With the increasing application of high-stakes decisionmaking application in Federated Learning (FL), ensuring fairness across different populations to prevent biases against certain groups has become crucial. However, achieving group fairness (GF) in FL presents a formidable challenge due to its decentralization, which complicates the global GF estimation by the server. Moreover, distrust and fragility hinder the server from gathering GF values from unreliable clients. This challenge motivates our proposal of OursFed, a provable GF-aware FL framework that integrates a privacy pairbased contract and robust GF estimation method to address issues of distrust and fragility. Methodologically, we categorize client unreliability into two categories: active unreliability stemming from distrust and passive unreliability arising from fragility. To mitigate active unreliability, we design a privacy pair-based contract to guarantee truthful GF reporting, and enhance multivariate analysis by identifying relationships among multiple private data. To counteract passive unreliability, we develop a robust GF estimation using non-parametric techniques to smooth data and estimate probability densities and regression functions, improving per-client GF accuracy under multi-dimensional data perturbation. Theoretically, we demonstrate the efficacy of OursFed by analyzing its convergence, GF stability, and accuracy deviation. Experimentally, evaluations on two real datasets show that OursFed improves GF by 28.61% with at most 2.7% trade-off versus state-ofthe-art baselines, and synthetic experiments further confirm its effectiveness in handling fragility and distrust.

A novel semi-orthogonal transmission method, the pilot data superimposed dynamic inflow (PDSDI) method, is proposed to tackle the challenges of massive access in grant-free random access wireless communication systems. This approach aims to balance large-scale user access while mitigating performance degradation associated with non-orthogonal pilot sequences. The traditional orthogonal transmission scheme is also analyzed as a baseline for comparison. Uplink rate expressions in closed form are obtained under two common channel estimation methods, that is, least squares and minimum mean square error. The analysis investigates the asymptotic behavior of important system parameters, including RiceanK-factor, antenna numbersM, and symbol power, revealing that the sum uplink rate converges to log2M, regardless of theK-factor. In addition, a parameter optimization model is developed to maximize system performance by adjusting critical variables, such as antenna number and power scaling factors, ensuring improved system capacity and efficiency. The simulation results confirm that the PDSDI method greatly exceeds the performance of the traditional scheme with respect to uplink rate and user access capacity. This work presents a practical framework for optimizing system parameters in future large-scale wireless networks.

This article studies the cell-free massive multi-input multi-output (MIMO) two-way relay systems with low-resolution analog-to-digital converters (ADCs). Primarily, we analyze the normalized mean square error (NMSE) and derive a closed-form expression for NMSE’s expectation${\mathrm {Exp}}_{\mathrm {nmse}}$. Asymptotic analysis showcases that, with an infinite number of access point (AP) antennas M or ideal ADCs,${\mathrm {Exp}}_{\mathrm {nmse}}$converges to a finite value. Particularly, as pilot power decreases with M in a power law, the channel estimation performance can be maintained at a desired level. Secondly, we derive two closed-form expressions of spectral efficiencies (SE) for multiple-access channel (MAC) phase and broadcasting (BC) phases, respectively. The corresponding analysis implies that, as AP number$L\rightarrow \infty $, the SE tends to infinity with MAC phase and approaches to constant with BC phase. Interestingly, all SE expressions can reduce to the conventional cases with high-resolution ADCs. Finally, based on geometric programming, an effective power successive approximation (PSA) scheme is provided to maximize the sum SE. Results prove that the proposed PSA scheme can significantly improve the SE compared to baseline benchmarks, particularly for median AP antenna regime. All the derived closed-form expressions are validated through Monte Carlo simulations.

The performance of mean square error (MSE) in channel estimation for multi-cell massive multiple-input multiple-output (MIMO) systems with Rician fading is studied. In this report, we initially derive the closed-form expressions of the probability distribution function and cumulative distribution function of MSE, which are applicable for any number of base-station antennas M and any Rician K-factor. Furthermore, we perform an asymptotic analysis for both strong line-of-sight (LOS) and Rayleigh fading scenarios. Subsequently, we present closed-form expressions for the expectation of MSE $\left(\operatorname{Exp}_{\mathrm{mse}}\right)$ and the variance of MSE. Next, utilizing maximal-ratio combining detector, we investigate the relationship between achievable downlink spectral efficiency and $\operatorname{Exp}_{\mathrm{mse}}$. It is observed that as $\operatorname{Exp}_{\text {mse }}$ increases, the achievable downlink spectral efficiency constantly reduces, eventually reaches a given constant. Finally, Monte-Carlo simulations are performed to corroborate the results discussed earlier.

