-learning focuses on obtaining the solution of BSs and MD-STAR, whilst the outer layer Q-learning aims for optimizing the hyperparameters, including learning rate and discount rate of the inner-layer one. Additionally, we employ federated learning to facilitate information exchange between agents in the inner Q-learning. We evaluate our proposed TMFQ in terms of different numbers of MD-STAR elements, transmit antennas, and sensing targets. Benefiting from hyperparameter optimization of the inner layer Q-learning and information exchange of federated learning, the proposed TMFQ can achieve the highest rate compared to the other benchmarks, including Q-learning without hyperparameter optimization and without federated learning, heuristic algorithm, and conventional beamforming.

Low Earth orbit (LEO) has a significant potential to provide ubiquitous global coverage with high capacity data services in sixth generation (6G) wireless networks. Due to the denser deployment of LEO satellites, it becomes mandatory to mitigate the interference induced by LEO beams. The high mobility of LEOs further stirs up a complex interference scenario different from conventional terrestrial networks. Therefore, we conceive a multi-LEO constellation that incorporates multi-beamforming and handover by using only the information of signal-to-interference-plus-noise ratio (SINR) and beam indexes. In this paper, we propose a joint LEO handover and fast beam switching (HOBS) algorithm that performs handover, beam search, and beam/power resource allocation. Our goal is to maximize energy efficiency (EE) while satisfying the SINR requirement of each user. We evaluate our proposed HOBS scheme in terms of different user densities, time frame sizes, and beam-sweeping schemes. Benefiting from a comparatively smaller beam search space, HOBS is capable of providing lower latency, as well as higher SINR and EE compared to conventional exhaustive beam search and fixed power control.

As frequency modulated continuous wave (FMCW) radars are becoming a key component of autonomous driving technology, mutual interference among automotive FMCW radars is a significant challenge. In this work, we propose a novel signal processing solution to effectively mitigate mutual interference among FMCW radars. The proposed framework distinguishes the target echoes and interference using their characteristics in the time-frequency spectrum, both of which appear as lines but with different slope characteristics. An interference map is generated from the interference contaminated time-frequency spectrum by employing the Hough transform along with slope filtering. The interference map is then used as a mask for interference mitigation. The interference-mitigated signal is reconstructed by applying time-frequency ridge-based mapping to subjugate the power loss associated with the target signals. We carry out extensive simulations to demonstrate the interference mitigation capabilities of the proposed Hough trans-form and time-frequency ridge (HTFR)-based scheme in terms of the signal-to-interference plus noise ratio (SINR), correlation coefficient, and computational efficiency.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) provides a promising way to expand coverage in wireless communications. However, limitation of single STAR-RIS inspire us to integrate the concept of multi-hop transmissions, as focused on RIS in existing research. Therefore, we propose the novel architecture of multi-hop STAR-RISs to achieve a wider range of full-plane service coverage. In this paper, we intend to solve active beamforming of the base station and passive beamforming of STAR-RISs, aiming for maximizing the energy efficiency constrained by hardware limitation of STAR-RISs. Furthermore, we investigate the impact of the on-off state of STAR-RIS elements on energy efficiency. To tackle the complex problem, a Multi-Agent Global and locAl deep Reinforcement learning (MAGAR) algorithm is designed. The global agent elevates the collaboration among local agents, which focus on individual learning. In numerical results, we observe the significant improvement of MAGAR compared to the other benchmarks, including Q-learning, multi-agent deep Q network (DQN) with golbal reward, and multi-agent DQN with local rewards. Moreover, the proposed architecture of multi-hop STAR-RISs achieves the highest energy efficiency compared to mode switching based STAR-RISs, conventional RISs and deployment without RISs or STAR-RISs.

Three-dimensional (3D) positioning technology plays an important role in millimeter wave (mmWave) non-terrestrial or integrated sensing and communication (ISAC) networks in sixth-generation (6G) systems. However, the complexity of joint range and orientation estimation in mmWave hybrid beamforming (HBF) systems forms a technical hurdle to its practical realization. In view of the recent advancement in green learning (GL) technology, a low-complexity GL architecture is developed herein for 3D positioning in mmWave HBF systems. The entire architecture only consists of one layer of unsupervised representation learning, followed by a supervised feature learning stage and a regression layer for parameter estimation. Compared to the typical deep learning method, the complexity of the proposed method is at least 3 order lower, while the performance is comparable to that of the maximum likelihood estimations of individual parameters given perfect knowledge of the other parameters. This presents the potential of GL in ISAC for future$6G$systems.

In recent years, the demand for pervasive smart services and applications has increased rapidly. Device-free human detection through sensors or cameras has been widely adopted, but it comes with privacy issues as well as misdetection for motionless people. To address these drawbacks, channel state information (CSI) captured from commercialized Wi-Fi devices provides rich signal features for accurate detection. However, existing systems suffer from inaccurate classification under a Nonline-of-Sight (NLoS) and stationary scenario, such as when a person is standing still in a room corner. In this work, we propose a system called colorization and contrastive learning enhanced NLoS human presence detection (CRONOS), which generates dynamic recurrence plots (RPs) and color-coded CSI ratios to distinguish mobile and stationary people from vacancy in a room, respectively. We also incorporate supervised contrastive learning to retrieve substantial representations, where consultation loss is formulated to differentiate the representative distances between dynamic and stationary cases. Furthermore, we propose a self-switched static feature-enhanced classifier (S3FEC) to determine the utilization of either RPs or color-coded CSI ratios. Our comprehensive experimental results show that CRONOS outperforms existing systems that either apply machine learning or nonlearning-based methods, as well as non-CSI-based features in the open literature. CRONOS achieves the highest human presence detection accuracy in vacancy, mobility, Line-of-Sight (LoS), and NLoS scenarios.

In this paper, we consider a prospective receiving hybrid beamforming structure consisting of several radio frequency (RF) chains and abundant antenna elements in multi-input multi-output (MIMO) systems. Due to conventional costly full connections, we design an enhanced partially connected beamformer employing a low-density parity-check (LDPC)-based structure. As a benefit of the LDPC-based structure, information can be exchanged among clustered RF/antenna groups, which results in a low computational complexity order. Advanced message passing (MP) capable of inferring and transferring information among different paths is designed to support the LDPC-based hybrid beamformer. We propose a message-passing enhanced antenna and RF chain selection (MARS) scheme for minimizing the operational power of antennas and RF chains of the receiver as well as hybrid beamforming. Furthermore, sequential and parallel MP schemes for MARS are designed, namely, MARS-S and MARS-P, respectively, to address the convergence speed issue. A heuristic genetic algorithm is designed for receiving hybrid beamforming, comprising gene generation initialization, elite selection, crossover, and mutation. Simulations validate the convergence of both the MARS-P and the MARS-S algorithms. Due to the asynchronous information transfer of MARS-P, it requires higher power than MARS-S, which strikes a compelling balance among power consumption, convergence, and computational complexity. It is also demonstrated that the proposed MARS scheme outperforms the existing benchmarks using the heuristic method of fully/partially connected architectures in the open literature by requiring the lowest power and realizing the highest energy efficiency.

The joint uplink/downlink (JUD) design of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is conceived in support of both uplink (UL) and downlink (DL) users. Furthermore, the dual STAR-RISs (D-STAR) concept is conceived as a promising architecture for 360-degree full-plane service coverage, including UL/DL users located between the base station (BS) and the D-STAR as well as beyond. The corresponding regions are termed as primary (P) and secondary (S) regions. Both BS/users exist in the P-region, but only users are located in the S-region. The primary STAR-RIS (STAR-P) plays an important role in terms of tackling the P-region inter-user interference, the self-interference (SI) from the BS and from the reflective as well as refractive UL users imposed on the DL receiver. By contrast, the secondary STAR-RIS (STAR-S) aims for mitigating the S-region interferences. The non-linear and non-convex rate-maximization problem formulated is solved by alternating optimization amongst the decomposed convex sub-problems of the BS beamformer, and the D-STAR amplitude as well as phase shift configurations. We also propose a D-STAR based active beamforming and passive STAR-RIS amplitude/phase (DBAP) optimization scheme to solve the respective sub-problems by Lagrange dual with Dinkelbach’s transformation, alternating direction method of multipliers (ADMM) with successive convex approximation (SCA), and penalty convex-concave procedure (PCCP). Our simulation results reveal that the proposed D-STAR architecture outperforms the conventional single RIS, single STAR-RIS, and half-duplex networks. The proposed DBAP of D-STAR outperforms the state-of-the-art solutions found in the open literature for different numbers of quantization levels, geographic deployment, transmit power and for diverse numbers of transmit antennas, patch partitions as well as D-STAR elements.

Underwater image recognition is crucial for underwater detection applications. Fish classification has been one of the emerging research areas in recent years. Existing image classification models usually classify data collected from terrestrial environments. However, existing image classification models trained with terrestrial data are unsuitable for underwater images, as identifying underwater data is challenging due to their incomplete and noisy features. To address this, we propose a cross-modal augmentation via fusion ( CMAF ) framework for acoustic-based fish image classification. Our approach involves separating the process into two branches: visual modality and sonar signal modality, where the latter provides a complementary character feature. We augment the visual modality, design an attention-based fusion module, and adopt a masking-based training strategy with a mask-based focal loss to improve the learning of local features and address the class imbalance problem. Our proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/WilkinsYang/CMAF .

For millimeter or Tera-Hertz communications, channel capacity is often found limited by its channel diversity due to the sparse or line-of-sight transmission conditions in these frequency bands. This diversity problem exacerbates particularly in future non-terrestrial communications. Wireless communication that exploits the diversity of orbital angular momentum (OAM) in electro-magnetic waves is considered one of the potential technologies to push the limit despite the skepticism for its long-rang transmissions in free space. Inspired by the recent advances on hybrid beamforming, we propose a novel OAM beam focusing method to explore the feasibility of multiple input multiple output (MIMO) OAM wireless transmissions. With the proposed optimal beam focusing and power allocation scheme and the use of a concentric uniform circular array (CUCA) of diameter 1.2 meters (m), we show that signals of up to three OAM modes can be delivered to a receive uniform circular array of diameter 20 cm at a distance of 500 m, which presents the potential of MIMO OAM for future 6th generation communication systems.

Low-Earth orbit (LEO) satellites have been prosperously deployed for various Earth observation missions due to its capability of collecting a large amount of image or sensor data. However, traditionally, the data training process is performed in the terrestrial cloud server, which leads to a high transmission overhead. With the recent development of LEO, it is more imperative to provide ultra-dense LEO constellation with enhanced on-board computation capability. Benefited from it, we have proposed a collaborative federated learning for low Earth orbit (FELLO). We allocate the entire process on LEOs with low payload inter-satellite transmissions, whilst the low-delay terrestrial gateway server (GS) only takes care for initial signal controlling. The GS initially selects an LEO server, whereas its LEO clients are all determined by clustering mechanism and communication capability through the optical inter-satellite links (ISLs). The re-clustering of changing LEO server will be executed once with low communication quality of FELLO. In the simulations, we have numerically analyzed the proposed FELLO under practical Walker-based LEO constellation configurations along with MNIST training dataset for classification mission. The proposed FELLO outperforms the conventional centralized and distributed architectures with higher classification accuracy as well as comparably lower latency of joint communication and computing.

Channel State Information (CSI) is widely adopted as a feature for indoor localization. Taking advantage of the abundant information from the CSI, people can be accurately sensed even without equipped devices. However, the positioning error increases severely in non-line-of-sight (NLoS) regions. Reconfigurable intelligent surface (RIS) has been introduced to improve signal coverage in NLoS areas, which can redirect and enhance reflective signals with massive meta-material elements. In this paper, we have proposed a Transformer-based RIS-assisted device-free sensing for joint people counting and localization (WiRiS) system to precisely predict the number of people and their corresponding locations through configuring RIS. A series of predefined RIS beams is employed to create inputs of fingerprinting CSI features as sequence-to-sequence learning database for Transformer. We have evaluated the performance of proposed WiRiS system in both ray-tracing simulators and experiments. Both simulation and real-world experiments demonstrate that people counting accuracy exceeds 90%, and the localization error can achieve the centimeter-level, which outperforms the existing benchmarks without employment of RIS.

The open radio access network (O-RAN) architecture offers a cost-effective and scalable solution for internet service providers to optimize their networks using machine learning algorithms. The architecture’s open interfaces enable network function virtualization, with the O-RAN serving as the primary communication device for users. However, the limited frequency resources and information explosion make it difficult to achieve an optimal network experience without effective traffic control or resource allocation. To address this, we consider mobility-aware load balancing to evenly distribute loads across the network, preventing network congestion and user outages caused by excessive load concentration on open radio unit (O-RU) governed by a single open distributed unit (O-DU). We have proposed a multi-agent multi-armed bandit for load balancing and resource allocation (mmLBRA) scheme, designed to both achieve load balancing and improve the effective sum-rate performance of the O-RAN network. We also present the mmLBRA-LB and mmLBRA-RA sub-schemes that can operate independently in non-realtime RAN intelligent controller (Non-RT RIC) and near-RT RIC, respectively, providing a solution with moderate loads and high-rate in O-RUs. Simulation results show that the proposed mmLBRA scheme significantly increases the effective network sum-rate while achieving better load balancing across O-RUs compared to rule-based and other existing heuristic methods in open literature.

The demand for device-free indoor localization using commercial Wi-Fi devices has rapidly increased in various fields due to its convenience and versatile applications. However, random frequency offset (RFO) in wireless channels poses challenges to the accuracy of indoor localization when using fluctuating channel state information (CSI). To mitigate the RFO problem, an error vector spectrum (EVS) is conceived thanks to its higher resolution of signal and robustness to RFO. To address these challenges, this paper proposed a novel error vector assisted learning (EVAL) for device-free indoor localization. The proposed EVAL scheme employs deep neural networks to classify the location of a person in the indoor environment by extracting ample channel features from the physical layer signals. We conducted realistic experiments based on OpenWiFi project to extract both EVS and CSI to examine the performance of different device-free localization techniques. Experimental results show that our proposed EVAL scheme outperforms conventional machine learning methods and benchmarks utilizing either CSI amplitude or phase information. Compared to most existing CSI-based localization schemes, a new paradigm with higher positioning accuracy by adopting EVS is revealed by our proposed EVAL system.

Wi-Fi channel state information (CSI) has become a promising solution for non-invasive breathing and body motion monitoring during sleep. Sleep disorders of apnea and periodic limb movement disorder (PLMD) are often unconscious and fatal. The existing researches detect abnormal sleep disorders in impractically controlled environments. Moreover, it leads to compelling challenges to classify complex macro- and micro-scales of sleep movements as well as entangled similar waveforms of cases of apnea and PLMD. In this paper, we propose the attention-based learning for sleep apnea and limb movement detection (ALESAL) system that can jointly detect sleep apnea and PLMD under different sleep postures across a variety of patients. ALE-SAL contains antenna-pair and time attention mechanisms for mitigating the impact of modest antenna pairs and emphasizing the duration of interest, respectively. Performance results show that our proposed ALESAL system can achieve a weighted F1-score of 84.33, outperforming the other existing non-attention based methods of support vector machine and deep multilayer perceptron.

Low Earth orbit (LEO) satellite constellation is capable of providing global coverage area with high-rate services in the next sixth-generation (6G) non-terrestrial network (NTN). Due to limited onboard resources of operating power, beams, and channels, resilient and efficient resource management has become compellingly imperative under complex interference cases. However, different from conventional terrestrial base stations, LEO is deployed at considerable height and under high mobility, inducing substantially long delay and interference during transmission. As a result, acquiring the accurate channel state information between LEOs and ground users is challenging. Therefore, we construct a framework with a two-way transmission under unknown channel information and no data collected at long-delay ground gateway. In this paper, we propose hierarchical multi-agent multi-armed bandit resource allocation for LEO constellation (mmRAL) by appropriately assigning available radio resources. LEOs are considered as collaborative multiple macro-agents attempting unknown trials of various actions of micro-agents of respective resources, asymptotically achieving suitable allocation with only throughput information. In simulations, we evaluate mmRAL in various cases of LEO deployment, serving numbers of users and LEOs, hardware cost and outage probability. Benefited by efficient and resilient allocation, the proposed mmRAL system is capable of operating in homogeneous or heterogeneous orbital planes or constellations, achieving the highest throughput performance compared to the existing benchmarks in open literature.

