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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. [ABSTRACT FROM AUTHOR]

In recent years, wireless location estimation has attracted a significant amount of attention in different areas. Various types of radio signals are applied for the development of location-estimation algorithms. In this paper, the range measurements acquired from the received time-based information are adopted, and the modified least square (LS) method is utilized to process the raw data and to finally locate the target object. Practical issues, such as the nonline-of-sight (NLOS) errors and the geometric dilution of precision (GDOP) effect, are of concern. The NLOS error will cause a large nonnegative bias while measuring the propagation delay, which will lead to an unreliable result for location estimation. On the other hand, a large GDOP value corresponds to a poor geometric topology, which will result in inferior performance by adopting most of the existing location algorithms. The proposed location-estimation algorithms with virtual base stations (VBSs) will both mitigate the influence from the NLOS errors by imposing the geometric constraints and reduce the GDOP effect by incorporating the assisted VBSs. Two iterative schemes are proposed, including the center-of-gravity-based VBS (VBS-CG) and the minimal GDOP-based VBS (VBS-MG) algorithms, to determine the required number and the locations of the assisted VBSs. The proposed VBS algorithms are compared with other existing location-estimation schemes via simulations. The performance of the VBS-MG algorithm is observed to outperform the other schemes, particularly under the environments with larger NLOS errors and poor geometric layouts. Index Terms--Geometric dilution of precision (GDOP), non-line-of-sight (NLOS) errors, time of arrival (TOA), two-step least square (LS) method, wireless location estimation.

In recent years, many location-aware routing protocols have been proposed for mobile ad hoc networks. The efficiency of the routing protocols can be improved by considering the location information of the mobile nodes (MNs). However, the mobility characteristics of the MNs have not been taken into account in most of the related work. In this paper, the proposed velocity-aided routing (VAR) algorithm determines its packetforwarding scheme based on the relative velocity between the intended forwarding node and the destination node. The routing performance can further be improved by the proposed predictive mobility and location-aware routing (PMLAR) algorithm, which incorporates the predictive moving behaviors of MNs in protocol design. The region for packet forwarding is determined by predicting the future trajectory of the destination node. The routing performance can be effectively enhanced by adopting both the proactive maintenance and the VAR mechanisms within the proposed PMLAR scheme. Simulation results show that the PMLAR protocol associated with its derivative schemes outperforms other routing protocols under different network topologies. Index Terms--Location-based ad hoc routing protocol, Mobile Ad hoc NETwork (MANET), mobility model, prediction mechanism.

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 vehicular ad hoc networks (VANETs). The predictive location and mobility of the vehicles are adopted to provide robust communication links while using the directional beams. The deafness problem is also alleviated using the directional-listen mechanism in the proposed algorithm. Moreover, the exploitation of the directional beacons within the scheme can enhance the reliability of the communication linkages, even when the moving directions and speeds of the vehicles have been changed. 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 MAC protocols in simulations. Index Terms--Directional antennas, medium-access control (MAC), prediction mechanism, vehicular ad hoc networks (VANETs).

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. [ABSTRACT FROM AUTHOR]

With proliferation of fifth generation (5G) new radio (NR) technology, it is expected to meet the requirement of diverse traffic demands. We have designed a coordinated multi-point (CoMP) enhanced flexible multi-numerology (MN) for 5G-NR networks to improve the network performance in terms of throughput and latency. We have proposed a CoMP enhanced joint subcarrier and power allocation (CESP) scheme which aims at maximizing sum rate under the considerations of transmit power limitation and guaranteed quality-of-service (QoS) including throughput and latency restrictions. By employing difference of two concave functions (D.C.) approximation and abstract Lagrangian duality method, we theoretically transform the original non-convex nonlinear problem into a solvable maximization problem. Moreover, the convergence of our proposed CESP algorithm with D.C. approximation is analytically derived with proofs, and is further validated via numerical results. Simulation results have demonstrated that our proposed CESP algorithm outperforms the conventional non-CoMP and single-numerology mechanisms along with other existing benchmarks in terms of lower latency and higher throughput under the scenarios of center and edge users. [ABSTRACT FROM AUTHOR]