The Pedestrian Text-Image Retrieval task aims to retrieve the target pedestrian image based on textual description. The primary challenge of this task lies in mapping two heteromodal data (visual and textual descriptions) into a unified feature space. Previous approaches have focused on global or local matching methods. However, global matching methods are susceptible to result in weak alignment, while local matching methods may lead to ambiguous matching phenomenon. In order to address the issues arising from the above methods, we introduce Implicit Neighbourhood Reranking Network (INRNet) which utilizes a bilateral feature extractor to learn global image-text matching knowledge and leverages nearest neighbors as prior knowledge to mine positive samples. Specifically, our proposed approach involves using the bilateral feature extractor to extract features from both texts and pedestrian images and employs a Similarity Distribution Matching (SDM) method to establish preliminary global text-image alignment. Subsequently we establish a Neighborhood Data Construction Mechanism (NDCM), restructuring the data for re-ranking tasks. Finally, we input the restructured data into our Implicit Neighborhood Inference (INI) module, utilizing nearest neighbor intersection to optimize retrieval performance. Through extensive experimentation, our proposed method demonstrates superior performance across three public datasets.

Abstract With the development of automatic driving and path planning technology, predicting the moving trajectory of pedestrians in dynamic scenes has become one of key and urgent technical problems. However, most of the existing techniques regard all pedestrians in the scene as equally important influence on the predicted pedestrian's trajectory, and the existing methods which use sequence‐based time‐series generative models to obtain the predicted trajectories, do not allow for parallel computation, it will introduce a significant computational overhead. A new social trajectory prediction network, Social‐ATPGNN which integrates both temporal information and spatial one based on ATPGNN is proposed. In space domain, the pedestrians in the predicted scene are formed into an undirected and non fully connected graph, which solves the problem of homogenisation of pedestrian relationships, then, the spatial interaction between pedestrians is encoded to improve the accuracy of modelling pedestrian social consciousness. After acquiring high‐level spatial data, the method uses Temporal Convolutional Network which could perform parallel calculations to capture the correlation of time series of pedestrian trajectories. Through a large number of experiments, the proposed model shows the superiority over the latest models on various pedestrian trajectory datasets.

This paper studies the downlink spectral efficiency of a two-way cell-free massive multiple-input multiple-output system with few-bit analog-to-digital converts (ADCs). By utilizing minimum mean squared error channel estimation method and maximal-ratio transmission precoder, we derive a closed-form expression for the effective downlink signal-to-interference-plus-noise ratio (SINR), which applies to any number of access point (AP) antennas M and distortion factors of ADCs in both AP and user pair sides. Additionally, the obtained analytical expression specializes to the conventional one when the ideal ADCs are adopted. Moreover, the asymptotic performance and power scaling law of the effective SINR in high M regime are studied. The corresponding analysis indicates that increasing M can provide considerable gains to compensate the rate loss caused by non-ideal ADCs and also, appropriately cutting down the pilot power and transmission power will not affect the SINR performance in high M region. Finally, all the theoretical results are verified via simulations.

Despite significant improvement in human pose estimation research, most top-performance methods are challenging to deploy in practical applications because of their complex architecture and high computational costs. Although the lightweight human pose estimation approach requires less processing and may be deployed on devices with low resources, such as mobile phones or robots, its network model performance is not exceptional. In this paper, we design the structure based on High-Resolution Network (HRNet), and propose a High-Resolution and Global Context Network (HRGCNet) based on the attention mechanism. Our approach redesigns the bottleneck block according to the attention mechanism of the Global Context Network (GCNet). By combining lightweight and high-performance GC blocks with bottleneck blocks, HRGCNet adds global context features at each location in the high-resolution subnet. The resulting high-resolution representation contains richer feature information. Our experiments on the COCO train2017 dataset show the efficiency of our method. Compared to HRNet with state-of-the-art performance, HRGCNet achieves higher accuracy, and the AP score improves by 2.0 percentage points with similar model size (#Params) and computational complexity (FLOPs). On the COCO test-dev set, HRGCNet has an AP score of 78.3, which is better than most current methods with good performance.

In underwater search and rescue, it is very important for underwater robot to reach the rescue position quickly. Planning path in advance is very important to save rescue time and energy consumption. Therefore, it is meaningful to find a better and shorter path as soon as possible. As a common method of path planning , RRT* has the disadvantages of high cost and slow convergence. To solve these flaws, an improved motion planner for underwater robots is proposed in this paper. In this study, the simulation experiments were divided into two-dimensional conditions and three-dimensional conditions, where used point cloud of real underwater scene to find a better initial solution. Based on RRT*, this paper finds the ancestor node farthest from the sampling point and without collision in the random tree as parent node, adds intermediate nodes in the path according to the step size, and uses trigonometric inequality many times throughout the process, so as to obtain an optimized path. Through a large number of simulation experiments, the results show that the cost of path is less and the convergence speed is faster than RRT* and Q-RRT*.