IEEE 802.11ax system has been adopted to provide enhanced throughput performance for next-generation wireless local area networks. Its orthogonal frequency division multiple access (OFDMA) allows massive users to concurrently utilize different subbands for data transmission from their corresponding access points (APs). However, severe adjacent channel interference (ACI) incurs overlapping channels under the scenarios of dense users with multiple APs, which should be properly alleviated to provide adequate system throughput. In this paper, we propose a long-/short-term reinforcement learning channel allocation (LSRCA) scheme to effectively mitigate ACI for multi-AP scenarios in IEEE 802.11ax systems. With the considerations of signal features from both long and short time durations, the LSRCA algorithm can maximize effective sum rate through online adaptation and learning via the updates of two Q-tables for weighting adjustments and action execution. Experimental results in realistic fields have demonstrated the effectiveness of LSRCA scheme by providing higher system throughput compared to existing benchmark methods.

With explosively escalating service demands, beyond fifth generation (B5G) aims to realize various requirements for multi-service networks, i.e., higher performance of mixed enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services than 5G. To flexibly serve diverse traffic, various functional split options (FSOs) are specified by 5G protocols enabling different network functions. In order to improve signal qualities for edge users, we consider flexible FSO (FFS) based coordinated multi-point (CoMP) transmission as a prominent technique capable of supporting high traffic demands. However, due to conventional confined hardware processing capability, a processor sharing (PS) model is introduced to deal with high latency for multi-service FSO-based networks. Therefore, it becomes essential to assign CoMP-enhanced functional split modes under PS model. A more tractable FSO-based network in terms of ergodic rate and reliability is derived by stochastic geometry approach. Moreover, we have proposed CoMP-enhanced functional split mode allocation (CFSMA) scheme to adaptively assign FSOs to provide enhanced mixed throughput and latency-aware services. The simulation results have validated analytical derivation and demonstrated that the proposed CFSMA scheme optimizes system spectrum efficiency while guaranteeing stringent latency requirement. The proposed CFSMA scheme with the designed PS FFS-CoMP system outperforms the benchmarks of conventional FCFS scheduling, non-FSO network, fixed FSOs, and limited available FSO selections in open literature.

Multi-numerology (MN) providing a flexible transmission frame structure has attracted a considerable attention for supporting abundant services for beyond fifth-generation (B5G) networks. However, mobility induces severe performance degradation under different numerologies including temporal and spectral fluctuation, which is not well-investigated in existing literature. We have conceived an MN-enabled energy efficiency (EE) problem aiming for alleviating mobility- and MN-induced interferences through moderate power and sub-carrier assignment, while considering quality-of-service (QoS) and latency requirements for different services. We propose a multi-numerology based power and resource block allocation (MNPRA) scheme considering time-/frequency-division (TD/MD) based MN leveraging temporal and spectral features among numerologies. The original non-solvable problem is theoretically transformed into a convex one by employing Dinkelbach process, Taylor approximation and difference of two concave functions (D.C.). Convergence of proposed MNPRA scheme is analyzed and verified by simulations. In simulation results, we have evaluated MNPRA under different service demands, user velocities and MN types. Our proposed scheme outperforms the conventional single-numerology framing and existing methods in open literature, which results in performances of higher EE as well as of lower throughput/delay outage probability.

This paper presents a novel approach to conduct highly efficient federated learning (FL) over a massive wireless edge network, where an edge server and numerous mobile devices (clients) jointly learn a global model without transporting the huge amount of data collected by the mobile devices to the edge server. The proposed FL approach is referred to as spatio-temporal FL (STFL), which jointly exploits the spatial and temporal correlations between the learning updates from different mobile devices scheduled to join STFL in various training epochs. The STFL model not only represents the realistic intermittent learning behavior from the edge server to the mobile devices due to data delivery outage, but also features a mechanism of compensating loss learning updates in order to mitigate the impacts of intermittent learning. An analytical framework of STFL is proposed and employed to study the learning capability of STFL via its convergence performance. In particular, we have assessed the impact of data delivery outage, intermittent learning mitigation, and statistical heterogeneity of datasets on the convergence performance of STFL. The results provide crucial insights into the design and analysis of STFL-based wireless networks.

Sleep apnea syndrome is a common sleep disorder that can lead to a variety of diseases. The traditional diagnostic method, polysomnography (PSG), is time-consuming, expensive, and inconvenient for patients. In this paper, we proposed the transfer learning based apnea detection (TLAD) system as a non-contact based method utilizing the channel state information (CSI) from commercial Wi-Fi devices. In order to reduce the overhead of collecting CSI data and improving efficiency during training process, the transfer learning technique is applied to establish pre-trained model by utilizing open source contact-based thoracic movement data. Moreover, existing research works detect apnea based on breathing pauses and shallow breathing periods, which are not effective to identify complex apnea characteristics. This potential drawback is overcome in proposed TLAD system since both CSI amplitude and frequency features are extracted for apnea classification. Our experimental results showed that the TLAD system achieves an F1-score of 90.1, which is superior to other existing methods.

Substantially increasing wireless traffic and extending serving coverage is required with the advent of sixth-generation (6G) wireless communication networks. Reconfigurable intelligent surface (RIS) is widely considered as a promising technique which is capable of improving the system sum rate and energy efficiency. Moreover, full-duplex (FD) multi-input-multi-output (MIMO) transmission provides simultaneous transmit and received signals, which theoretically provides twice of spectrum efficiency. However, the self-interference (SI) in FD system is a challenging task requiring high-overhead cancellation, which can be resolved by configuring appropriate phase shifts of RIS. This paper has proposed an RIS-empowered full-duplex interference cancellation (RFIC) scheme in order to alleviate the severe interference in an RIS-FD system. We consider the interference minimization of RIS-FD MIMO while guaranteeing quality-of-service (QoS) of whole system. The closed-form solution of RIS phase shifts is theoretically derived with the discussion of different numbers of RIS elements and receiving antennas. Simulation results reveal that the proposed RFIC scheme outperforms existing benchmarks with more than 50% of performance gain of sum rate.

Device-free indoor human presence detection problem has been studied in recent years based on supervised learning and wireless signals such as channel state information (CSI). Thanks to the abundant spatial information of CSI, we can perform indoor presence detection more accurately. Nevertheless, the CSI is susceptible to humidity, temperature, and even machine restarts, resulting in unexpected changes and prediction failures of the trained model. The most intuitive approach to resolve this time-varying phenomenon is to retrain the model by recollecting and labeling CSI data. However, it is time- and labor-consuming to label the dataset every time we retrain the model. Our proposed self-attention based semi-supervised learning for adjoining room presence detection (SAS-PD) system provides an alternative solution to retrain the detection model without any effort to label data and consequently overcome the time-varying issue. The proposed teacher/student training strategy can effectively classify four classes based on a single permanent labeled CSI dataset and recollected unlabeled CSI datasets. In addition, the positional encoder is adopted to enhance time domain correlations of CSI data. Experimental results show that our proposed SAS-PD system can provide enhanced presence detection accuracy under time-varying environments, and it can almost reach the upper bound of the training algorithm with supervised learning.

Device-free human presence detection via infrared sensors or cameras has been well-developed in the past years. However, the infrared-based solutions suffer from misdetection problems with standstill people; while camera-based systems incur personal privacy issues. In recent years, wireless signals were adopted for presence detection, and channel state information (CSI) is one of the most popular information to achieve higher detection accuracy. Nonetheless, existing methods result in misclassification under non-line-of-sight (NLoS) static scenarios when the person stands still in the corner of the room. In this paper, based on multi-antenna Wi-Fi access points, we proposed the CSI ratio with coloring-assisted learning presence detection (CALPD) system that can detect human presence even when the person is motionless in the NLoS scenarios. The CSI ratio between antennas is illustrated on the complex plane to visualize the classification differences. Next, the RGB images are generated based on the proposed coloring-based classifier in order to distinguish and predict the final results. Field experimental results show that our proposed CALPD scheme outperforms other existing methods by achieving higher detection accuracy, especially under NLoS static scenarios.

Terahertz (THz) communication with ultra-wide available spectrum is a promising technique that can achieve the stringent requirement of high data rate in the next-generation wireless networks, yet its severe propagation attenuation significantly hinders its implementation in practice. Finding beam directions for a large-scale antenna array to effectively overcome severe propagation attenuation of THz signals is a pressing need. This paper proposes a novel approach of federated deep reinforcement learning (FDRL) to swiftly perform THz-beam search for multiple base stations (BSs) coordinated by an edge server in a cellular network. All the BSs conduct deep deterministic policy gradient (DDPG)-based DRL to obtain THz beamforming policy with limited channel state information (CSI). They update their DDPG models with hidden information in order to mitigate inter-cell interference. We demonstrate that the cell network can achieve higher throughput as more THz CSI and hidden neurons of DDPG are adopted. We also show that FDRL with partial model update is able to nearly achieve the same performance of FDRL with full model update, which indicates an effective means to reduce communication load between the edge server and the BSs by partial model uploading. Moreover, the proposed FDRL outperforms conventional non-learning-based and existing non-FDRL benchmark optimization methods.

IEEE 802.11ax supports orthogonal frequency division multiple access (OFDMA) separating the channel into multiple resource units (RUs), which achieves higher flexibility and diversity in terms of spectrum utilization compared to conventional 802.11 mechanisms. Benefited by multi-band resources, enormous stations (STAs) are allowed to access different bands for data transmission from the serving access points (APs). Conventional uplink (UL) OFDMA non-random access (UONRA) specified in 802.11ax confronts with the bottleneck of buffer state report phase which lacks appropriate scheduling and queueing information. Furthermore, dense scenario with multi-AP deployment for downlink (DL) data transfer is challenging to assign limited radio resources due to adjacent channel interference (ACI) problem. Based on the aforementioned issues, we propose an enhanced queue-aware arbitration-based contention and resource allocation (QCRA) scheme for joint UL arbitration-based contention and DL resource assignment to mitigate collision occurrence and severe interference. The proposed objective aims at maximizing the effective sum rate by jointly considering UL overhead and DL rate which is theoretically resolved via difference of concave (D.C.) method and Taylor approximation. Simulation results reveal that the proposed QCRA scheme outperforms the other existing benchmarks in open literatures in terms of both higher UL contention successful probability and effective DL sum rate.

In the fifth-generation (5G) communication system, millimeter-wave (mmWave) technology brings superior capabilities, such as higher capacity, lower latency, and a flexible beamforming structure. The interference management strategies play an important role in mmWave beamforming networks to support the multibeam operation and maximize the overall data rates for user equipments (UEs). Currently, most of the existing research do not jointly consider designs, including the mainlobe interference (MI) avoidance and resource blocks (RBs) fairness allocation. In this article, a novel fairness allocation strategy is proposed to achieve the minimum MI and a fair RB assignment for mmWave networks. To achieve the minimum MI, an MI mitigation (MIM) algorithm is designed to maximize the data rate for each UE. With the adaptive mini-timeslot design, the MIM algorithm can achieve MI cancelation for all UEs at each identical timeslot and beam. To combine MIM and fairness for RB allocation among all UEs, the MIM-fairness allocation (MIM-FA) algorithm is also presented. Based on a novel mini-timeslot with designed multiple frames, the MIM-FA algorithm can simultaneously guarantee the fairness among all UEs and mitigate MI at each mini-timeslot for each beam. Additionally, the MIM-FA algorithm can be verified that it achieves the maximum user data rate with the identical number of RBs under the lowest number of frames. Simulation results validate that the proposed MIM and MIM-FA algorithms can provide a higher data rate and better fairness for different scenarios compared to current state-of-the-art competitive approaches.

This paper investigates the first resource management problem for vehicular-to-infrastructure (V2I) networks under the in-band full-duplex (IBFD) backhauling scheme with guaranteed ultra-reliable and low-latency (URLLC) service. The considered networks suffers from interference caused by three node transmission in IBFD scheme and the mobility of vehicular user equipments (VUEs). The resource allocation problem is formulated to jointly optimize VUE association, resource block assignment (RA) and power allocation (PA), while satisfying the reliability and latency constraints at the same time. The formulated problem is a mixed integer non-linear problem with non-convex objective function and constraints. Finding globally optimal solution for this type of problem is still an open problem. To develop tractable solutions, the original problem is firstly simplified using derived equivalent expression for the objective function. The proposed method then decomposed it into RA and PA sub-problems. In each iteration, the PA sub-problem is solved for a set of given PA solution; while the solution for PA sub-problem is searched under determined RA results. The RA and PA sub-problems are solved iteratively until the converging condition is achieved. Theoretical analysis proves that the proposed method achieves Nash-stable equilibrium and local optimality for RA and PA sub-problems respectively. Simulation results verify the effectiveness of the derived performance analysis and demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms in the literatures.

In this paper, we study a delay-aware admission control (AC) and beam allocation (BA) problem with the consideration of quality of experience (QoE) and dynamic variation of channel condition for centralized unit (CU) and distributed unit (DU) based functional split options (FSOs) in millimeter wave (mmWave) fronthaul downlink networks. The original optimization problem aims to maximize time-average QoE subject to delay and queue stability constraints. The intractability of the considered problem comes from the necessity to allocate resources across periods of time slots. After applying virtual queue transformation and Lyapunov optimization method, the targeting problem can be converted into two independent AC and BA sub-problems in each time slot. The converted problem involves actual and virtual queues, which causes conflicting tendency. With the design of a flexible Lyapunov function, the influences of actual and virtual queues are theoretically to be proved balanced. Moreover, the AC sub-problem can be solved by Karush-Kuhn-Tucker condition, whereas mmWave BA sub-problem is tackled by genetic algorithm and matching game for the CU-based and DU-based FSOs, respectively. Simulation results demonstrate the applicability of the proposed CU-/DU-based algorithms in terms of average data rate, admission control rate, queue length, and corresponding system delay under either uniform or dense deployment of DUs. Moreover, the proposed scheme achieves the lowest delay and highest QoE performances while sustaining the system stability compared with the state-of-the-art mechanisms in open literatures.

The mobile wireless communication system with high-speed massive transmissions attracts immense attention in the fifth-generation (5G) new radio (NR) networks. However, it provokes difficulties to provide high-performance services for massive connections due to inter-carrier interference (ICI) caused by mobility factors. Furthermore, under the limited number of hardware antennas, it becomes essential to develop an effective testbed that is capable of emulating and evaluating an excessive number of users. In this paper, we propose virtual user equipment (VUE) emulation system with a VUE generator emulating massive VUE transmissions under a limited number of antennas. The precoding scheme is designed to transform the virtual channels of mobile VUEs to the realistic channels of antennas of the VUE generator. With the consideration of channel transformation estimation error and ICI, we propose the antenna selection and power/sub-carrier allocation (APSA) scheme aiming to maximize the achievable system sum rate. Simulation results demonstrate that the performance is influenced by VUE positions and channel errors in the designed VUE emulation system. Moreover, the proposed APSA scheme outperforms other methods in terms of quality-of-service (QoS) outage and sum rate.

In recent years, pedestrian flow analysis has gained popularity in public area such as shopping malls, hospitals or public facilities. Also, as the location-based service (LBS) become prevalent, more indoor environments have provided wireless positioning system which can record user's location and generate user's trajectory database. In this paper, a pedestrian flow analysis scheme is proposed on the basis of recorded location sequences provided by indoor wireless positioning system. To consider different application scenarios for pedestrian flow and with the existence of positioning errors, we proposed a trajectory regularization method to normalize the location sequences in a suitable format. Furthermore, to analysis the pedestrian flow, a trajectory consecutive pattern mining method which considers the sequential continuity of the trajectories is proposed based on the properties and proofs of consecutiveness of frequent patterns. Simulation results show that our proposed scheme can provide effective pedestrian flow analysis for both route and hotspot scenarios with lowered computational complexity.