Nowadays, Wi-Fi or Bluetooth based indoor localization algorithms are designed to improve the effects from variation of receive signal strength (RSS) at different locations. However, the positioning accuracy of targeting device is highly related to the indoor topological layout, e.g., the blocking effects from partitioning walls. In this paper we propose a novel skeleton-based positioning and reference point deployment for hybrid wireless localization (S-PDH) that utilizes the spatial skeleton of indoor layout to improve location estimation accuracy. Firstly, we adopt an automatic map analysis algorithm to generate appropriate reference points (RPs) from the spatial skeleton of indoor layout. Secondly, we propose a novel clustering algorithm to consider both spatial layout and RSS information in order to enhance the accuracy of RP matching. Those outlier RPs resulting poor performance will be replaced by appropriate deployment of BLE APs. Finally, the cluster-based positioning algorithm is presented to estimate the target’s location. Compared to existing methods, both the simulation and experimental results show that the proposed S-PDH system can achieve better positioning accuracy along with the benefits of automatically generating the spatial layout information for wireless indoor localization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) provides extremely high throughput owing to their high bandwidth utilization over higher frequencies. To compensate for the severe loss and attenuation, beamforming training is used to determine the optimal beam direction and thereby improve directional transmission power. However, the training overhead will be significantly increased with narrower beams, particularly under conventional exhaustive schemes. Therefore, we propose a learning-based adjustable beam number training (LABNT) scheme to intelligently and flexibly select beam training candidates considering different user mobility and hybrid line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. In LABNT, several deep learning networks are parallelly constructed and connected to a reinforcement learning network to determine training candidates dynamically based on performance rewards. A novel enhanced feature selection method, that is, uniformly distributed mutual information, is developed based on the correlation of historical results to select the beam inputs for each deep learning network. In simulations, the proposed LABNT outperforms other existing schemes in terms of beam alignment accuracy, training latency, and system throughput. Moreover, following the IEEE 802.11ad/ay protocols, we implement realistic mmWave beamforming training on a programmable hardware platform with integrated 60 GHz wireless-gigabit (WiGig) transceiver devices. The experimental results on the WiGig platform demonstrate that the proposed LABNT scheme can achieve real-time performance with approximately Gbps transmission throughput and millisecond-level training overhead. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) systems have attracted considerable attention as a benefit of their Gbps services. Therefore, wireless-gigabit (WiGig) transmission has been developed based on the IEEE 802.11ad and 802.11ay standards of wireless local area networks (WLANs) in the 60-GHz band. To overcome the high path loss of mmWave, beamforming and beam-sector based training protocols are specified in 802.11ad/ay. With the increase in service demand, more WiGig access points (APs) will be deployed, hence the transmission frame structure of beam control should be adjustable for supporting both multiple APs and multiple users. The conventional exhaustive beam search (EBS) scheme has a potentially excessive complexity for high number of beams. Furthermore, assigning fixed training slots for users will lead to high-latency beam training under EBS. Hence, we design an advanced protocol for multiple APs and multiple users. Based on this protocol, we propose coordinated multiple AP multiuser training (CMMBT) including variable length framing and adjustable beam training. Our simulations demonstrate that CMMBT provides flexible training control and achieves the lowest training latency and highest average throughput performance amongst the state-of-the-art solutions in the open literature. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) has been widely considered as a promising technology in the next generation wireless communication systems. Various beam-based directional transmission techniques have been proposed to compensate high pathloss caused by mmWave properties. However, mobility effect of users potentially causes severe inter-beam interferences, which has not been fully-investigated in existing studies. In this paper, we have analyzed different types of mobility-aware beam interference models. Additionally, the theoretical SINR is derived based on channel statistics and the trajectories of users. We propose a mobility-aware subband and beam resource allocation (MSBA) scheme for the mmWave subband-beam massive multi-input multi-output (SB-MMIMO) systems. The proposed MSBA benefits from reduced computation complexity for maximizing system throughput constrained by quality-of-service (QoS) demands of users. The first phase of MSBA scheme is responsible for resource block assignment; while the second phase allocates beamwidth and beam directions according to the analytical derivations. Numerical results show that the proposed MSBA scheme can effectively achieve the highest system throughput and the lowest complexity compared to existing schemes in open literatures. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) technology achieving multi-gigabits speed plays a significant role in beyond 5G and the next 6G wireless communication networks thanks to its huge spectrum utilization and beam-based directional transmissions. To tackle temporary ultra-high data demands of hotspot areas, three-dimensional (3D) heterogeneous network (HetNet) is designed with the integration of mmWave unmanned aerial vehicles (UAV) to provide resilient instantaneous control and data transmissions. However, some critical beam-related issues for mmWave implementation of UAVs/drones are addressed including robust initial beam alignment, mobility-aware beam tracking and beam refinement. In this research, we aim at developing robust and efficient beam control mechanisms by implementing a prototype of 3D flying heterogeneous communications. The backhaul connections operate at mmWave frequency between airship and UAV/drone, while fronthaul links adopt lower frequency bands such as Wi-Fi for multiuser data transmissions. We evaluate system performances for our proposed beam control schemes and provide a real-time prototype of 3D on-demand flying mobile communication for mmWave HetNets. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) in the unlicensed 60 GHz band provides extremely large spectrum utilization, which is a promising technique due to the rapid growth of wireless communication services. With wireless devices explosively increasing, the limited number of association and beamforming training (A-BFT) opportunities in conventional protocol leads to high packet collisions. In this paper, we design a novel non-slotted A-BFT frame structure for multi-users in order to alleviate packet collisions providing flexible training slots. To further reduce collision occurrence, we propose two association mechanisms including the time-based distributed coordination function (TDCF) and time-based beam collision avoidance (TBCA) schemes. The theoretical closed-forms of both TDCF and TBCA methods are also derived, and proved to outperform the random association. Furthermore, the adaptive beam search (ABS) and dynamic A-BFT adjustment (DAA) schemes are proposed to consider historical beam training information by adjusting the number of training beams and A-BFT length, respectively. Simulations are conducted to show that our proposed ABS, DAA, and TDCF/TBCA mechanisms outperform existing methods; whereas higher spectrum efficiency can be achieved by proposed non-slotted frame structure compared to conventional slotted structure. [ABSTRACT FROM AUTHOR]