This paper studies the trade-off between the comunication capacity of unmanned aerial vehicle (UAV) and the UAV's unintentional interference in a shared spectrum scenario. In fact, UAV will also cause unintentional interference to other ground users (GUs) who do not communicate with the UAV while transmitting data to a specific ground node (GN). And the location of these GUs is usually unknown. For this problem, we introduce the concept of unacceptable area in which the interference power received by GUs from UAV exceeds their anti-jamming tolerance, and study the trade-off between UAV's average communication capacity and average unacceptable area by joint UAV's 3D trajectory and transmit power optimization. Further, we propose an effective iterative algorithm to solve this non-convex problem by using successive convex approximation (SCA) and block coordinate descent (BCD) method. Numerical results show the proposed scheme can meet the communication requirements of UAV and reduce the UAV's unintentional interference at the same time.

Vehicular ad hoc network (VANET) has become an accessible technology for improving road safety and driving experience, the problems of heterogeneity and lack of resources it faces have also attracted widespread attention. With the development of software-defined networking (SDN) and multiaccess edge computing (MEC), a variety of resource allocation strategies in MEC-enabled software-defined networking-based VANET (SDVN) have been proposed to solve these problems. However, we note that few of these work involves the situation where SDVN is under Distributed Denial of Service (DDoS) attacks. Actually, Internet of Things (IoT) devices are extremely easy to be compromised by malicious users, and compromised IoT devices may be used to launch edge DDoS attacks against the MEC servers in MEC-enabled SDVN at any time. In this article, we propose a graph neural network (GNN)-based collaborative deep reinforcement learning (GCDRL) model to generate the resource provisioning and mitigating strategy. The model evaluates the trust value of the vehicles, formulates mitigation of edge DDoS attacks and resource provisioning strategies to ensure that the MEC servers can work normally under edge DDoS attacks. In addition, GNN is adopted in the DRL model to extract the structure feature of the graph composed of MEC servers, and help transfer computing tasks between MEC servers to alleviate the problem of resources imbalance between them. Experimental results show that the method of estimating the vehicular trust value is effective, and our method can make the average throughput of edge nodes more stable and lower down the average delay and the average energy consumption under the edge DDoS attack. Also, a real-world case study is conducted to verify our conclusion.

We formulate and investigate the problem of distributed channel rendezvous in collision-prone wireless networks. Existing researches on this topic are mainly devoted to designing channel hopping sequences, each pair of which can overlap on a common channel within bounded delay. However, this overlap-based canonical rendezvous design does not take into account channel collision, which may render existing rendezvous algorithms fail to achieve bounded delay in collision-prone environment. Motivated by this observation, we formulate and investigate the collision-aware channel rendezvous problem in a generic scenario, where a collision occurs if more than$C$packets overlap in time on a same channel. Our generic formulation allows to model both the baseline single packet reception model with$C=1$and the more sophisticated multiple packet reception model with$C > 1$. We further abstract the collision-aware rendezvous problem as the problem of constructing a robust rendezvous system. We establish the theoretical limit of the problem, guided by which we design a collision-resilient distributed rendezvous algorithm with truly bounded rendezvous delay. We then demonstrate the performance of our rendezvous algorithm both analytically and numerically.

As the population increases, problems such as crowds and traffic jams have emerged one after another. How to effectively achieve accurate human flow monitoring has become an urgent problem of today’s society. This paper proposes a multi-task scale adaptive ladder network (MT-SALN) for generating high-accuracy crowd density maps. This network, based on VGG-16 network, consists of several sets of Adaptive Dilated-Convolution Module (ADCM), a Position Recalibration Branch (PRB) and a Density Estimation Branch (DEB). We employ ADCM in different stages to broaden the width of the network and introduce weights for each channel parameter through an attention mechanism. The residual structure enables the network model to have a back propagation ability even though the number of network layers is large. In addition, transposed convolution is used to upsample the features so that they can be merged with other layers’ features to generate a more refined density map with high resolution. The existence of PRB can effectively guide the network to generate crowd density at the correct location and accelerate network convergence. The ladder architecture is beneficial to produce high-quality density maps. Extensive experiments on challenging crowd counting datasets (UCF_CC_50, Shanghaitech) demonstrate the effectiveness of the proposed approach.