Human presence detection via camera-based monitoring systems has been well-adopted in various applications including smart homes, factories, and hospitals. However, its privacy concerns have been raised in many occasions such as daycare centers and homes with elderly living alone. In recent years, literatures adopting wireless signals were proposed to resolve privacy issues for presence detection; nevertheless, existing works can only be applied in a single room scenario. In this paper, we are the first work to propose a device-free multi-room human presence detection system based on efficient star network topology. Our proposed conditional recurrent architecture-based multi-room presence detection (C-MuRP) system extracts both spatial and temporal features from the Wi-Fi channel state information (CSI). Associated with a voting scheme, the proposed novel deep-learning architecture classifies the states of multi-room with the condition on current waveform to emphasize present feature states. Real-time experimental results showed that our proposed C-MuRP system can achieve higher accuracy for multi-room presence detection compared to existing methods.

In recent years, indoor positioning system employing Bluetooth has attracted tremendous attention. However, it is investigated that its received channel information is significantly affected by hardware configuration and wireless environments, especially the received dataset of angle of arrival (AOA), which leads to inaccurate channel estimation and positioning. Further-more, AOA estimation at larger angle direction is severely influenced by the variant wireless environment and signal distortion, which has not been resolve in existing literature. In this paper, we propose an advanced I/Q density-based AOA estimation (IQDAE) to deal with the above-mentioned problem, which consists of two sub-schemes. We firstly employ the designed phase difference (PD) filter to convert I/Q signals to phase information and then select the candidate sets by eliminating outliers. Afterward, we conceive a PD density-based classification algorithm to estimate AOA. The experimental results show that the mean absolute error of proposed IQDAE algorithm is comparably smaller than that from the other schemes, including commercial solutions, especially at larger angles. The results indicate that we can effectively increase the service range for the Bluetooth positioning system by adopting the proposed algorithm.

Internet of Things (IoT) is a promising technology attracting huge attentions in recent years, allowing an excessive number of connections between sensors and devices. Different from conventional human-oriented applications, long range (LoRa) developed in IoT facilitates massive simultaneous sensor data transmissions for the low-power wide-area network (LPWAN), where massive LoRa devices perform packet contention and backoff mechanisms to access opportunity for uplink data transfer. Therefore, it is compellingly imperative to take into account the fluctuation of different channel qualities and various traffic-buffer types for the optimum contention policy, which are not considered in open literatures. In this article, we propose a traffic-aware channel and backoff window size allocation (TCBA) scheme to improve network capacity and latency. Moreover, a statistical latency-aware network model is designed to derive the closed forms of the optimum packet transmission probability and maximum number of LoRa devices supported. The performance results validate that the theoretical analysis approaches the simulated one. Moreover, in both simulated and experimental results, our proposed TCBA scheme is capable of supporting massive LoRa connections achieving the highest throughput and the lowest end-to-end latency compared to other schemes in existing literatures.

Visual acuity (VA) measurement is utilized to test a subject's acuteness of vision. Conventional VA measurement requires a physician's assistance to ask a subject to speak out or wave a hand in response to the direction of an optotype. To avoid this repetitive testing procedure, different types of automatic VA tests have been developed in recent years by adopting contact-based responses, such as pushing buttons or keyboards on a device. However, contact-based testing is not as intuitive as speaking or waving hands, and it may distract the subjects from concentrating on the VA test. Moreover, problems related to hygiene may arise if all the subjects operate on the same testing device. To overcome these problems, we propose an intelligent VA estimation (iVAE) system for automatic VA measurements that assists the subject to respond in an intuitive, noncontact manner. VA estimation algorithms using maximum likelihood (VAML) are developed to automatically estimate the subject's vision by compromising between a prespecified logistic function and a machine-learning technique. The neural-network model adapts human learning behavior to consider the accuracy of recognizing the optotype as well as the reaction time of the subject. Furthermore, a velocity-based hand motion recognition algorithm is adopted to classify hand motion data, collected by a sensing device, into one of the four optotype directions. Realistic experiments show that the proposed iVAE system outperforms the conventional line-by-line testing method as it is approximately ten times faster in testing trials while achieving a logarithm of the minimum angle of resolution error of less than 0.2. We believe that our proposed system provides a method for accurate and fast noncontact automatic VA testing.

In this paper, we conceive the first work of optimal functional split mode (FSM) selection for striking a balance between enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (uRLLC) in a cloudradio access network (C-RAN) for 5G new radio (NR). As a benefit of processing sharing (PS) and of coordinated multipoint transmission (CoMP) techniques, the user equipment (UE) can be served with more flexible transmission and sustaining eMBB/uRLLC service quality. Therefore, we aim at minimizing delay-outage probability (DOP) for uRLLC services by guaranteeing the quality of eMBB traffic. By employing Newton's method and convexity property, we propose an optimal FSM decision (OFD) scheme to achieve the optimal centralized/distributed control mode (CM/DM) assignment. Simulation results demonstrate the convergence and optimality of the proposed OFD algorithm compared to different parameters and schemes.

This paper is the first work to investigate power allocation design for multi-numerology based 5G new radio (NR) networks. With consideration of inter-numerology interference (INI) caused by the non-orthogonality between different numerologies, the objective function of considered optimization problem is to maximize total effective capacity (EC) under transmission power constraint. A geometry programming based power allocation (GPA) algorithm is proposed, which firstly tackles the non-convexity induced by INI in the multi-numerology systems via lower bound estimation and variable transformation techniques. Power allocation solution is then obtained by Lagrangian dual method. Simulation results reveal the influence of INI on power allocation result and demonstrate the superiority of proposed GPA algorithm based on its performance on total EC.

Millimeter wave (mmWave) is capable of supporting ultra-high system performance due to its spatial diversity from directional transmission techniques in fifth generation (5G) wireless networks. Improved by the beamforming, mmWave could be applied to potentially achieve more accurate indoor positioning. Due to impermeability and high path loss of mmWave, most of transmission is operated under line-of-sight (LoS) conditions. In this paper, we propose beam-based midline intersection positioning (BMIP) and advanced beam scaling positioning (ABSP) for indoor positioning. Two mmWave access points (APs) are employed for performing positioning under LoS conditions. The user equipment (UE) will receive downlink information of angle-of-departure (AoD) and corresponding signal-to-noise-ratio (SNR) values from two APs, respectively. We have evaluated our proposed BMIP and ABSP indoor positioning algorithms via both mmWave ray-tracing based simulation and experimental implementation using commercialized 60 GHz transceivers. The results demonstrate that our proposed positioning algorithms could achieve the centimeter-level estimation errors.

Recently, many researches are focusing on millimeter wave (mmWave) due to its large bandwidth resources. In the next 5G generation, wireless multi-gigabit (WiGig) is developed based on IEEE 802.11ad/ay standards at unlicensed mmWave bands for providing extremely high throughput. However, mmWave has high propagation loss due to high frequency transmission properties. Beamforming (BF) technique is adopted to solve this problem, and therefore, we have to perform beam training before data transmission. In the standard, the conventional method for beam training is exhaustive beam search (EBS) which takes too much time so that the data transmission time will decrease. On the other hand, blocked environments may severely degrade WiGig beam training performance, and most of existing algorithms do not consider this issue. Recently, machine and deep learning have been widely used in the wireless communication field. We propose a learning-based beam training (LBT) to simultaneously learn about wireless environments and beam training candidates. We select simplified neural network (NN) model to achieve lower computation overhead. To further refine learning information, we propose two enhanced algorithm, expanded LBT (LBT-E) and history-aided expanded LBT(LBT-HE). LBT-E aims to tackle unexpected slight deviation and LBT-HE make use of historical information to improve beam matching accuracy with acceptable latency. In our simulation, our proposed learning-based schemes achieve much higher throughput compared to EBS and algorithms in existing literatures.

In this paper, a quality of experience (QoE)- oriented beam management and admission control (AC) problem is investigated with consideration of time-varying channels and delay requirement for different functional split (FS) millimeter (mm)-wave fronthaul networks. The original problem aims to maximize time-averaged QoE of distributed units (DUs) with co-existence of short-term and long-term constraints, which makes the considered problem intractable. By leveraging Lyapunov optimization technique, the formulated problem is converted into a series of AC and beam allocation (BA) problems. Two AC and BA (ACBA) algorithms are proposed to solve the converted problem based on whether it is implemented at central unit (CU-ACBA) or DU (DU-ACBA). Simulation results demonstrate technical insights and applicability of the proposed algorithms by indicating the appropriate scenario for each FS option (FSO).

Present detection can remotely detect whether a person appears in specific scene. Channel state information (CSI) can provide high precision environment detection by its characteristic, and this technology can often be adopted for indoor localization or presence detection nowadays. We propose a device-free multiple presence detection system by using particular preprocessing method for our system, and a two-stage learning system combining convolutional denoising autoencoder (CDAE) and neural network (NN) to classify different cases of presence detection. The first stage contains several one-dimensional convolutional hidden layers which can denoise and reduce the dimension of data. The second stage classifies the cases for the purpose of presence detection. The proposed system can detect the presence of multiple persons at hotspots with high estimation accuracy.

Unstable drone landing scenarios have caused severe damage to expensive drone-based devices. It is required to provide precision altitude information of the drone during its landing maneuvers. In this paper, we propose a device-free wireless localization algorithm, by adopting channel state information (CSI) to assist drone landing. By adopting our proposed system, the drone will not need to carry any additional sensing devices for precise positioning. The commodity Wi-Fi access points only need to be installed near the landing nest of the drone, which means that multiple drones can use the same set of landing systems. Experimental results show that our proposed system can provide centimeter localization precision for drone landing applications. With the device-free feature, system resources can be effectively utilized and costs are greatly reduced.

Millimeter wave (mmWave) offers Gbps-level wireless services thanks to its huge spectrum and spatial utilization in the next fifth-generation (5G) wireless networks. Due to attenuations of signal loss, beam-based directional transmission is thus significant to overcome physical disadvantages. For multiuser service demands, the base station is capable of transmitting multiple high-gained beams via the massive antenna deployment. To simultaneously support a large number of users in a hotspot with finite beams, clustering is needed which several user groups are scheduled for each beam. To mitigate inter-beam interferences, we partition the original non-convex and nonlinear problem into two parts and propose the orthogonal beam clustering (OBC) and iterative power allocation (IPA) schemes through gradient descent method. Simulation results demonstrate that the groups with larger beam gains tend to be assigned with more power in order to provide higher rate. It also achieves better performance with increments of the orthogonal beam resources. Our proposed scheme can supports Gbps-level per user throughput which is twice higher compared to existing literatures.

Long range wide area networks (LoRaWAN) is one of the novel specification for the applications of Internet-of-Things. Recent research focused on the propagation model and coverage discussion based on experimental measurement. In this paper, power- class analytical model is firstly proposed to model the operations of LoRaWAN end-devices for throughput and power consumption. Based on this model, the energy efficient class-selection algorithm (EECA) is proposed to achieve the optimal energy efficiency with the consideration of power consumption on end-devices by the suitable class selection scheme. A system level simulation has been conducted for performance evaluation. The simulation results provide the guideline for system parameters configuration and class selection on each end-devices under different scenarios.

Transmission policy of the hybrid energy model allows maximizing energy utilization of small cells in the wireless system. One of the key challenges in maximization of energy utilization is to minimize the grid power and energy wastage of green harvested energy (GHE) sources simultaneously. In this paper, unlike existing transmission policy solution which focuses only on one highlighted objective, we proposed multi-objective model checking for Markov decision process (MOMC-MDP) with linear temporal logic (LTL) to find the best feasible solution for packet transmissions in each time slot. Based on the MOMC-MDP for different battery status, the optimal transmission policy of small cells is obtained in order to deliver the highest harvest energy sources and guarantee the quality of service (QoS) demands. Numerical results show that the MOMC- MDP scheme can ensure to efficiently use the limited energy of battery while maximizing green energy utilization in real-time transmission.

In this paper, we study joint interference cancellation and resource allocation for full-duplex (FD) cloud radio access networks (C-RANs) with antenna correlation. The target is to maximize the capacity of downlink (DL) user equipments (UEs) with guaranteed quality of service (QoS) of uplink (UL) UEs under the constraints of DL and UL transmit power. Intractability of the considered problem involves non-convexity and coupling between postcoding and remote radio head (RRH) selection. To deal with the high coupling between system variables, an estimation-free self-interference (SI) cancellation (EFSC) scheme with merit of significantly reducing signaling overhead for channel estimation is proposed. The reduced signaling overhead is also derived in closed-form expression. Furthermore, we propose a generalized Bender's decomposition-based resource allocation (GRA) algorithm, which separates the continuous and discrete variables to solve the optimization problem. With the design of a flexible utility function, the tradeoff between DL capacity and co-channel interference (CCI) can be achieved. Moreover, we identify the scenario in which antenna correlation will be beneficial for both the UL and DL communications in FD C-RAN with the implementation of EFSC scheme. The effectiveness of the proposed methods and theorems are verified via simulation results.

Non-orthogonal multiple access (NOMA) has been envisioned as a useful component of fifth generation (5G) mobile networks. As imperfect channel state information (CSI) due to channel estimation errors poses problems for most wireless receivers, it presents even greater challenges in successive interference cancellation (SIC) reception of NOMA signals. We present a novel approach to the multi-user detection problem by exploiting the important constraints of forward error correction (FEC) code word. We devise our new receiver based on the minimum output energy (MOE) criterion while preserving a distortionless response to the user equipment (UE) of interest. In particular, we efficiently adopt the UE signatures presented by the FEC channel codes under distinct permutations to separate desired signals of interest from interfering UEs. We formulate our receiver optimization into a quadratic-programming problem anchored with a set of code constraints. Our simulations demonstrate that the proposed code-anchored quadratic programming (CQP) receiver can accurately improve SIC performance and provide robustness to CSI errors better than other legacy schemes.

Wireless indoor localization technique has attracted wide attention recently. Fingerprint (FP) based method with received signal strength indicator (RSSI) is a popular approach due to easy implementation and robustness. Nowadays, fine-grained indoor spot localization resort to channel state information (CSI) owing to rich information property of CSI. However, due to a higher dimension of CSI compared to RSSI, CSI-based FP method requires higher storage and communication overhead, which is not suitable for most scenarios. In this paper, we propose a novel refined autoencoder-based CSI hidden feature extraction for indoor spot localization (RACHEL). Based on the concept of FP, we first introduce an autoencoder (AE) for the dimension reduction and feature discrimination. A low dimensional hidden feature of trained AE model is saved as FP database in the off-line stage. For indoor spot localization problems, users position is assumed to be close to one of the reference points. Therefore, CSI transforms to hidden feature space and users location is estimated by the nearest-neighbor algorithm in the on-line stage. Furthermore, to enhance the performance, instinctive AE is modified by considering corruption from time-varying environment and sensitivity between the hidden layer and input layer. Performance evaluations demonstrate that the proposed RACHEL can achieve 97.8% spot classification accuracy and yield a spaced savings of 94.3%.

MmWave in the unlicensed 60GHz band is highly discussed due to the rapid growth of wireless communication services. The utilization of microwave spectrum below 10 GHz is almost reaching its limit. Beamforming (BF) has become an essential technique to compensate the high path loss phenomenon of mmWave. IEEE 802.11ad and 802.11ay standardization task group institute directional multi-gigabit (DMG) wireless local area networks (WLANs) supporting mmWave techniques. With wireless devices explosively increasing, the limited number of BF slots in conventional DMG protocols leads to high packet collision probability, which potentially degrades the system performance. Most of the existing research does not take the multi-user BF contention problem into consideration. In this paper, we design a non-slotted association beamforming training (A-BFT) frame structure for multi-users without separating A-BFT slots in order to alleviate packet collisions due to the shortage of contention and training frames. This new proposed structure allows collisions with useful information left in remaining non-collided slots or frames in order to leverage the insufficiency of BF training for multiple devices. Furthermore, in order to further reduce BF collision probability, we propose two multiuser beamforming training mechanisms, including the time-based distributed coordination function (TDCF) and time-based beam-collision avoidance (TBCA) schemes. Simulation results demonstrate that our new proposed frame structure along with proposed TDCF and TBCA mechanisms outperforms 802.11ad protocol.