In this paper, hybrid energy utilization was studied for the base station in a 5G network. To minimize AC power usage from the hybrid energy system and minimize solar energy waste, a Markov decision process (MDP) model was proposed for packet transmission in two practical scenarios. In this model, one buffer is transmitted with a given number of data packets over finite transmission time intervals. In the two scenarios of packet transmission, solar energy harvesting (SEH) is stored in the first scene, while the other scene uses the energy immediately. The MDP model determines the best actions and decisions for both scenarios. When considering a finite battery size and finite packet buffer, the MDP model defines the actions, states, state transition probabilities, and cost value for each action. In the first scenario, the received solar energy harvester will not be used if it is not enough to transmit the packets in the buffer. In the second scenario, the received solar energy harvester will be used immediately. In both scenarios, the cost value is the weighted sum of AC power and SEH wastage. The simulation results showed the optimum buffer sizes could be determined for the balance between AC power consumption and SEH wastage based on the cost value of the proposed model. Finally, the numerical results indicated that the proposed MDP model could reduce AC power usage by up to 50%. [ABSTRACT FROM AUTHOR]

This letter focuses on the resource management problem for uplink (UL) of millimeter wave based full-duplex small cell networks with consideration of user equipments’ (UEs’) queue backlog and time-varying channels. The original problem aims to maximize UL UEs’ long-term quality of experience (QoE) under queue stability constraint. To solve the intractability caused by the long-term functions, the considered problem is converted into a series of beam assignment (BA) and rate control (RC) problems in each time slot by leveraging Lyapunov method. Furthermore, novel closed-form solutions (CFSs) for BA and RC policies are derived by considering beam usage constraints; whereas an iterative algorithm is proposed based on the CFSs. Simulation results verify the superiority of the proposed algorithm based on its performance on system delay and QoE. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, augmented reality (AR) is considered a promising technology that combines virtual information such as videos, images, and three-dimensional objects with a real camera view in mobile platforms. Interactive AR further provides human–computer interaction to allow the user to interact with virtual objects on the mobile display. In this paper, we proposed a cloud-based mobile augmented reality interactive system (MARIS), which includes MARIS-I for image target tracking and MARIS-H for hand motion tracking. MARIS-I estimates the position of the image target by adopting a feature-based mean-shift algorithm, which is feasible for real-time applications with its small region feature detection. MARIS-H provides two tracking modes for fingertip and back of hand tracking to enhance user experiences (UX) for interaction. Either the center position for the back of hand or fingertip is first estimated by particle filtering technique, which calculates the weighting of each particle according to hand or fingertip model. Afterward, the contour of the fingertip is estimated by level-set-based contour evolution in the fingertip tracking mode. Furthermore, we implement a device/cloud architecture for the proposed MARIS to decrease memory requirement and computational complexity on the device side. Experimental results show that MARIS including MARIS-I and MARIS-H can outperform other existing methods for image and hand motion tracking, respectively. The proposed MARIS is demonstrated in a picture book to provide fruitful interactive UX for digital learning systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

An indoor base station (BS), such as a remote radio head or home eNodeB, is a cost-effective solution to achieve ubiquitous access and positioning functions in indoor Long-Term Evolution Advanced (LTE-A) networks. In this paper, two distance estimation algorithms adopt received signal strength (RSS) to estimate the corresponding distance between a BS and a mobile station. The statistical inference distance estimation (SIDE) algorithm is proposed to provide a consistent distance estimator when the particle number is larger than an inferential theoretic lower bound given a confidence level and an error constraint. Moreover, the particle-based distance estimation (PDE) algorithm is proposed to estimate distance information with the technique of particle filtering under mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in indoor LTE-A networks. Furthermore, the theoretic Cramér–Rao lower bound (CRLB), considering the variations from fading effects and time-variant channels, is derived as a benchmark to evaluate the precision of distance estimators. The performance of the proposed SIDE algorithm is verified through simulations, and the results fulfill the requirements of different confidence levels and error constraints. Furthermore, the proposed PDE algorithm outperforms other distance estimation schemes and reveals robustness against mixed-sight and time-variant indoor LTE-A networks. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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 Cramér-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. [ABSTRACT FROM PUBLISHER]