The explosive deployment of the Internet of Things (IoT) brings a massive number of light-weight and energy-limited IoT devices, challenging stable wireless access. Energy-efficient, Frame Slotted Aloha (FSA) recently emerged as a promising MAC protocol for large-scale IoT systems such as Machine to Machine (M2M) and Radio Frequency Identification (RFID). Yet the stability of FSA and how to stabilize it, despite of its fundamental importance on the effective operation in practical systems, have not been systematically addressed. In order to bridge this gap, we devote this paper to designing stable FSA-based access protocol (SFP) to stabilize IoT systems. We first design an additive active node population estimation scheme and use the estimate to set frame size and participation probability for throughput optimization. We then carry out theoretical analysis demonstrating the stability of SFP in the sense of geometric ergodicity of Markov chain derived from dynamics of the active node population and its estimate. Our central theoretical result is a set of closed-form conditions on the stability of SFP. We further conduct extensive simulations whose results confirm our theoretical analysis and demonstrate the effectiveness of SFP.

In this paper, by considering the average mean squared error (AMSE) of channel estimation, we primarily obtain the closed-from expressions of the probability density function (PDF) and cumulative distribution function of AMSE for the least squares (LS)/minimum mean squared error (MMSE) estimation method as the line-of-sight (LOS) component is known, where the asymptotic analysis is executed in Rayleigh fading and strong LOS conditions. Secondly, the closed-form expressions for the expectation of AMSE ( Expamse) and variance of AMSE ( Varamse) are acquired, where Varamseis inversely proportional to the number of antennas ( M). As M becomes infinite, the PDF of AMSE at Expamsehas an order of root M. When the pilot power decreases with M in a power law, the LS case keeps deteriorating while the MMSE case converges to a constant which basically depends on the Rician K-factor. Next, the spectral efficiency is investigated by considering AMSE. When Expamseaccelerates, the spectral efficiency of the LS method keeps dropping and that of the MMSE method firstly is degraded and then is improved to a constant except Rayleigh fading. Finally, all results are validated via simulations.

Nowadays, the booming demand of big data analytics and the constraints of computational ability and network bandwidth have made it difficult for a stand-alone agent/service provider to provide suitable information for every user from the large volume online data within the limited time. To handle this challenge, a recommender system (RS) can call in a group of agents to collaborate to learn users' preference and taste, which is known as a distributed recommender system (DRS). DRSs can improve the accuracy of a traditional RS by requesting agents to share information with each other. However, it is challenging for DRSs to make personalized recommendations for each user due to the large amount of candidates. In addition, information sharing among agents raises a privacy concern. Thus, we propose a privacy-preserving DRS in this paper, and then model each service provider as a distributed online learner with context-awareness. Service providers collaborate to make personalized recommendations by learning users' preferences according to the user context and users' history behaviors. We adopt the federated learning framework to help train a high quality privacy- preserving centralized model over a large number of distributed agents which is probably unreliable with relatively slow network connections. To handle big data scenario, we build an item-cluster tree to deal with online and increasing datasets from top to the bottom. We further consider the structure of social network and present an efficient algorithm to avoid more performance loss adaptively. Theoretical proofs show that our proposed algorithm can achieve sublinear regret and differential privacy protection simultaneously for service providers and users. Numerical results confirm that our novel framework can handle increasing big datasets and strike a trade-off between privacy-preserving level and the prediction accuracy.

In this study, vehicle‐to‐vehicle (V2V) radio channel measurements at 5.9 GHz were conducted on a suspension bridge and a beam bridge. Given that a different structure of the two bridges will result in different channel properties, the authors study small‐scale and large‐scale propagation characteristics for the bridge scenarios based on measurement data. By using Akaike's information criteria, the study determines optimal small‐scale fading distribution. Owing to that Ricean distribution occupies the dominant position, Ricean K‐factors in the two bridge scenarios are modelled separately and compared with each other. Afterwards, power delay profiles, Doppler power spectral densities, root‐mean‐square (RMS) delay spread and RMS Doppler spread are estimated and analysed in different cases. They employ bimodal Gaussian mixture distribution (BGMD) to model the RMS delay spreads and the RMS Doppler spreads. Fitting results of the BGMDs show good matching levels. For the large‐scale propagation characteristics, path loss model for the beam bridge scenario is proposed based on the two‐ray theory. On the other hand, path loss for the suspension bridge scenario is modelled by using an empirical function derived from WINNER II. Finally, grey relational grade–mean absolute percentage error, and RMS error criteria prove the effectiveness of the two proposed path loss models.