Millimeter wave (mmWave) technology provides multi-Gbps services thanks to huge spectrum usage. IEEE 802.11ad/ay standardizes the next generation wireless local area networks (WLANs) for high data rate transmissions at 60 GHz band. Directional beamforming (BF) transmissions overcome the difficulty of high signal attenuation. In addition, a beam sector based scheme for BF training is taken by 802.11ad/ay to tackle sophisticated indoor environments. However, conventional exhaustive beam search (EBS) takes much time and some literatures try to design to reduce training latency. Also, most of papers do not consider about the mobility effect of moving devices, which deteriorates the beam matching outcomes. With the consideration of beam matching accuracy and mobility effects corresponding to historically observed transmitted beams, we propose the mobility-aware fast beam training (MFBT) algorithm. Simulation results show how mobility affects the system in terms of latency and throughput. There are trade-offs when operating with different beamwidth and matching accuracy requirements. Moreover, our proposed algorithm achieves lower latency and higher throughput than other existing beam training schemes.

In this paper, licensed-assisted access (LAA) for dual-band small cell network is investigated. A framework for LTE coexisting with incumbent unlicensed systems is firstly introduced. The analytical expressions are also derived to characterize the transmission strategies for both LTE and wireless fidelity (WiFi) systems over unlicensed spectrum. Based on the natures provided by analytical models, a discontinuous transmission (DTX)-enabled LAA (DLA) mechanism is proposed to maximize energy efficiency with quality- of-service (QoS) support. Also, a dual access-based opportunistic traffic offloading (DOTO) scheme is developed to determine whether traffic offloading or channel aggregation, i.e., WiFi or LAA, shall be adopted in unlicensed spectrum. Numerical results demonstrate that the proposed DLA mechanism can dynamically adjust DTX pattern to opportunistic share unlicensed spectrum or achieve energy conservation from the perspective of energy efficiency. The tradeoff between traffic offloading and channel aggregation over unlicensed spectrum is also provided by evaluating the performance of proposed DOTO scheme.

The narrowband Internet-of Things (NB-IoT) standard has been developed to provide low data rate requirement for 5G IoT systems. To prolong the battery lifetime of user equipment (UE), the extend discontinuous reception (eDRX) and power saving mode (PSM) are specified in the standard for power efficient purpose. However, it is non- trivial to configure suitable power-saving parameters to achieve power conservation while reducing transmission latency. In this paper, we proposed a QoS-guaranteed power-saving configuration prediction (QPCP) scheme to effectively reduce system power consumption with guaranteed packet delay constraints. By adopting partially observable Markov decision process (POMDP) and scheduling of base station (BS), a feasible set of power-saving parameters can be obtained. Simulation results show that the proposed QPCP scheme outperforms other existing methods for power-saving of both UE and BS with guaranteed QoS constraints.

In this paper, the spectrum sharing in both licensed and unlicensed bands under heterogeneous networks (HetNets) is investigated. A frame architecture of licensed-assisted access (LAA) in unlicensed bands is introduced by analyzing its statistical behavior of channel access opportunity. A multi-objective optimization problem is then designed for small cell to enhance energy efficiency by jointly maximizing the achievable sum-rate and minimizing the power consumption. The proposed discontinuous transmission (DTX)-enabled LAA (DLA) mechanism can be employed to find the Pareto optimal solution of resource allocation policy while guaranteeing the quality of service (QoS) of incumbent systems in both licensed and unlicensed bands. Numerical results show that the proposed DLA mechanism can enhance energy efficiency through dynamically allocating radio resources and adjusting system parameters to opportunistic share the licensed and unlicensed resources.

In this paper, we propose a Particle-based Window Rotation and Scaling (PWRS) algorithm, which is a multi-stage system that can perceive hand size and rotating angle using a single camera. There are three stages of operation in the PWRS scheme, including window-locating stage, window-scaling stage and window-rotating stage, that are adopted to recognize the location, size and angle of hand motion, respectively. Each stage employs histogram of oriented gradients, support vector machine, and particle filter to detect and track the hand window. Unlike traditional multi-stage system which requires to detect and then remove non-hand regions case-by-case, the PWRS scheme can preserve similar characteristics at each stage and predict the results propagated from other stages by cross-stage propagation method. This architecture allows each stage to focus on its own target characteristics so as to detect and track in a diversity- reduced space. Experimental results show that the proposed PWRS algorithm can effectively provide satisfactory hand motion recognition and tracking for real-time applications.

Scheduler plays an important role for Long Term Evolution-Advanced (LTE-A) system to achieve high throughput performance. Existing research work on scheduler design did not fully consider non-full buffer capacity at the base station (BS) owing to the fluctuation on data volume. In this paper, we proposed a quality-of-service (QoS) guaranteed channel-aware (QGCA) scheduler with the consideration of BS buffer status to improve system capacity. The proposed QGCA scheme fully considers the required components for LTE-A scheduler, including resource grouping along with modulation and coding scheme. Moreover, we proposed an adaptively channel-aware resource grouping (CARG) method within the QGCA scheduler for improving the performance of existing resource grouping schemes. Numerical results show that our proposed QGCA scheduler and CARG scheme outperform conventional methods, especially under non-full buffer and delay-sensitive VoIP traffic.

Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. In this paper, we proposed a automatic method to extract useful information from indoor map as spatial skeleton database (SSD). Based on conventional probabilistic fingerprinting technique and particle filter tracking algorithm, we also proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) which use SSD to construct realistic transition model. According to the experiment result, the whole system consists of SSD, S-DFD and S-PT called spatial skeleton-enhanced location tracking for indoor localization (SELT) can achieve accurate location estimation.

Location estimation has received wide attention due to the emerging demand for location-based services (LBSs) in indoor environments. Although positioning algorithms have been rapidly developed, the positioning accuracy has not reached the requirement of indoor LBSs. Indoor positioning methods based on the existing communication systems such as Wi-Fi or Bluetooth have the advantage of lower cost and higher penetration rates, which can provide a sufficient number of signal sources. Unlike the global positioning system where the satellites are well- deployed to provide four or more signal sources and well-conditioned geometric for outdoor devices, the critical limits of indoor positioning are the insufficient signal sources and disunified deployment for access points (APs). To address the problem, we propose an automatic hybrid AP deployment (AHAD) algorithm to provide optimal locations and required numbers of both WiFi APs and BLE APs for achieving higher location estimation accuracy. With the adoption of genetic algorithm, the AHAD scheme can maintain satisfactory Wi-Fi communication quality and fulfill user's budget for AP deployment. Furthermore, a hybrid indoor positioning (HIP) scheme is also proposed based on the combination of Wi-Fi fingerprinting and BLE proximity. Experimental results show that the proposed AHAD algorithm can provide better location estimation accuracy compared to conventional AP deployment based on user instinct.

In this paper, an energy efficient resource allocation is investigated to enhance the network performance of cellular networks. Specifically, the small cells (SCs) using both green and on-grid energy and the power splitter-enabled wireless charging for user equipments (UEs) are considered. A quality-of-service (QoS)-constrained problem is then designed to maximize energy efficiency (EE) through the joint strategies of power allocation, resource block (RB) assignment, and power splitting ratio adjustment. By exploiting some mathematical transformations, the non-convex optimization problem can be solved by adopting the proposed joint wireless charging and hybrid power based resource allocation (JWHRA) algorithm. The efficiency of proposed algorithm is validated via simulation. Numerical results show that the proposed JWHRA scheme can provide higher EE with its joint design on wireless charging and hybrid energy source.

To satisfy future explosive demands of mobile traffic, millimeter wave (mmWave) technology has been widely considered in the next generation wireless networks thanks to its large spectrum and spatial resources. MmWave Hybrid beamforming (HBF) with multi-input multi-output (MIMO) system is a critical technique to support multi-beam operation and reduce the cost and complexity. In this paper, we define inter-user interference in the mainlobe and the sidelobe. With the consideration of quality of service (QoS) requirements, we design a mainlobe interference avoidance (MIA) scheduling algorithm to maximize data throughput under the existence of inter-user interferences. Our proposed scheme allocates system resources by jointly considering not only the subband resources but also the antenna beamwidth and beam direction. Simulation results demonstrate that the proposed scheme outperforms existing heuristic algorithms both in the uniform and hotspot scenarios.

A great amount of research has devoted to cognitive radio (CR) in recent years in order to improve spectrum efficiency. In decentralized CR networks, it is not realistic for CR users to sense entire spectrum in practice due to hardware limitations. Consequently, the partially observable Markov decision process (POMDP) can be utilized to provide CR users with sufficient information in partially observable environments. Existing POMDP-based protocols adopt channel aggregation techniques in order to improve spectrum opportunities and system performance. However, the required time for channel sensing is neglected which can result in large sensing time overhead and spectrum opportunity loss in realistic environments. In this paper, based on partially observable channel state with the consideration of sensing overhead, the stochastic multiple channel sensing (SMCS) protocol is proposed to conduct optimal channel selection for maximizing the aggregated throughput of CR users. By adopting the proposed SMCS protocol, CR users can highly accommodate themselves to rapidly varying environment based on the dynamically adjustable channel sensing strategy. Moreover, the channel sensing problem is further extended to imperfect sensing scenario, which can severely degrade system throughput due to packet collision between primary users (PUs) and CR users. Consequently, in addition to channel selection, it is required for CR users to determine the sensing time length in order to address the collision problem. The two-phase SMCS (TSMCS) protocol is proposed to maximize the aggregated throughput of CR users while still fulfilling PUs' quality-of-service (QoS) requirements. Numerical results show that the proposed SMCS and TSMCS protocols can effectively maximize the aggregated throughput for decentralized CR networks.

The 3GPP long term evolution (LTE) system is developed to enhance mobile services from the former 3G systems. In order to prolong the battery life time of mobile devices, the discontinuous reception (DRX) scheme is specified in the LTE standard to reduce the power consumption of user equipment. Existing analytical models did not comprehensively consider all essential sleep mode behaviors. Therefore, a performance analysis, including major DRX parameters is proposed in this paper for sleep mode operation. The correctness of proposed model is validated and the improvement can be observed via simulations. Furthermore, according to proposed analytical model, the DRX parameters can significantly influence the power-saving efficiency and quality-of-service (QoS) of LTE system. Hence, a traffic-based DRX cycles adjustment (TDCA) scheme is proposed to adaptively adjust the sleep cycles to balance the sleep ratio and mean packet delay based on traffic estimation. A partially observable Markov decision process is employed to conjecture the present traffic status. The policy for selecting DRX parameters can be constructed-based the evaluation metrics in TDCA scheme. Simulation results show that the proposed TDCA scheme can enhance the energy-saving efficiency while the QoS constraint is still satisfied.

This paper investigates a cloud radio access network (C-RAN) architecture for future wireless network, which focuses on centralized processing for spatially distributed remote radio heads (RRHs). Our main goal is to promote an energy efficient C-RAN under the consideration of multiple access interference (MAI) and imperfect channel state information at the transmitter (CSIT) via outage-aware resource allocation. The closed-form expression of fading-induced outage probability for each transceiver pair will be established by analyzing the statistical property of signal-to-interference-plus-noise. In the course of finding feasible solutions to enhance energy efficiency, the optimization procedures named C-RAN-based energy efficient power allocation (CEEPA) and low-complexity CEEPA are proposed. As the number of RRHs grows to infinity, the deterministic equivalents of performance metrics can be derived by applying recent results from random matrix theorem, which lead to an efficient way to obtain the asymptotic-optimal resource allocation policy. Also, the regularized zero-forcing precoding technique is adopted to mitigate MAI and can tackle the impact of imperfect CSIT thanks to the derivation of deterministic equivalents. Numerical simulations show that the proposed resource allocation schemes can provide better energy efficiency and the accuracy of asymptotic expressions is also verified. Furthermore, the merit of a distributed antenna system is demonstrated through the comparison with centralized antenna system.

Full-duplex (FD) wireless communication systems have become an important research issue since it can increase spectral efficiency through higher spectrum utilization. The main challenge in achieving FD is the cancellation of strong interference at the received interface from the transmitted interface of a node, namely, self-interference (SI). Therefore, a technique for SI cancellation using an oblique projection-based postcoding (OPP) scheme is proposed in this paper. The major merit of the proposed OPP method is the non-necessity of SI channel estimation, which can reduce the signaling overhead and can be adopted even when the SI channel distribution is unknown. Simulation results show that the proposed OPP scheme can achieve significant performance gain in terms of signal-to-interference-plus-noise ratio (SINR) compared to conventional method.

Recently, the centralized management in small cell (SC) networks enabled by the existence of a central controller and backhaul links is developed to meet the growing data demand. The effect of load-balancing under the considerations of limited capacity backhaul and co-channel interference is investigated in this paper. Our main goal is to maximize energy efficiency (EE) in SC network through a joint decision strategy consists of user association and radio resource allocation with quality-of-service (QoS) support. To tackle this mixed combinatorial optimization problem, a load-balanced user association and radio resource allocation (LB-UARA) mechanism based on the quantum-behaved particle swarm optimization algorithm is proposed. Simulation results demonstrate that the objectives of QoS satisfaction and energy conservation can be realized via load-balancing procedure in our proposed LB-UARA mechanism.

For long term evolution-advanced (LTE-A) system, carrier aggregation (CA) allows LTE evolved Node B (eNB) and user equipment (UE) to access idle frequency spectrum to serve continuously growing traffic demands. To efficiently enhance network throughput, the concept of cognitive radio (CR) is adopted for LTE-A system to dynamically access opportunistic spectrum not occupied by primary system. Under the consideration of limited feedback system, the partially observable Markov decision process (POMDP) is applied in this paper to estimate the channel occupancy information (COI) on shared spectrum by partially sensing the frequency carriers. A POMDP-based spectrum sharing (POSS) scheme is then proposed to realize throughput maximization via the designs of contention mechanism and resource allocation policy according to the partially observable COI, channel state information, and number of contending cells in the network. Compared with the exhaustive search, the proposed approach can achieve optimal performance gain with lower computational complexity, which is proved to be a linear time algorithm. Numerical results illustrate that the proposed POSS protocol can effectively improve system throughput on shared spectrum for the LTE-A networks.

The IEEE 802.16 standard is developed to support services with high data rate and high mobility for the next generation broadband wireless access networks. There are existing sleep mode operations specified in the series of IEEE 802.16 standards in order to provide energy conservation for the mobile devices. In this paper, the analytical models for sleep mode operations of both the IEEE 802.16e and IEEE 802.16m standards are proposed, respectively. The effects of both downlink and uplink traffic are properly considered in the proposed models. Simulations are performed in order to validate the effectiveness of proposed system models. However, according to the performance evaluation for IEEE 802.16e/m system, inefficiency is observed which can be resulted from specific mechanisms within the sleep mode operations, such as frequent state transitions, under-utilized listening windows, and the adoption of binary-exponential growth of sleep window size. A POMDP-based sleep window determination (PSWD) approach is proposed in this paper, which stochastically determines the adequate length of each sleep window according to the traffic pattern. Based on the estimated traffic state, an energy cost-based sleep window determination policy is provided within the PSWD approach in consideration of tolerable network delays. Simulation results show that the proposed PSWD approach outperforms conventional IEEE 802.16e/m power-saving mechanisms in terms of energy conservation while the delay constraints are also satisfied corresponding to various traffic demands.

The adoption of femtocell networks is considered as a promising solution to resolve the poor received signal strength problem experienced by the user equipment (UE) located in the coverage hole or in indoor environment. The architecture of femtocell networks, which adopt cognitive radio (CR), centralized controlled by femtocell gateway (F-GW) is further proposed to enhance the system performance by accessing more licensed spectrums. However, more deployed femtocells lead to additional energy consumption and it has become a crucial challenge for mobile operators. In this paper, the spectrum selection with limited number of antennas (SSLNA) is proposed to derive the target spectrum with consideration of hardware limitation of both femtocell access points (FAPs) and UEs. Based on the spectrum selection policies, the energy-efficient joint resource block (RB) and power allocation (EJRPA) scheme is proposed to improve the system energy efficiency. To avoid large amount of communicational load and complex computation for both F-GW and FAPs, the energy efficiency optimization problem is transformed into a two-layer problem solved respectively by F- GW and FAPs when the EJRPA is adopted. Simulation results show that better system energy efficiency can be achieved by adopting the proposed schemes.

The deployment of small cells with carrier aggregation (CA) technique is a significant feature of next-generation cellular networks. The potential benefits of dense base stations (BSs) deployment are provided by using proper interference management mechanisms. Meanwhile, the user equipments (UEs) can simultaneously access multiple component carriers (CCs) to meet the dramatically increased traffic demand by means of CA. In this paper, an optimization problem consists of UE-BS association, CC configuration, frequency resources scheduling, and power allocation with adaptive modulation schemes is investigated to enhance energy efficiency, focusing on the quality of service (QoS)-aware CA. To obtain the near optimal solutions as well as the consideration of computational complexity, a cross entropy- based algorithm (CEA) is proposed to jointly solve the combinatorial problem. Compared to the traditional heuristic algorithms, the merits of proposed scheme can be observed via simulation results.

With the widespread of smartphones, people can easily figure out where they are and enjoy other advanced services like searching nearby restaurant information or checking bus arrival time. Indoor positioning becomes a popular issue for location- based services used in shopping malls, hospitals, or largescale buildings. Contrast with spacious surroundings of outdoor, indoor environment is filled with obstacles and moving people, which impose great challenges to provide precise estimation of indoor positioning. This paper proposes Wi-Fi fingerprinting technique using received signal strength with consideration of map information to effectively eliminate unreasonable estimation outcomes. The proposed area estimation (AE) algorithms calculate the similarity of each area in the entire region to increase accuracy of distinguishing which area the user locates. Moreover, the shortest path with adjacent recognition (SPAR) algorithm further utilizes the concept of Dijkstra's shortest path algorithm and the previous location information to predict user's position. Experimental results show that the proposed AE with SPAR algorithms can provide better area estimation compared to conventional scheme.

In this paper, the cloud radio access network (C- RAN) is considered to extend the transmission coverage via the distributed deployment of large- scale remote radio units (RRUs). However, this type of structure can induce considerable computational loadings due to the centralized management mechanisms. To reduce the complexity incurred in the C-RAN architecture, the clusterization technique is designed to categorize those RRUs into several groups. For the purpose of enhancing energy efficiency (EE) as well as the consideration of computational complexity, the joint clusterization and power allocation schemes are proposed to obtain the better tradeoff under the quality-of-service (QoS) requirement for each user equipment (UE). Simulation results show that the proposed algorithms can provide better performance gain than the existing method.

In recent years, wireless positioning technologies have been widely integrated into new developments of telecom systems and services. As path loss characteristics are comprehensively investigated and adopted by international telecommunication union (ITU), received signal strength (RSS) is utilized as a type of measurement and can be properly transformed to obtain distance information. With the models and parameters addressed in the technical reports of 3GPP, the RSS-based distance and location estimation (RDLE) algorithm is proposed for mixed line-of-sight (LOS)/non-line-of-sight (NLOS) in LTE cellular networks. The proposed RDLE algorithm consists of a distance estimation method and a location estimation scheme. The particle-based distance estimation (PDE) method is proposed to estimate distances with RSS measurements under various environments and mixed sight conditions. Moreover, the geometry-improved location estimation (GILE) algorithm is proposed to explore geometric relationships between base stations (BSs) and mobile station (MS). A geometric constraint based on range differences which is related to time-difference-of-arrival (TDOA) information is constructed to confine MS's location estimation within a specific closed region. Moreover, a geometric reformation derived from minimizing the geometric dilution of precision (GDOP) is utilized as an assistance to increase the degree of freedom in space domain against the errors in location estimations. The theoretic Cramer-Rao lower bound is also derived as a benchmark to evaluate the precisions of positioning algorithms with range differences. The proposed RDLE algorithm is perceived to outperform other existing localization methods, especially under poor network topologies and insufficient measurement inputs.

The IEEE 1609.4 standard has been proposed to provide multi-channel operations in wireless access for vehicular environments (WAVE), where all channels are periodically synchronized into control and service intervals. Communication device in each vehicle will stay at the control channel for negotiation and contention during the control interval, and thereafter switch to one of the service channels for data transmission in the service interval. In this paper, based on the concept of cognitive radio (CR), the vehicles are categorized into primary providers (PPs) that intend to transmit safety-related messages and secondary providers (SPs) with non-safety information to be delivered. The prioritized optimal channel allocation (POCA) approaches are proposed to improve channel utilization of IEEE 1609.4 standard for multi-channel vehicular networks. Prioritized channel access is analyzed in the POCA schemes in order to increase the transmission opportunity of PPs. Moreover, depending on whether the CR network is distributed or centralized, the optimal channel-hopping sequence and optimal channel allocation is assigned for SPs based on dynamic programming and linear programming technique, respectively. These schemes are designed to consider optimal load balance between both channel availability and channel utilization within the throughput constraints of PPs. With the adoption of proposed POCA schemes, simulation results show that maximum throughput of SPs can be achieved with guaranteed quality-of-service requirement for PPs.

In recent years, wireless positioning technologies have been widely considered in the developments of new telecom systems and services. The increasing requirements of location-based services (LBS) in many practical applications have promoted the investigations of location estimation methods. With the system models and network structures addressed in the technical reports of 3GPP, the geometry-improved location estimation (GILE) algorithm is proposed to explore geometric relationships between base stations (BSs) and mobile station (MS) in order to improve the accuracy and precision of location estimations. A geometric constraint based on the network topology and time-difference-of-arrival (TDOA) measurements is constructed to confine MS's location estimation within a specific closed region. Moreover, a geometric reformation derived from minimizing the geometric dilution of precision (GDOP) is utilized as an assistance to increase the degree of freedom in space domain against the errors from TDOA measurements to location estimations. According to the performance evaluations, proposed GILE algorithm is perceived to outperform other existing localization methods, especially under poor network topologies and insufficient measurement inputs.

In 3GPP Release-12 specification, the adoption of small cells in long term evolution (LTE) networks is considered as a promising solution to meet the growing traffic demand for mobile data usage. However, energy consumption becomes a crucial challenge for network operators to deploy more small cells in heterogeneous networks in order to fulfill the requirement for achieving high data rates. In this paper, an advanced signaling mechanism has been proposed to practically acquire global channel state information (CSI) between network components. Based on the signaling scheme, an energy-efficient cell selection and resource allocation (ECR) algorithm is proposed to increase system energy efficiency by handovering UEs from light-loaded base station to nearby cells. Some active cells can therefore be switched off to reduce total energy consumption. Simulation results show that better energy efficiency can be achieved by adopting the proposed signaling and ECR schemes.

In this paper, a novel multi-carrier and multi-antenna for a multi-tier cellular network is presented for Long-Term Evolution Advanced (LTE-A) heterogeneous networks (Het-Nets) with carrier aggregation enhancement. First, we investigate a system model of frequency domain load balancing-based inter-cell interference coordination (ICIC) to minimize the total interference, while satisfying the required quality of service constraints. To achieve the desired system performance, a heuristic solution by joint spectrum and antenna allocation is then proposed, which operates at the base station side to enable efficient interference management. The optimal deployment for component carriers and antennas at the serving base station is updated for specific time periods while passively receiving the system information transmitted from neighboring base stations. Furthermore, system level simulations are carried out to demonstrate how the throughput can be greatly improved by our solution, as compared to conventional methods such as the randomized and static ICIC approaches.

Due to the popularity of location-based services in environment with weak GPS signals, indoor location estimation problem have attracted more and more attention in recent years. Among several distance-related measurements, channel impulse response (CIR) reflects multi-path situation between the transmitter and receiver pair and is suitable to describe the characteristic of different positions. Note that CIR, which can be obtained from the inverse Fourier transform of channel frequency response in broadband wireless networks, is supported in most of the commercial standards. In this paper, a novel compressive sensing based location estimation using CIR measurements (CS-CIR) is proposed as well as fingerprinting algorithm. CIR information is collected from each reference point (RP) to access point (AP) and stored in the database. During the on-line stage, the mobile user measures CIR from the AP and compares measured CIR with those CIR values in the database. Note that user position is close to one of the RPs and user position vector is represented as a sparse vector. By applying compressive sensing theory, user position can be recovered by solving l1-minimization problem. Simulation result validate that the CS-CIR outperforms the K-nearest neighbour method using CIR measurements and conventional received signal strength based methods.

Visual acuity (VA) measurement is for a subject to test his/her acuteness of vision. Several kinds of automatic VA test are gradually developed and used in recent years. Without experimenter, the traditional way for a subject to speak out or wave a hand in response to the direction of optotype is then replaced mostly by the contact based response such as pushing buttons or keyboards on a device nowadays. However, the contact based response is not intuitive as speaking or waving hands, and it may distract subjects from concentrating on the test. To overcome these problems, we propose a hand motion recognition based visual acuity (HMRVA) measurement which keeps the advantage of automatic VA measurement, and also allows subject to respond in an intuitive contactless way. A velocity based hand motion recognition (V-HMR) algorithm is used to classify hand motion data collected by a sensing device into one of the four directions of optotypes. Based on the V-HMR scheme, a maximum likelihood based visual acuity (ML-VA) estimation algorithm is developed for VA estimation and is implemented on a tablet. According to the experimental results, we can conclude that the proposed HMRVA system achieve our goals to provide accurate and efficient automatic VA tests.

Augmented reality (AR) technology is mainly composed of feature extraction, feature points matching and 3D object drawing to impose virtual information onto the real camera. Hand tracking method is designed to realize interactive AR system to further enhance user experience (UX). However, feature extraction, feature points matching, and hand tracking all require high computational cost. To achieve real-time implementation on mobile, we proposed a Mobile Augmented ReAlity Interactive System based on cloud computing (MARAIS). Cloud side is responsible for heavy tasks that need sufficient computational capabilities, and device side performs sensing, detecting and displaying. Device/cloud architecture has been designed to maintain tolerable processing delay with minimum communication overhead. To validate the suitability for real-time interaction, MARAIS is combined with a picture book to realize a digital learning system. Implementation of interactive AR system based on cloud computing eases the requirement of data storage, memory, and computational power at device side.

Recently, promoting energy efficiency is an important research issue in the wireless communication system. This paper investigates the resource management problem with the regularized zero-forcing (RZF) precoding for the distributed large-scale multiple-input multiple-output cloud radio access network (DLS MIMO C-RAN) which consists of a large number of spatially distributed remote radio heads (RRHs). The challenges of this power allocation problem arise from the presence of both interference and imperfect channel state information at the transmitter (CSIT). Therefore, the C-RAN based power allocation schemes are designed to efficiently allocate the transmit power of each RRH. Moreover, the large random matrix theory is applied to derive the asymptotic expressions for large number of antennas. Simulation results show that the proposed schemes can provide better energy efficiency with the consideration of quality-of-service (QoS) support.

We investigate the problem of cooperative self-navigation (CSN) for multiple mobile sensors in the mixed line-of-sight (LOS) and nonline-of-sight (NLOS) environment based on measuring time-of-arrival (TOA) from the cooperative sensing. We first derive an optimized recursive Bayesian solution by adopting a multiple model sampling-based importance resampling particle filter for the development of CSN. It can accommodate nonlinear signal model and non-Gaussian position movement under different levels of channel knowledge. We also utilize a Rao-Blackwellization particle filter to split the original problem by tracking the channel condition with a grid-based filter and estimating the position with a particle filter. The CSN with position and channel tracking exhibits advantage over the noncooperative methods by utilizing additional cooperative measurements. It also shows improvement over the methods without channel tracking. Simulation results validate that both schemes can take the advantage of cooperative sensing and channel condition tracking in mixed LOS/NLOS environments, which motivates future research of cooperative gain for navigation and localization in a more general environment.

In recent years, femtocell plays an important role in wireless networks not only for its spectrum reuse but also for its low power consumption. However, there exist several critical issues that need to be investigated, especially for the interferences between the macrocell BSs (mBSs) and femtocell BSs (fBSs). The level of interference mainly depends on the access strategies of fBSs. Two major access policies are considered in femtocell network, including the closed access mode and open access mode. The closed access mode only permits authorized subscribers to utilize the fBS; while all users are allowed to connect to the fBS by adopting the open access mode. Closed access will intuitively be advantageous to the femtocell subscribers, however, interference from the fBS to mBS's users can become severe in the closed access mode than in open access mode. System performance of the entire heterogeneous network (HetNet) can be improved if fBS is operated in the open access mode. In order to relax the inflexible access strategies, hybrid access policy is considered in this paper which allows nonsubscribers to possess limited connections to the fBS. Two cell selection games for distinct scenarios are theoretically modeled to formulate the behaviors of nonsubscribers, and the existences of pure strategy Nash equilibria are also proven under feasible utility functions. From the perspectives of subscribers, HetNet system, and operator, numerical results suggest the adoption of hybrid access mode to provide higher flexibility for the performance enhancement.

The limitation on battery lifetime has been a critical issue for the advancement of mobile computing. Different types of power-saving techniques have been proposed in various fields. In order to provide feasible energy-conserving mechanisms for the mobile subscriber stations (MSSs), three power-saving types have been proposed for the IEEE 802.16e broadband wireless networks. However, these power-saving types are primarily targeting for the cases with a single connection between the base station (BS) and the MSS. With the existence of multiple connections, the power efficiency obtained by adopting the conventional scheduling algorithm can be severely degraded. In this paper, with the consideration of multiple connections and their quality-of-service (QoS) constraints, a frame aggregation-based power-saving scheduling (FAPS) algorithm is proposed to enhance the power efficiency by aggregating multiple under-utilized frames into fully-utilized ones. The optimality on the minimum number of listen frames in the proposed FAPS algorithm is also provided, and is further validated via the correctness proofs. Performance evaluation of proposed FAPS scheme is conducted and compared via simulations. Simulation results show that the power efficiency of FAPS algorithm outperforms the other existing protocols with tolerable frame delay.

Because of the scarcity of spectrum and energy resource, the problems about allocation of these resources become more and more important in recent years. However, inappropriate resource allocation may bring about high intercell interference which has a great effect on the performance of the entire system. Thus, in this paper, considering inter-cell interference, we provide the optimal formulation of the resource allocation problem and divide it into two sub-problems, including the component carrier (CC) selection problem and the power allocation problem. The proposed enhanced data rate component carrier selection (EDR-CCS) scheme not only reduces the intercell interference but also maximizes the UEs' data transmission rates. Regarding the power allocation problem, two schemes (i.e., geometry programming power allocation (GPPA) and constant interference power allocation (CIPA)) are proposed with the target of finding suboptimal solutions to the power allocation problem under the constraints of eNB's maximum transmission power and UEs' transmission rate requirement on the control channel of primary CC (PCC). Simulation results demonstrate that the data rate performance of EDR-CCS scheme is better than that of random CC selection scheme. Furthermore, GPPA scheme can achieve better performance than CIPA scheme since the GPPA scheme can accommodate the variations from inter-cell interference.