With rapid development of radio frequency identification (RFID) technology, ever-increasing research effort has been dedicated to devising various RFID-enabled services. The missing tag identification, which is to identify all missing tags, is one of the most important services in many Internet-of-Things applications such as inventory management. Prior work on missing tag detection all rely on hash functions implemented at individual tags. However, in reality hash functions are not supported by commercial off-the-shelf (COTS) RFID tags. To bridge this gap between theory and practice, this paper is devoted to detecting missing tags with COTS Gen2 devices. We first introduce a point-to-multipoint protocol, named P2M that works in an analog frame slotted Aloha paradigm to interrogate tags and collect their electronic product codes (EPCs). A missing tag will be found if its EPC is not present in the collected ones. To reduce time cost of P2M resulted from tag response collisions, we further present a collision-free point-to-point protocol, named P2P that selectively specifies a tag to reply with its EPC in each slot. If the EPC is not received, this tag is regarded to be missing. We develop two bitmask selection methods to enable the selective query while reducing communication overhead. We implement P2M and P2P with COTS RFID devices and evaluate their performance under diverse settings.

With the rapidly growing number of connected smart devices deployed and diverse services provided in the Internet of Things (IoT), selecting proper services for users is becoming more and more important. However, challenges exist as a result of the highly heterogeneous environments, characteristics of various kinds of users and the myriad services offered by many service providers, which have promising applications in the IoT era. In the meantime, users' contexts (e.g., location, time, and surroundings) are wildly utilized in the IoT scenario to better satisfy individuals' demands, raising privacy issues among people. To address these problems, we proposed a differentially private tree-based contextual online learning approach for IoT service selection to select suitable services for users. Leveraging on the historical records of services' and users' feedback, our algorithm achieves high prediction accuracy. Besides, instead of considering the services as individual items, we utilize a top-down cover tree structure to select services, which supports increasing large-scale dataset and diverse natural conditions. We theoretically prove that the accumulative regret of our approach has a sublinear bound and our experiment confirms that it can handle big data problems while achieving a balance between privacy-preserving level and service selection accuracy.

In decentralised cognitive radio (CR) networks, establishing communication sessions between a communicating pair requires them to meet each other on a common channel via a ‘rendezvous’ process. Devising distributed CR rendezvous protocol is a challenging task as cognitive nodes are not necessarily synchronised, and may have different perceptions of channel availability. In this study, the authors present M‐Rendezvous , an order‐optimal rendezvous protocol exploiting the performance gain brought by having multiple radios at cognitive nodes. As a distinguished feature, M‐Rendezvous is a unified rendezvous protocol that can operate in both homogenous case where both of the rendezvous nodes are equipped with only one radio or multiple radios, and heterogeneous case where one of the rendezvous nodes has single radio and the other has multiple radios. In both cases, by rigorous analysis, the authors demonstrate that M‐Rendezvous can guarantee rendezvous over every channel with bounded and order‐minimal delay even when rendezvous nodes have asynchronous clocks and asymmetrical channel perceptions.

The explosive growth of multimedia contents (MCs) in today's mobile social networks has pushed edge computing to face severe security and online big data-processing problems. On the one hand, the edge nodes (ENs) should help mobile users find, cache, and share MCs in the presence of an ever-increasing scale of multimedia big data. On the other hand, how to provide secure MC retrieval schemes to excludedishonest-and-maliciousuntrusted ENs and to prevent privacy breaches fromhonest-but-curiousENs and users is a challenging issue. To tackle these problems, we study the privacy-preserving and trustworthy MCs retrieval system to make personalized MC recommendations from ENs to users with big data support. In our framework, each EN is modeled as a distributed context-aware online learner. ENs collaborate to learn users’ preferences based on their contexts and previous behaviors and social intimacy. To support big data analytics, we establish an MC-cluster tree from top to the bottom to handle the dynamically varying cached MC datasets. A differentially private algorithm is proposed to preserve the data privacy among honest-but-curious ENs and users. To guarantee trustworthy edge computing, a trust evaluation mechanism is designed to evaluate the reliability of ENs. We further consider the structure of edge networks to improve the performance of our algorithm. Experimental results validate that our new framework can support increasing multimedia big datasets while striking a balance among privacy-preserving level, Trustworthy level, and caching MC prediction accuracy.