Location estimation and tracking for mobile stations (MSs) have attracted a significant amount of attention in recent years. In indoor environment where the signals from global positioning system (GPS) are either weak or blocked, signal sources from long term evolution advanced (LTE-A) system can be adopted to provide location estimation for MS. Based on the range signals from both macro base station (mBS) and femto BS (fBS) in LTE-A heterogeneous networks (HetNet), we propose femto-assisted location estimation (FALE) schemes to estimate MS's position especially for indoor environments with insufficient signal inputs from mBSs. Since fBSs are usually deployed by users in their residential or business buildings, the locations of these fBSs generally cannot be known exactly. We depict the imprecise fBSs' positions as belief information to investigate a Bayesian estimation method based on the time difference of arrival (TDOA) measurement, and utilize particle filtering technique to develop the FALE algorithm. Moreover, a simplified FALE (FALE-S) scheme is proposed to reduce the computation cost resulting from the particle filter in original FALE method. Performance evaluation is conducted based on the LTE-A HetNet environments. Compared to conventional scheme, simulation results show that the proposed FALE algorithms can provide better location estimation of MS through the assistance of fBSs.

Due to the lack of a centralized coordinator for wireless resource allocation, the design of medium access control (MAC) protocols is considered crucial for throughput enhancement in the wireless ad hoc networks. The receiver blocking problem, which has not been studied in most of the MAC protocol design, can lead to severe degradation on the throughput performance. In this paper, the multiple receiver transmission (MRT) and the fast NAV truncation (FNT) mechanisms are proposed to alleviate the receiver blocking problem without the adoption of additional control channels. The adaptive receiver transmission (ART) scheme is proposed to further enhance the throughput performance with dynamic adjustment of the selected receivers. Analytical model is also derived to validate the effectiveness of the proposed ART protocol. Simulations are performed to evaluate and compare the proposed three protocols with existing MAC schemes. It can be observed that the proposed ART protocol outperforms the other schemes by both alleviating the receiver blocking problem and enhancing the throughput performance for the wireless multihop ad hoc networks.

The design of wireless local area networks (WLANs) with enhanced throughput performance have attracted significant amounts of attention in recent years. Based on the IEEE 802.11n standard, frame aggregation is considered one of the major factors to improve the system performance of WLANs from the medium access control (MAC) perspective. In order to fulfill the requirements of high throughput performance, feasible design of automatic repeat request (ARQ) mechanisms becomes important for providing reliable data transmission. In this paper, two MAC-defined ARQ protocols are proposed to consider the effect from frame aggregation for the enhancement of network throughput. An aggregated selective repeat ARQ (ASR-ARQ) scheme is proposed which incorporates the selective repeat ARQ scheme with the consideration of frame aggregation. On the other hand, for worse channel quality, the aggregated hybrid ARQ (AH-ARQ) mechanism is proposed to further enhance the throughput performance by adopting the Reed-Solomon (RS) block code as forward error correction (FEC) scheme. Novel analytical models for both the ASR-ARQ and AH-ARQ protocols are established with the consideration of interfering wireless stations. Simulations are conducted to validate and compare the proposed ARQ mechanisms based on the service time distribution and system throughput. Numerical evaluations show that the proposed AH-ARQ protocol can outperform the other schemes under worse channel condition; while the ASR-ARQ scheme is superior to the other mechanisms under better channel condition.

Linear estimators have been extensively utilized for wireless location estimation for their simplicity and closed form property. In the paper, the class of linear estimator by introducing an additional variable, e.g., the well-adopted linear least squares (LLS) estimator, is discussed. There exists information loss from the linearization of location estimator to the nonlinear location estimation, which prevents the linear estimator from approaching the Cramér-Rao lower bound (CRLB). The linearized location estimation problem-based CRLB (L-CRLB) is derived in this paper to provide a portrayal that can fully characterize the behavior for this type of linearized location estimator. The relationships between the proposed L-CRLB and the conventional CRLB are obtained and theoretically proven in this paper. As suggested by the L-CRLB, higher estimation accuracy can be achieved if the mobile station (MS) is located inside the convex hull of the base stations (BSs) compared to the case that the MS is situated outside of the geometric layout. This result motivates the proposal of geometry-assisted localization (GAL) algorithm in order to consider the geometric effect associated with the linearization loss. Based on the initial estimation, the GAL algorithm fictitiously moves the BSs based on the L-CRLB criteria. Two different implementations, including the GAL with two-step least squares estimator (GAL-TSLS) and the GAL with Kalman filter (GAL-KF), are proposed to consider the situations with and without the adoption of MS's historical estimation. Simulation results show that the GAL-KF scheme can compensate the linearization loss and improve the performance of conventional location estimators.

The 3GPP long term evolution (LTE) standard is developed to enhance mobile services from the former 3G systems. In order to prolong the battery life of mobile device, a novel discontinuous reception (DRX) scheme is specified in the standard to reduce the power consumption of a user equipment. In this paper, a traffic-based DRX cycles adjustment (TDCA) scheme is proposed to enhance the energy saving performance based on existing DRX operation. A partially observable Markov decision process (POMDP) is employed to conjecture the present traffic status. Optimal policy for the selection of DRX parameters can be obtained via the POMDP framework in the proposed TDCA scheme. Simulation results show that the TDCA scheme can enhance the energy saving efficiency while the quality-of-service constraint is still satisfied.

This paper studies the joint resource allocation problem of antenna, subchannel, transmission power, and phase duration for the relay-enhanced bidirectional multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) networks. The goal of resource allocation is to minimize the transmission energy in the networks with multiple relay stations (RSs) under the data rate constraints of user equipment (UE). The challenges of this resource allocation problem arise from the complication of multiple-phase assignments within a subchannel since the RS can provide an additional transmission path from the base station to the UEs. The green resource allocation (GRA) schemes with reduced computational complexity are proposed in this paper to develop the joint resource allocation algorithm for the UEs with the consideration of direct and two-hop communications. Both the separate downlink (DL) and uplink (UL) and mixed DL and UL relaying assignments are adopted to obtain the solutions for proposed GRA schemes. Simulation results show that the GRA schemes can provide comparably better energy conservation with the consideration of quality-of-service (QoS) support.

The point-to-multipoint (PMP) mode is considered a well-adopted transmission type that is supported by the IEEE 802.16 standard. The base station (BS) is served as a centralized coordinator to control and forward packets for mobile stations (MSs) within the network. With the consideration of direct communications, the required bandwidth and packet latency are reduced. However, the inappropriate arrangements of direct communications may introduce additional inter-cell interferences for other communications. In order to avoid this situation, a predictive interference-based scheduling (PIS) mechanism for each pair of MSs that are expected to conduct direct communication is proposed in this paper. Based on calculating the interference region and feasible region for the pair, the PIS mechanism properly arranges the MSs to conduct direct communication. The efficiency of the proposed PIS mechanism is evaluated and compared via simulations. Simulation studies show that the PIS approach efficiently enhances the performance of user throughput in comparison with the original adaptive point-to-point (APC) approach and the conventional IEEE 802.16 scheme.

The driver assistance system (DAS) is important to assist drivers with driving safety and efficiency. The lane departure warning system (LDWS), being an essential portion within the DAS, aims at providing warning messages under irregular driving behaviors due to distraction, drowsiness, or driver negligence. As the developments of mobile devices are boosted vigorously, it becomes more feasible to integrate sensing, photographing and displaying components within the LDWS. In this paper, the pitch-aided lane tracking (PALT) algorithm is proposed to detect and track the lanes on the roadways base on the particle filter. A pitch model is proposed to track the lane attributes associated with the pitch angle of a mobile device in order to conquer the effects from bumpy road and loosely-equipped of a mobile device. Moreover, the proposed PALT scheme can compensate the influences of insufficient observations from the display. Numerical results show that proposed PALT scheme can provide better lane-tracking performance compared to both original observations and the model without considering pitch angle.

Relay nodes (RN) have been suggested to be placed near the edge of a cellular network in order to improve link qualities of cell edge user equipments (UEs). Compared to conventional homogeneous networks, system performance for the relay-enhanced network can be significantly degraded if handovers between the RNs and evolved node base-station (eNB) are not correctly and immediately determined by the eNB. Therefore, the UEs may suffer from worse channel conditions which can result in increased energy consumption of entire network. In existing long term evolution advanced (LTE-A) standard, fixed reporting time period from the UEs to eNB is considered inflexible which can be too long to provide immediate handover decisions. Hence, prediction-based handover (PH) scheme is proposed to allow eNBs to make potential handover decisions within the UE's reporting period. The channel qualities for both the direct and relay-enhanced links during this period are predicted based on the partially observable Markov decision process. Moreover, three objectives are designed for the proposed PH scheme including maximizing received signal to interference plus noise ratios of UEs, and minimizing system energy consumption without and with considerations of energy outage of relay nodes. Numerical results show that the proposed PH schemes outperform conventional handover scheme from the perspectives of both system energy consumption and outage probability.

In this paper, we propose a fuzzy Q-learning-based MIMO configuration mode and MCS level (FQL-MOMS) selection scheme for high speed packet access evolution (HSPA+) systems. The FQL-MOMS selection scheme intends to enhance the system throughput under the block error rate (BLER) requirement guarantee. It will determine an appropriate MIMO configuration mode and MCS (modulation and coding scheme) level for packet data transmission in HSPA+systems, under the situations that the channel status is varying and the channel quality indication (CQI) has report delay. The FQL-MOMS scheme considers not only the reported CQI and the last transmission result but also the BLER performance metric and the transmission efficiency. Moreover, it is effectively configured, where the fuzzy rules and the reinforcement signals for the Q-learning algorithm are sophisticatedly designed. Simulation results show that the proposed FQL-MOMS scheme increases the system throughput by up to 49.3 and 35.9 percent, compared to the conventional adaptive threshold selection (ATS) scheme [12] and the Q-HARQ scheme [14], respectively, under the BLER requirement fulfillment.

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

Abstract Techniques for improving network congestion have been proposed in different fields. In recent years, the congestion control problems were studied to enhance the routing performance in wireless multihop networks (WMNs). In this paper, the network allocation vector (NAV) introduced by the contention‐based medium access control (MAC) protocols is utilized for the determination of the channel status around a wireless node (WN). A MAC‐assisted congestion‐controlled routing (MCR) algorithm is proposed to alleviate the network congestion problem by adopting the information from the MAC layer. The routing path is selected based on the congestion‐free probability along the path for transmitting the data packets. Moreover, the adaptive path‐switching scheme and the aggressive hop‐reduction (AHR) local repair mechanism further enhance the routing performance of the proposed MCR algorithm. The effectiveness of the MCR protocol is evaluatedviaboth the analytical study and the simulation results. Without consuming excessive control packets for each WN, the MCR algorithm can achieve better performance when compared with other existing schemes, especially under the scenarios with network congestion. Copyright © 2010 John Wiley & Sons, Ltd.

The power-saving class of type II (PSC II), a type of sleep mode operations in the IEEE 802.16e standard, is designed to reduce power consumption for a mobile station with unsolicited grant service and real-time variable-rate connections. However, the configuration of fixed-length listening windows in the PSC II incurs unnecessary energy consumption or packet loss for these types of connections. In order to improve the energy efficiency of sleep mode operation, a cycle-based flexible window adjustment (FWA) approach for IEEE 802.16m systems is proposed in this paper. The FWA scheme dynamically adjusts the ratio of listening window to sleep window for each sleep cycle based on the number of both arrival and retransmission packets. Numerical analysis and simulation results show that the proposed FWA approach can achieve better energy conservation with reduced packet loss rate.

In recent years, the femtocell plays an important role in wireless networks not only for its spectrum reuse but also for its low power consumption. However, there exists several critical issues that need to be investigated, especially for the interferences between the macrocells and the femtocells. Two major access policies are considered in the femtocell network, including the closed access mode and the open access mode. The closed access mode only permits authorized subscribers to utilize the femtocells; while all users are allowed to connect to the femtocell by adopting the open access mode. The closed access will intuitively be advantageous to the femtocell subscribers, however, it has shown that interference from the macrocell to the femtocell can be mitigated by using the open access mode. In this paper, a cell selection game is theoretically modeled to formulate the behaviors of the nonsubscribers who have the opportunities to connect to the femtocells. The distinct connection manners of nonsubscribers to access the macrocells and the femtocells are modeled as the primary users and cognitive users, respectively. Considering the channel capacity of femtocell as the utility function of this game, the existence of a pure strategy Nash equilibrium is illustrated to provide the win-win situation between the subscribers and nonsubscribers.

The IEEE 1609.4 standard has been proposed to provide multi-channel operations in wireless access for vehicular environments (WAVE), where all the channels are periodically synchronized into control and service intervals. In this paper, based on the concept of cognitive radio, the vehicles are categorized into primary providers (PPs) that intend to transmit safety-related messages and secondary providers (SPs) with non-safety information to be delivered. The cognitive radio-enabled optimal channel-hopping sequence (CROCS) approach is proposed to improve the channel utilization of IEEE 1609.4 standard for multi-channel vehicular networks. Prioritized channel access is analyzed in the CROCS scheme in order to increase the transmission opportunity of the PPs. Moreover, optimal channel hopping sequence is assigned for the SPs based on the dynamic programming technique. It is designed to consider the optimal load balance between both the channel availability and channel utilization within the throughput constraints of PPs. With the adoption of proposed CROCS approach, simulation results show that maximum throughput of SPs can be achieved with guaranteed quality-of-service requirement for the PPs.

This paper studies the subchannel and power allocation problem in the relay-enhanced downlink orthogonal frequency-division multiple access (OFDMA) systems. The challenges of this resource allocation problem arise from the complication of two-phase assignments within a subchannel since the relay station (RS) can provide an additional two-hop signal path from the base station to the user equipments (UEs). Existing research work does not fully consider all the influential factors to achieve feasible resource allocation for the relay-based networks. In this paper, the QoS-based resource allocation (QRA) schemes are proposed to design the subchannel and power allocations for the UEs with the consideration of direct and two-hop communications. Both the selective and fixed phase assignments for the UEs are addressed to obtain the suboptimal solution for the proposed QRA schemes. Different weights are designed for the UEs to exploit the fairness and maximization of system throughput for the relay-enhanced networks. Simulation results show that the proposed QRA schemes with selective phase assignment can provide comparably higher network throughput with QoS consideration.

A great amount of research has devoted to cognitive radio (CR) in recent years in order to improve spectrum efficiency. In decentralized CR networks, the CR users are expected to be capable of dynamically and opportunistically accessing unused spectrums in primary networks. However, since the spectrum of primary networks is comparatively wide, it is not realistic for the CR users to sense the entire spectrum in practice. Consequently, the partially observable Markov decision process (POMDP) is utilized to provide the CR users with sufficient information in partially observable environments. Moreover, existing POMDP-based protocols exploit techniques of channel aggregation in order to improve the spectrum opportunities and system performance. However, the required time for channel sensing is neglected, which is considered inevitable to result in large sensing overhead and spectrum opportunity loss in realistic environments with increased number of channels. Therefore, in this paper, the stochastic multiple channel sensing (SMCS) protocol is proposed to conduct optimal decision-making based on partially observable channel state information under the consideration of sensing overhead. By adopting the proposed SMCS protocol, the CR user can highly accommodate itself to the rapidly varying environment since the optimal decision-making on multiple channel sensing is dynamically adjusted. Furthermore, the steady-state based SMCS (SMCS-S) scheme with simplified decision-making process is proposed in consideration of implementation complexity. Numerical results illustrate that the proposed SMCS protocol can effectively maximize the aggregated throughput for decentralized CR networks.

In the conventional IEEE 802.11 medium access control protocol, the distributed coordination function is designed for the wireless stations (WSs) to perform channel contention within the wireless local area networks (WLANs). Research work has been conducted to modify the random backoff mechanism in order to alleviate the packet collision problem while the WSs are contending for channel access. However, most of the existing work can only provide limited throughput enhancement under specific number of WSs within the network. In this paper, an adaptive reservation-assisted collision resolution (ARCR) protocol is proposed to both improve packet collision and reduce the backoff delays from the random access scheme. With its adaptable reservation period, the contention-based channel access can be adaptively transformed into a reservation-based system if there are pending packets required to be transmitted between the WSs and the access point. Moreover, in order to support quality-of-service requirements, the enhanced-ARCR (E-ARCR) protocol is further proposed to provide adaptation for multiple prioritized traffic in the WLAN. Analytical models are derived for both proposed schemes to evaluate their throughput performance. It can be observed from both analytical and simulation results that the proposed protocols outperform existing schemes with enhanced channel utilization and network throughput.

The IEEE 802.16m standard is developed to support advanced services with high data rate and high mobility for the next generation broadband wireless access networks. A novel sleep mode operation is specified in the standard to conserve the energy of mobile devices. In this paper, a concise analytical model for the sleep mode operation of the IEEE 802.16m is proposed. The effects of both downlink and uplink traffic are properly considered in the proposed model. The power-saving efficiency and mean waiting time are utilized as the metrics for performance analysis. Simulations are performed in order to validate the effectiveness of the proposed system model. Moreover, the performance comparisons between the IEEE 802.16m and IEEE 802.16e are also conducted in the simulations.

Location estimation and tracking for the mobile stations have attracted a significant amount of attention in recent years. Moreover, different types of signal sources are considered available to provide the measurement inputs for location estimation and tracking. In this paper, a hybrid unified Kalman tracking (HUKT) technique is proposed to provide an integrated algorithm for precise location tracking based on both the time-of-arrival (TOA) and time-difference-of-arrival (TDOA) measurements. A new variable is incorporated as an additional state within the Kalman filtering formulation in order to consider the nonlinear behavior for wireless location estimation. Comparing with existing schemes, numerical results illustrate that the proposed HUKT algorithm can achieve enhanced accuracy for mobile location tracking, especially under the environments with insufficient number of signal sources in a single signal path.

The IEEE 1609.4 standard has been proposed to provide multi-channel operations in wireless access for vehicular environments (WAVE), where all the channels are periodically synchronized into control and service intervals. The communication device in each vehicle will stay at the control channel for negotiation and contention during the control interval, and thereafter switch to one of the service channels for data transmission in the service interval. The inefficiency of WAVE system comes from the fact that half of the time intervals of the service channels remain idle since all the stations are performing message contention within the control channel. In this paper, the cognitive radio-enabled multi-channel access (CREM) protocol is proposed to increase the channel utilization of IEEE 1609.4 standard. Based on the concept of cognitive radio, the vehicular stations are categorized into primary stations with safety-related messages and secondary stations with non-safety information to be delivered. Prioritized channel access is designed in the proposed CREM scheme in order to increase the transmission opportunity of primary stations. Moreover, extended time intervals are granted for primary stations to ensure reliability for data transmission. The enhanced CREM (CREM-E) protocol is proposed to further opportunistically increase the channel utilization of secondary stations. Simulation results show that the proposed CREM-E scheme outperforms the existing IEEE 1609.4 protocol with enhanced channel utilization and smaller waiting time intervals.

Generally, optimization techniques for resource allocation of OFDMA systems are infeasible for real-time applications. In this paper, we propose a genetic algorithm with subscriber station (SS) grouping resource allocation (GGRA) scheme for IEEE 802.16 uplink systems. The GGRA scheme firstly designs a rate assignment strategy, applied with a predefined residual lifetime, to allocate resource to each service dynamically. It then aggregates high correlation SSs into the same group, where the SSs will be allocated to different slots so as to avoid mutual user interference. Finally, the GGRA scheme finds an optimal assignment matrix for the system by the genetic algorithm, based on the SS groups to greatly lessen the computation complexity. The GGRA scheme can also maximize system throughput and fulfill QoS requirements. Simulation results show that the proposed GGRA scheme performs better than the EFS algorithm [] and the MLWDF algorithm [] in system throughput, voice/video packet drop rate, unsatisfied ratio of HTTP users/packets, and FTP throughput. The computation complexity of the GGRA scheme is also tractable and thus feasible for real-time applications.

In recent years, channel-hopping based medium access control (MAC) protocols are proposed to improve the capacity in a decentralized multi-channel cognitive radio (CR) networks without extra usage of a control channel. Each CR user has to stochastically follow a default channel-hopping sequence in order to sense a channel and to conduct its frame transmission. In this paper, based on the channel-hopping protocol, an analysis is conducted on both the probability of channel availability and the average frame delay for the primary queueing networks. The analytical model is proposed by considering the impact caused by imperfect sensing of the CR users and the imperfect synchronization between the primary and CR networks. According to the proposed model with more realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on dynamic programming technique. It is designed by exploiting the optimal load balance between both the channel availability and channel utilization within the delay constraints of primary users (PUs). By adopting the OCS approach, the maximum aggregate throughput of CR users and the quality of service (QoS) requirement of PUs can both be achieved. Numerical results illustrate that the proposed OCS scheme can effectively maximize the aggregate throughput compared to conventional channel-hopping sequences, and as well guarantee the QoS requirement of the PUs.

The IEEE 802.11n standard has been proposed to provide enhanced throughput performance for the wireless local area networks. Most of the existing research work considers packet aggregation techniques for enhancing the network throughput from the medium access control perspective. Consequently, the large amount of small-sized acknowledgement (ACK) packets are aggregated via the block ACK mechanisms in order to improve the system throughput. In this paper, a block ACK classification is proposed to categorize the existing block ACK mechanisms into the greedy and the conservative schemes. Based on the Markovian techniques, the respective analytical models are constructed in order to measure the throughput-related metric of the window utilization under different packet error probabilities. Simulations are conducted to validate and compare the proposed analytical models associated with the two types of block ACK schemes. It can be observed from the numerical results that the greedy scheme outperforms the conservative approach with comparably higher window utilization.

The power saving class of type II (PSC II), one of the sleep mode operations in the IEEE 802.16e standard, is designed to reduce power consumption for unsolicited grant service (UGS) and real-time variable-rate (RT-VR) connections. However, the configuration of fixed-length listening windows in the PSC II incurs unnecessary energy consumption or packet loss for these types of connections. In order to enhance the performance of sleep mode operations, an approach with adaptive listening window (ALW) is proposed. The ALW scheme dynamically adjusts the length of each listening window based on the number of both arrival and retransmission packets as well as the delay constraint. Numerical analysis and simulation results show that the proposed ALW approach can effectively reduce the packet loss rate for UGS connections, whereas better energy conservation with reduced packet loss rate is achieved for RT-VR connections.

The void problem causing the routing failure is the main challenge of the greedy routing in the wireless sensor networks. The current research work still can not fully deal with the void problem since the excessive control overheads should be consumed so as to guarantee the delivery of packets. In our previous work, a greedy anti-void routing (GAR) protocol is proposed to solve the void problem with increased routing efficiency by exploiting the boundary finding technique for the unit disk graph (UDG). The proposed rolling-ball UDG boundary traversal (RUT) is employed to completely guarantee the delivery of packets from the source to the destination node under the UDG network. However, the realization of the GAR scheme is not trivial since there can be considerable efforts required in order to realize the continuous rolling ball mechanism of the RUT scheme. In this paper, the boundary map (BM) and the indirect map searching (IMS) scheme are therefore proposed as efficient algorithms for the realization of the RUT technique. After the realization of the GAR protocol, the extensive simulations are conducted and compared with the existing localized routing algorithms. The simulation results show that the proposed GAR protocol can provide better routing efficiency.

Recent studies have been conducted to indicate the ineffective usage of licensed bands due to the static spectrum allocation. In order to improve the spectrum utilization, the cognitive radio is therefore suggested to dynamically exploit the opportunistic primary frequency spectrums. The interference from the secondary users to the primary user consequently draws the attention to the spectrum and power management for the cognitive radio networks. In this paper, the constrained stochastic games are utilized to exploit the optimal policies for power management by considering the variations from both the channel gain and the primary traffic. Both the underlay and overlay waveforms are considered within the network scenarios for the proposed power management scheme. Constraints for allowable interferences will be applied in order to preserve the communication quality among the primary and the secondary users. According to the formulation of the constrained stochastic games, the existence of the constrained Nash equilibrium will be validated with rigorous proofs, which will be acquired as the optimal policies for the power management problem. Simulation results further validate the correctness of the theoretically-derived policies for dynamic power management.

Channel condition is considered an important issue that affects the performance in wireless networks. Link Adaptation techniques have been proposed to improve the degraded network performance by adjusting the design parameters, e.g. the modulation and coding schemes, in order to adopt the dynamically changing channel conditions. Furthermore, due to the advancement of the IEEE 802.11n standard, the network goodput can be enhanced with the exploitation of its frame aggregation schemes. However, none of the existing link adaption algorithm is designed to consider the feasible aggregated frame length that should be adapted according to the changing environments. In this paper, A frame-aggregated link adaptation (FALA) algorithm is proposed to dynamically adjust system parameters in order to improve the network goodput under varying channel conditions. For the purpose of maximizing the network goodput, both the optimal frame payload size and the modulation and coding schemes are jointly acquired according to the signal-to-noise ratio under specific channel condition. Numerical results illustrate that the proposed FALA protocol can effectively increase the goodput performance comparing with other existing link adaptation schemes, especially under dynamically changing environments.

The power-saving class of type I (PSC I), one of the sleep mode operations in the IEEE 802.16e standard, is designed to reduce power consumption for non-real-time traffic. However, the inefficiency of PSC I comes from its configuration and operation. Based on the notions of IEEE 802.16m sleep mode operation, a statistical sleep window control (SSWC) approach is proposed to improve energy efficiency for mobile stations in this paper. The SSWC approach exploits a partially observable Markov decision process (POMDP) to conjecture the present traffic state. Based on the properties of POMDP, the optimal policy for sleep window selection is acquired in the SSWC approach. The performance evaluation is conducted and compared via the simulations. Simulation results show that the proposed SSWC approach outperforms the IEEE 802.16e PSC I scheme and the inferred IEEE 802.16m power-saving mechanism.

The next generation wireless local area networks (WLANs) with enhanced throughput performance have attracted significant amounts of attention in recent years. Based on the IEEE 802.11n standard, frame aggregation is considered one of the major factors to improve the system performance of WLANs from the medium access control perspective. In order to fulfill the requirements of the high throughput performance, feasible design of automatic repeat request (ARQ) mechanisms is considered important for providing reliable data transmission. In this paper, an aggregated selective repeat ARQ (ASR-ARQ) algorithm is proposed, which incorporate the conventional selective repeat ARQ scheme with the consideration of frame aggregation. A novel analytical model based on the signal flow graph is established in order to realize the behaviors of ASR-ARQ algorithm. Simulations are also conducted to validate the effectiveness of proposed ASR-ARQ mechanism.

The limitation on the battery lifetime has been a critical issue for the advancement of mobile computing. Different types of power-saving techniques have been proposed in various fields. In order to provide feasible energy-conserving mechanisms for the mobile subscriber stations (MSSs), three power-saving types have been proposed for the IEEE 802.16e broadband wireless networks. However, these power-saving types are primarily targeting for the cases with a single connection between the base station (BS) and the MSS. With the existence of multiple connections, the power efficiency obtained by adopting the conventional scheduling algorithm can be severely degraded. In this paper, with the consideration of the multiple connections and their quality-of-service (QoS) constraints, a frame aggregation-based power-saving scheduling (FAPS) algorithm is proposed to enhance the power efficiency by aggregating multiple underutilized frames into fully-utilized ones. The performance evaluation is conducted and compared via the simulations. Simulation results show that the sleep frame ratio (i.e., a power efficiency metric) of the proposed FAPS algorithm outperforms the baseline protocols with tolerable delay.

Location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become inapplicable for location tracking due to the deficiency of signal sources. In this paper, the enhanced predictive location tracking (EPLT) are proposed to alleviate this problem. The EPLT scheme utilizes the predictive information obtained from the Kalman filter in order to provide the additional signal inputs for the location estimator. Furthermore, the EPLT scheme incorporates the geometric dilution of precision (GDOP) information into the algorithm design. Persistent accuracy for location tracking can be achieved by adopting the proposed EPLT scheme, especially with inadequate signal sources. Numerical results demonstrate that the EPLT algorithm can achieve better precision in comparison with other location tracking schemes.

In recent years, energy conservation has been considered as an important topic within the wireless sensor networks (WSNs). How to extended the network lifetime with cost-effective management is the primary concern within the design of power- saving mechanisms for WSNs. In this paper, with the consideration of energy conservation, two location tracking algorithms are proposed in the distributed WSNs in order to trace a mobile station that is not synchronized with the corresponding sensor nodes (SNs). The scheduled power-saving tracking (SPT) scheme is first proposed for the SNs to trace the MS's beacon instants and consequently to track its movement in a power-saving manner. Coordinators are selected to manage and collect the information for location estimation and tracking of the MS. The predicted information with respect to the MS's movements is obtained by the coordinator in order to notify specific hearable SNs for receiving the MS's next beacon. Moreover, the geometry-assisted power-saving tracking (GPT) algorithm is proposed to further conserve the energy of the SNs. With the consideration of the geometry dilution of precision effect, only specific three SNs will be selected for the computation of MS's positions. Performance comparison of the proposed schemes are conducted by observing both the power efficiency and tracking accuracy under varied MS's velocities. Compared to the SPT scheme, numerical results show that the proposed GPT algorithm can effectively extend the network lifetime and also achieve comparable tracking accuracy.

In conventional IEEE 802.11 medium access control protocol, the distributed coordination function is designed for the wireless stations (WSs) to perform channel contention within the wireless local area networks (WLANs). Packet collision is considered one of the major issues within this type of contention-based scheme, which can severely degrade the network performance for the WLANs. Research work has been conducted to modify the random backoff mechanism in order to alleviate the packet collision problem while the WSs are contending for channel access. However, most of the existing work can only provide limited throughput enhancement under specific number of WSs within the network. In this paper, an adaptive reservation-assisted collision resolution (ARCR) protocol is proposed to improve the packet collision from the random access schemes. With its adaptable reservation period, the contention-based channel access can be adaptively transformed into a reservation-based system while there are pending packets required to be transmitted from the WSs. Furthermore, according to the designed reservation table within the access point, the fairness for channel access between the WSs is addressed within the ARCR protocol. Numerical results indicate that the proposed ARCR scheme can outperform the existing schemes with enhanced channel utilization and network throughput.

Recent studies have been conducted to indicate the ineffective usage of licensed bands due to the static spectrum allocation. In order to improve the spectrum utilization, the cognitive radio (CR) is therefore suggested to dynamically exploit the opportunistic primary frequency spectrums. How to provide efficient spectrum handoff has been considered a crucial issue in the CR networks. Existing spectrum handoff algorithms assume that all the channels within the network can be correctly sensed by the CR users in order to perform appropriate spectrum hand-off process. However, this assumption is considered impracticable in realistic circumstances primarily due to the excessive time required for the CR user to sense the entire spectrum space. In this paper, the partially observable Markov decision process (POMDP) is exploited to estimate the network information by partially sensing the frequency spectrums. A POMDP-based spectrum handoff (POSH) scheme is proposed to determine the optimal target channel for spectrum handoff according to the partially observable channel state information. By adopting the policy resulted from the POSH algorithm for target channel selection, minimal waiting time at each occurrence of spectrum handoff can be achieved. Numerical results illustrate that the proposed POSH scheme can effectively minimize the required waiting time for spectrum handoff in the CR networks.