Tag search, which is to find a particular set of tags in a radio frequency identification (RFID) system, is a key service in such important Internet-of-Things applications as inventory management. When the system scale is large with a massive number of tags, deterministic search can be prohibitively expensive, and probabilistic search has been advocated, seeking a balance between reliability and time efficiency. Given a failure probability$\frac {1}{\mathcal {O}(K)}$, where$K$is the number of tags, state-of-the-art solutions have achieved a time cost of$\mathcal {O}(K \log K)$through multi-round hashing and verification. Further improvement, however, faces a critical bottleneck of repetitively verifying each individual target tag in each round. In this paper, we present an efficient tree-based tag search (TTS) that approaches$\mathcal {O}(K)$through batched verification. The key novelty of TTS is to smartly hash multiple tags into each internal tree node and adaptively control the node degrees. It conducts bottom–up search to verify tags group by group with the number of groups decreasing rapidly. Furthermore, we design an enhanced tag search scheme, referred to as TTS+, to overcome the negative impact of asymmetric tag set sizes on time efficiency of TTS. TTS+ first rules out partial ineligible tags with a filtering vector and feeds the shrunk tag sets into TTS. We derive the optimal hash code length and node degrees in TTS to accommodate hash collisions and the optimal filtering vector size to minimize the time cost of TTS+. The superiority of TTS and TTS+ over the state-of-the-art solution is demonstrated through both theoretical analysis and extensive simulations. Specifically, as reliability demand on scales, the time efficiency of TTS+ reaches nearly 2 times at most that of TTS.

We revisit the opportunistic scheduling problem in which a server opportunistically serves multiple classes of users under time-varying multi-state Markovian channels. The aim of the server is to find an optimal policy minimizing the average waiting cost of those users. Mathematically, the problem can be recast to a restless multiarmed bandit one, and a pivot to solve restless bandit by the Whittle index approach is to establish indexability. Despite the theoretical and practical importance of the Whittle index policy, the indexability is still open for opportunistic scheduling in the heterogeneous multi-state channel case. To fill this gap, we mathematically identify a set of sufficient conditions on a channel state transition matrix under which the indexability is guaranteed and consequently, the Whittle index policy is feasible. Furthermore, we obtain the closed-form Whittle index by exploiting the structural property of the channel state transition matrix. For a generic channel state transition matrix, we propose an eigenvalue-arithmetic-mean scheme to obtain the corresponding approximate matrix which satisfies the sufficient conditions, and consequently can get an approximate Whittle index. This paper constitutes a small step toward solving the opportunistic scheduling problem in its generic form involving multi-state Markovian channels and multi-class users.

We consider the multichannel opportunistic access problem, in which a user decides, at each time slot, which channel to access among multiple Gilbert-Elliot channels in order to maximize his aggregated utility (e.g., the expected transmission throughput) given that the observation of channel state is error-prone. The problem can be cast into a restless multiarmed bandit problem which is proved to be PSPACE-Hard. An alternative approach, given the problem hardness, is to look for simple channel access policies. Whittle index policy is a very popular heuristic for restless bandits, which is provably optimal asymptotically and has good empirical performance. In the case of imperfect observation, the traditional approach of computing the Whittle index policy cannot be applied because the channel state belief evolution is no more linear, thus rendering the indexability of our problem open. In this paper, we mathematically establish the indexability and establish the closed-form Whittle-index, based on which index policy can be constructed. The major technique in our analysis is a fixed point based approach which enable us to divide the belief information space into a series of regions and then establish a set of periodic structures of the underlying nonlinear dynamic evolving system, based on which we devise the linearization scheme for each region to establish indexability and compute the Whittle index for each region.

We investigate the opportunistic scheduling problem where a server opportunistically serves multiple classes of users under time varying multi-state Markovian channels. The aim of the server is to find an optimal policy minimizing the average waiting cost of users. Mathematically, the problem can be cast to a restless bandit one, and a pivot to solve restless bandit by index policy is to establish indexability. We mathematically propose a set of sufficient conditions on channel state transition matrix, and consequently, the index policy is feasible. Our work consists of a small step toward solving the opportunistic scheduling problem in its generic form involving multi- state Markovian channels and multi-class users.

We consider the channel access problem arising in opportunistic scheduling over fading channels, cognitive radio networks, and server scheduling. The multi-channel communication system consists of N channels. Each channel evolves as a time-nonhomogeneous multi-state Markov process. At each time instant, a user chooses M channels to transmit information, and obtains some reward, i.e., throughput, based on the states of the chosen channels. The objective is to design an access policy, i.e., which channels should be accessed at each time instant, such that the expected accumulated discounted reward is maximised over a finite or infinite horizon. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem, which is PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity. Hence, a natural alternative is to consider the easily implementable myopic policy that only maximises the immediate reward but ignores the impact of the current strategy on the future reward. In this paper, we perform an analytical study on the performance of the myopic policy for the considered RMAB problem, and establish a set of closed-form conditions to guarantee the optimality of the myopic policy.