The unreachability problem (i.e., the so-called void problem) that exists in the greedy routing algorithms has been studied for the wireless sensor networks. Some of the current research work cannot fully resolve the void problem, while there exist other schemes that can guarantee the delivery of packets with the excessive consumption of control overheads. In this paper, a greedy anti-void routing (GAR) protocol is proposed to solve the void problem with increased routing efficiency by exploiting the boundary finding technique for the unit disk graph (UDG). The proposed rolling-ball UDG boundary traversal (RUT) is employed to completely guarantee the delivery of packets from the source to the destination node under the UDG network. The boundary map (BM) and the indirect map searching (IMS) scheme are proposed as efficient algorithms for the realization of the RUT technique. Moreover, the hop count reduction (HCR) scheme is utilized as a short-cutting technique to reduce the routing hops by listening to the neighbor's traffic, while the intersection navigation (IN) mechanism is proposed to obtain the best rolling direction for boundary traversal with the adoption of shortest path criterion. In order to maintain the network requirement of the proposed RUT scheme under the non-UDG networks, the partial UDG construction (PUC) mechanism is proposed to transform the non-UDG into UDG setting for a portion of nodes that facilitate boundary traversal. These three schemes are incorporated within the GAR protocol to further enhance the routing performance with reduced communication overhead. The proofs of correctness for the GAR scheme are also given in this paper. Comparing with the existing localized routing algorithms, the simulation results show that the proposed GAR-based protocols can provide better routing efficiency.

Location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become inapplicable for location tracking due to the deficiency of signal sources. In this paper, two predictive location tracking algorithms are proposed to alleviate this problem. The predictive location tracking (PLT) scheme utilizes the predictive information obtained from the Kalman filter in order to provide the additional signal inputs for the location estimator. Furthermore, the geometric-assisted PLT (GPLT) scheme incorporates the geometric dilution of precision (GDOP) information into the algorithm design. Persistent accuracy for location tracking can be achieved by adopting the proposed GPLT scheme, especially with inadequate signal sources. Numerical results demonstrate that the GPLT algorithm can achieve better precision in comparison with other network-based location tracking schemes.

Due to the low-cost design nature of greedy-based routing algorithms, it is considered feasible to adopt this type of schemes within the three-dimensional (3D) wireless sensor networks. In the existing research work, the unreachability problem (i.e., the so-called void problem) resulting from the greedy routing algorithms has not been fully resolved, especially under the 3D environment. In this letter, a three-dimensional greedy anti-void routing (3D-GAR) protocol is proposed to solve the 3D void problem by exploiting the boundary finding technique for the unit ball graph (UBG). The proposed 3D rolling-ball UBG boundary traversal (3D-RUT) scheme is employed to guarantee the delivery of packets from the source to the destination node. The correctness proofs, protocol implementation, and performance evaluation for the proposed 3D-GAR protocol are also given in this letter.

In recent years, wireless location estimation has attracted a significant amount of attention in different areas. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile station (MS) and the base stations (BSs). The two-step Least Square (LS) method has been studied in related research to provide efficient location estimation of the MS. However, the algorithm results in inaccurate location estimation under the circumstances with poor geometric dilution of precision (GDOP). In this paper, the GDOP- assisted location estimation (GOLE) schemes are proposed by considering the geometric relationships between the MS and its associated BSs. According to the minimal GDOP criterion, the BSs are fictitiously repositioned and are served as a new set of BSs within the formulation of the two-step LS algorithm. The proposed GOLE schemes can both preserve the computational efficiency from the two-step LS method and obtain precise location estimation under poor GDOP environments. Comparing with other existing schemes, numerical results demonstrate that the proposed GOLE algorithms can achieve better accuracy in wireless location estimation.

The point-to-multipoint (PMP) mode is supported in the medium access control layer of the IEEE 802.16 standard. The base station (BS) is served as the centralized coordinator to control and forward packets for the subscriber stations (SSs) within the network. In the case that two SSs intend to conduct packet transmission, it is required for the packets to be rerouted to the BS before arriving at the destination SS. The communication bandwidth is apparently wasted due to the rerouting processes. In this paper, a point-to-point direct communicable (PDC) approach is proposed to achieve direct communication among the SSs within the PMP mode of the IEEE 802.16 standard. The BS is coordinating and arranging specific time intervals for the two SSs that are actively involved in packet transmission. The effectiveness of the proposed PDC scheme can be observed via the simulation results. The PDC approach outperforms the conventional scheme within the IEEE 802.16 PMP networks in terms of the network throughput.

In recent years, cooperative communication has been proposed as a new communication paradigm that incorporates a relay node to assist the direct point-to-point transmission. By exploiting the cooperative diversity, different types of techniques have been proposed to improve the transmission reliability from the physical layer perspective, e.g. the cooperative automatic repeat request. However, owing to the longer transmission time resulting from the cooperative schemes, there is no guarantee to enhance the network throughput in view of the medium access control (MAC) performance. In this paper, the system throughput of the cooperative communication is evaluated by exploiting the proposed analytical model based on the IEEE 802.11 MAC protocol. Both the relay-based and the original direct communications are considered in the analytical studies. Simulations are conducted to further validate the effectiveness of the proposed model. In terms of the network throughput, whether to adopt the cooperative schemes depends on the tradeoff between the cooperative transmission delay and the channel condition of the direct communication.

The design of an efficient handover technique is considered a crucial topic in the cellular-based wireless networks. Feasible handover process can minimize the performance degradation as the mobile station (MS) is moving between different base stations (BSs). Based on the IEEE 802.16e standard, it is required to provide satisfactory handover performance under a wide-range of MS’s moving speeds. With the requirements of providing location-based services as stated in the standard, it becomes feasible to utilize the MS’s estimated location to assist the decision of the handover process. However, in order to achieve a satisfactory performance for location estimation, excessive time is required for synchronizing with multiple non-serving BSs. In this paper, two location tracking based handover schemes are proposed for achieving reduced number of handover without additional connections between the MS and the non-serving BSs. The kinematics-assisted tracking (KAT) algorithm adopts the kinematic relationship to estimate the MS’s location while the non-serving BSs are unavailable. Furthermore, the geometry-assisted tracking (GAT) scheme utilizes the geometric constraints for the prediction of the MS’s position. Numerical results show that the proposed GAT algorithm can effectively reduce the handover number under different environments.

Different types of power-saving techniques have been proposed in various fields to alleviate the limitation on the battery lifetime for the mobile stations (MSs). In order to provide feasible energy-conserving mechanism, three different power-saving types have been introduced in the IEEE 802.16e broadband wireless networks. By means of pre-negotiations, the MS can be absent from the serving base station (BS) by performing the sleep mode operation. For each involved MS, connections with similar properties can be group into the defined power-saving classes. In this paper, a comprehensive performance modeling of the IEEE 802.16e power-saving classes of Type I and II are proposed. Multiple connections between the BS and the MS with both the downlink and the uplink traffic are considered in the analytical models. The power-saving efficiency and the mean waiting time are utilized as the metrics for performance analysis. Simulations are performed in order to validate the results obtained from the analytical models.

The design of an efficient handover technique is considered as one of the crucial topics in the cellular-based wireless networks. Feasible handover process can minimize the performance degradation as the mobile device is moving between different base stations. Based on the IEEE 802.16e standard, it is required to provide satisfactory handover performance under a wide-range of moving speeds of the mobile device. In this paper, a predictive movement-based handover (PMHO) algorithm is proposed. Without incurring excessive overheads as from the conventional location estimation and tracking techniques, the proposed sideline-traveling scheme within the PMHO algorithm employs the moving state indicator (MSI) to predict the potential moving characteristics of the mobile device. The handover decision of the PMHO approach is determined based on the MSI values associated with the neighbor base stations. Numerical results show that the proposed PMHO algorithm can effectively reduce both the number of handover and the size of the neighbor-scanning set within the mobile device.

The limitation on the battery lifetime has been a critical issue for the advancement of mobile computing. Different types of power-saving techniques have been proposed in various fields. In order to provide feasible energy-conserving mechanism for the mobile subscriber stations (MSSs), three power-saving types have been proposed for the IEEE 802.16e broadband wireless networks. However, these power-saving types are primarily targeting for the cases with a single connection between the base station (BS) and the MSS. With the existence of multiple connections, the power efficiency obtained by adopting the conventional scheduling algorithm can be severely degraded. In this paper, a maximal power-conserving (MPC) scheduling algorithm is proposed to consider the aggregated effect from the multiple connections to the power efficiency. Moreover, the quality-of-service (QoS) constraints from both the downlink and the uplink traffic are employed in the design of the MPC algorithm in order to facilitate the corresponding MSS to fulfill its QoS requirements in both directions. Numerical results show that the proposed MPC scheduling algorithm outperforms the conventional 802.16e power-saving mechanism, especially under the multi-connection scenarios.

Mobile location estimation has attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The two-step Least-Squares (LS) method has been studied in related research to provide efficient location estimation of the mobile devices. However, the algorithm results in insufficient accuracy for location estimation with the existence of Non-Line-Of-Sight (NLOS) errors. A Geometry-Assisted Location Estimation (GALE) algorithm is proposed in this paper with the consideration of different geometric layouts between the mobile device and its associated base stations. In order to enhance the precision of the location estimate, the GALE scheme is designed to incorporate the geometric constraints within the formulation of the two-step LS method. The algorithm can be utilized to estimate both the two-dimensional and the three-dimensional positions of a mobile device. The proposed GALE scheme can both preserve the computational efficiency from the two-step LS algorithm and obtain a precise location estimation under NLOS environments. Moreover, the Cramer-Rao Lower Bound (CRLB) for various types of measurement signals is derived to facilitate the performance comparison between different location estimation schemes. Numerical results illustrate that the proposed GALE algorithm can achieve better accuracy compared with other existing network-based location estimation schemes.

In recent years, various types of ad hoc routing protocols have been studied in the mobile ad hoc networks. Specifically, the cluster-based hierarchical routing algorithms have been developed to increase the system performance. However, the significant overhead resulting from the formation of the cluster structure make it unsatisfactory to assist the design of routing algorithms, especially with small number of communication pairs in the network. In this paper, an on-demand routing-based clustering (ORC) protocol is developed in order to alleviate the excessive overhead induced from conventional cluster formation. The cluster structure is simultaneously established along with the construction of the on-demand routing path. Depending on the existing number of communication pair within the network, the clusters are adaptively formed, maintained, and disbanded. The effectiveness of the proposed ORC algorithm can be observed via the simulation results.

location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become unapplicable due to the insufficiency of signal sources. In this paper, a predictive location tracking (PLT) algorithm is proposed to alleviate this problem. The predictive information obtained from the Kalman filter is employed to provide the additional signal inputs for the location estimators. The proposed PLT scheme can offer persistent accuracy for location tracking of the mobile devices, especially with inadequate signal sources. Numerical results demonstrate that the proposed PLT algorithm can achieve better precision, comparing with other existing schemes, in mobile location estimation and tracking.

The design of routing protocols is crucial for mobile ad-hoc networks due to their fast-changing characteristics. Most of the ad-hoc routing algorithms are designed based on extensive flooding of control packets, which result in excessive overheads within the networks. The greedy routing algorithms employ localized information from the mobile nodes (e.g. relative distance or direction), which induce comparably less control packets in the networks. However, several problems (e.g voids, loops, or dead-ends) are encountered in most of the greedy algorithms due to the localized characteristics. In this paper, a largest forwarding region (LFR) routing protocol is developed. The mobile node with the largest extended forwarding region (EFR) is selected as the next hopping node for packet forwarding. The associated backward constraint (BC) and dead-end recovery (DER) mechanisms further alleviate the problems resulting from voids in the networks. The performance comparison between the proposed LFR algorithm and other existing greedy routing schemes is conducted via simulations. It is observed that the LFR scheme effectively increases the packet arrival rate without creating excessive control overheads.

Mobile location estimation has attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The two-step Least Square (LS) method has been studied in related research to provide efficient location estimation of the mobile devices. However, the algorithm may results in inaccurate location estimation under (i) the existence of the Non-Line-Of-Sight (NLOS) errors and (ii) poor Geometric Dilution of Precision (GDOP) circumstances. A location estimation algorithm with the Virtual Base Stations (VBS) is proposed in this paper by considering the geometric layouts between the mobile device and its associated base stations. In order to enhance the precision of the location estimate, the VBS scheme is designed to incorporate the virtual base stations and the related geometric constraints within the formulation of the two-step LS method. The proposed VBS scheme can both preserve the computational efficiency from the two-step LS algorithm and obtain precise location estimation under poor GDOP and NLOS environments. Numerical results demonstrate that the proposed VBS algorithm can achieve better accuracy, comparing with other existing schemes, in mobile location estimation.

There has been an increasing demand for applications to support multicast communication in the mobile ad hoc networks. One of the primary concerns in the multicast communication is the feasible design of the multicast ad hoc routing protocols. Conventionally, the design of the multicast routing protocols can be categorized into the tree-based and the mesh-based schemes. These two types of protocols have their own strength and weakness under different networking scenarios. In this paper, a power-controlled hybrid multicast routing (PCHMR) protocol is proposed, which consists of both the tree-based and the mesh-based structures. The route determination scheme of the PCHMR algorithm not only relies on the hop counts but also on the received power strength of the neighborhood nodes. Moreover, the route determination thresholds are adaptive to the mean and the variations of the received power. The proposed PCHMR algorithm is suitable for the dynamically changing network topologies, especially for the group mobility scenario. Different signal propagation models are utilized in the simulations to evaluate the effectiveness of the PCHMR protocol

In recent years, there has been increasing demands in providing precise location estimation in weak signal environment (e.g. in urban area or inside a building). Conventional GPS acquisition techniques are considered to be adequate on positioning capability in outdoor environment. However, most of them are not satisfactory for applications with weak signals. It has been studied that a longer time of data integration is required in order to provide sufficient data for acquisition under weak signal circumstances. In this paper, an adaptive GPS acquisition technique is proposed, which adaptively adjusts the detection threshold and the length of data integration for signal acquisition. The location of the navigation bit-transition is perceived within the acquisition data in order to adaptively determine either the coherent or the differential coherent combining method should be used. The simulation results show that the proposed adaptive acquisition technique outperforms both the non-coherent combining and the differential-coherent combining algorithms. As the SNR value of the incoming signal decreases, the effectiveness of the proposed adaptive scheme can be observed.

The limitation on the battery life has been a critical issue for the advancement of the mobile computers. Users encounter unsatisfactory battery power while using their mobile devices, especially on the occasions of transmitting data using the wireless networks. It has been studied that the amount of energy consumed within the mobile devices is significantly affected by the design of the Medium Access Control (MAC) protocol within the wireless interface. This paper presents a Intelligent Router-Assisted (IRouter) power-saving MAC algorithm that achieves energy conservation by predicting the next hopping node within the delivering route. With the assistance of the intelligent routers in the network, the packet delivery between several mobile nodes can be accomplished within the same beacon interval. The performance comparison between the proposed IRouter algorithm and the existing MAC protocols is conducted via simulations. It is observed that the IRouter scheme can achieve feasible performance in both energy conservation and routing efficiency.

In recent years, the incorporation of the directional antennas within mobile devices has been studied in many areas. The usage of directional antennas can greatly reduce the radio interference, which results in improved utilization of the wireless medium. It becomes practical to exploit the directional antennas in the Medium Access Control (MAC) protocol design. In this paper, a Location and Mobility Aware (LMA) MAC protocol is developed for the mobile ad hoc networks. The predictive location of the mobile devices are adopted to enhance the robustness of the communication linkages while using the directional beams. The deafness problem is also alleviated using the directional listen (D-Listen) mechanism in the proposed algorithm. Under dynamic moving scenarios, both the spatial reuse and the routing efficiency are preserved using the proposed LMA MAC scheme. The performance of the proposed algorithm is evaluated and compared with other existing protocols in simulations.

In recent years, many location-a ware routing protocols have been proposed for the mobile ad hoc networks. The routing performance is improved by exploiting the position information of the mobile nodes. However, the mobility characteristics of the mobile nodes have not been taken into consideration. In this paper, the proposed predictive mobility and location-aware routing (PMLAR) algorithm incorporates the moving behaviors of the mobile nodes in the protocol design. The region for packet forwarding is determined by predicting the future trajectory of the destination node. The routing performance can be effectively improved by adopting the prediction mechanism of the proposed PMLAR algorithm. Simulation results show that the PMLAR algorithm outperforms other routing protocols under different network topologies.