Radio frequency identification technology has been widely used in missing tag detection to reduce and avoid inventory shrinkage. In this application, promptly finding out the missing event is of paramount importance. However, the existing missing tag detection protocols cannot efficiently handle the presence of a large number of unexpected tags whose IDs are not known to the reader, which shackles the time efficiency. To deal with the problem of detecting missing tags in the presence of unexpected tags, this paper introduces a two-phase Bloom filter-based missing tag detection (BMTD) protocol. The proposed BMTD exploits Bloom filter in sequence to first deactivate the unexpected tags and then test the membership of the expected tags, thus dampening the interference from the unexpected tags and considerably reducing the detection time. Moreover, the theoretical analysis of the protocol parameters is performed to minimize the detection time of the proposed BMTD and achieve the required reliability simultaneously. In addition, we derive a critical threshold on the unexpected tag size for the execution of first phase in BMTD. Extensive experiments are then conducted to evaluate the performance of the proposed BMTD. The results demonstrate that the proposed BMTD significantly outperforms the state-of-the-art solutions.

We formulate and study a missing tag detection problem arising in multiple-group, multiple-region radio frequency identification (RFID) systems, where a mobile reader needs to detect whether there is any missing event for each group of tags. The problem we tackle is to devise missing tag detection protocols with minimum execution time while guaranteeing the detection reliability requirement for each group. By leveraging the technique of Bloom filter, we develop a suite of three missing tag detection protocols, each decreasing the execution time compared to its predecessor by incorporating an improved version of the Bloom filter design and parameter tuning. By sequentially analyzing the developed protocols, we gradually iron out an optimum detection protocol that works in practice.

We consider the scheduling problem concerning N projects. Each project evolves as a multi-state Markov process. At each time instant, one project is scheduled to work, and some reward depending on the state of the chosen project is obtained. The objective is to design a scheduling policy that maximizes the expected accumulated discounted reward over a finite or infinite horizon. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem that is PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity. A natural alternative is to consider the easily implementable myopic policy that maximizes the immediate reward. In this paper, we perform an analytical study on the considered RMAB problem, and establish a set of closed-form conditions to guarantee the optimality of the myopic policy.

We consider the channel access problem arising in opportunistic scheduling over fading channels, cognitive radio networks, and server scheduling. The multi-channel communication system consists of N channels. Each channel evolves as a time-nonhomogeneous multi-state Markov process. At each time instant, a user chooses M channels to transmit information. Some reward depending on the states of the chosen channels is obtained for each transmission. The objective is to design an access policy that maximizes the expected accumulated discounted reward over a finite or infinite horizon. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem with PSPACE-hardness. A natural alternative is to consider the easily implementable myopic policy. In this paper, we perform an theoretical analysis on the considered RMAB problem, and establish a set of closed-form conditions to guarantee the optimality of the myopic policy.

Tag population estimation has recently attracted significant research attention due to its paramount importance on a variety of radio-frequency identification (RFID) applications. However, most, if not all, of the existing estimation mechanisms are proposed for the static case where tag population remains constant during the estimation process, thus leaving the more challenging dynamic case unaddressed, despite the fundamental importance of the latter case on both the theoretical analysis and the practical application. In order to bridge this gap, we devote this paper to designing a generic framework of stable and accurate tag population estimation schemes based on the Kalman filter for both the static and dynamic RFID systems. Technically, we first model the dynamics of RFID systems as discrete stochastic processes and leverage the techniques in the extended Kalman filter and cumulative sum control chart to estimate tag population for both the static and dynamic systems. By employing the Lyapunov drift analysis, we mathematically characterize the performance of the proposed framework in terms of estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters under which our scheme can stabilize around the real population size with bounded relative estimation error that tends to zero with exponential convergence rate.

Mobile data offloading is an emerging technology to avoid congestion in cellular networks and improve the level of user satisfaction. In this paper, we develop a distributed market framework to price the offloading service, and conduct a detailed analysis of the incentives for offloading service providers and conflicts arising from the interactions of different participators. Specifically, we formulate a multileader multifollower Stackelberg game (MLMF-SG) to model the interactions between the offloading service providers and the offloading service consumers in the considered market framework, and investigate the cases where the offloading capacity of APs is unlimited and limited, respectively. For the case without capacity limit, we decompose the followers' game of the MLMF-SG (FG-MLMF-SG) into a number of simple follower games (FGs), and prove the existence and uniqueness of the equilibrium of the FGs from which the existence and uniqueness of the FG-MLMF-SG also follows. For the leaders' game of the MLMF-SG, we also prove the existence and uniqueness of the equilibrium. For the case with capacity limit, by considering a symmetric strategy profile, we establish the existence and uniqueness of the equilibrium of the corresponding MLMF-SG, and present a distributed algorithm that allows the leaders to achieve the equilibrium. Finally, extensive numerical experiments demonstrate that the Stackelberg equilibrium is very close to the corresponding social optimum for both considered cases.

We develop a distributed market framework to price the offloading service, and conduct a detailed analysis of the incentives for offloading service providers and conflicts arising from the interactions of different participators. Specifically, we formulate a multi-leader multi-follower Stackelberg game (MLMF-SG) to model the interactions between the offloading service providers and the offloading service consumers in the considered market framework, and investigate the cases where the offloading capacity of APs is unlimited and limited, respectively. For the case without capacity limit, we decompose the followers' game of the MLMF-SG (FG-MLMF-SG) into a number of simple follower games (FGs), and prove the existence and uniqueness of the equilibrium of the FGs from which the existence and uniqueness of the FG-MLMF-SG also follows. For the leaders' game of the MLMF-SG, we also prove the existence and uniqueness of the equilibrium. For the case with capacity limit, by considering a symmetric strategy profile, we establish the existence and uniqueness of the equilibrium of the corresponding MLMF-SG, and present a distributed algorithm that allows the leaders to achieve the equilibrium. Finally, extensive numerical experiments demonstrate that the Stackelberg equilibrium is very close to the corresponding social optimum for both considered cases.

The authors consider a cognitive radio network overlaying on top of a legacy primary network in which a secondary user is allowed to access primary channel by overhearing feedback signals over the primary channels. Each channel is assumed to be a two state Makovian process. Aiming at maximising the expected accumulated discounted network throughput, the considered sequential decision‐making problem can be cast into a restless multi‐armed bandit (RMAB) problem which is well‐known to be PSPACE‐hard, and thus a natural alternative approach is to seek a simple myopic policy. This study presents a theoretical study on the optimality of the proposed myopic policy for the special RMAB problem by considering four different cases: negatively correlated homogeneous channels, heterogeneous channels, positively correlated heterogeneous channels and negatively correlated heterogeneous channels. More specifically, the authors establish the closed‐form conditions to guarantee the optimality of the myopic policy for the four cases, respectively, which, combined with the case of positively correlated homogeneous channels, constitute a complete paradigm for the optimality of the myopic policy.

In this paper, we consider the probing order and stopping problem arising from the identification of spectrum holes in multi-channel cognitive radio networks, in which a secondary user (SU) seeks to maximize the probability of finding an available channel while minimizing the related probing cost within a long time horizon. This problem can be casted into a restless multi-armed bandit problem, which is proved to be PSPACE-hard. The key point of this problem is the trade-off between exploitation, in which the SU stops probing once an available channel is identified, and exploration, in which the SU continues to probe new channels even after identifying an available channel in order to learn the system state to reduce probing cost in the future. To strike a desirable balance between the two conflicting objectives, we develop a heuristic channel probing policy, termed the v-step lookahead policy, in which the SU makes its decision based on the prediction of system state within the future v steps, with v being a tunable parameter. We conduct an analytical study on the structure of the proposed v-step lookahead policy and demonstrate how the policy can be implemented with linear complexity with respect to the number of channels in the system via a detailed analysis on the 1-step lookahead policy. Numerical experiments between the v-step lookahead policy and myopic probing policy on two representative network scenarios demonstrate the effectiveness of the proposed v-step lookahead policy.

We consider the channel access problem in a multi-channel opportunistic communication system with imperfect channel sensing, where the state of each channel evolves as an independent and identically distributed Markov process. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem that is of fundamental importance in decision theory. It is well-known that the optimal policy of RMAB problem is intractable for its exponential computation complexity. A natural alternative is to consider the easily implementable myopic policy that maximizes the immediate reward but ignores the impact of the current strategy on the future reward. In this paper, we perform an analytical study on the optimality of the myopic policy under imperfect sensing for the considered RMAB problem. Specifically, for a family of generic and practically important utility functions, we establish the closed-form conditions to guarantee the optimality of the myopic policy even under imperfect sensing. Despite our focus on the opportunistic channel access, the obtained results are generic in nature and are widely applicable in a wide range of engineering domains.

In this study, the authors consider the restless multi-armed bandit problem, which is one of the most well-studied generalisations of the celebrated stochastic multi-armed bandit problem in decision theory. However, it is known to be PSPACE-Hard to approximate to any non-trivial factor. Thus, the optimality is very difficult to obtain because of its high complexity. A natural method is to obtain the greedy policy considering its stability and simplicity. However, the greedy policy will result in the optimality loss for its intrinsic myopic behaviour generally. In this study, by analysing one class of so-called standard reward function, the authors establish the closed-form condition about the discounted factor β such that the optimality of the greedy policy is guaranteed under the discounted expected reward criterion, especially, the condition β=1 indicating the optimality of the greedy policy under the average accumulative reward criterion. Thus, this kind of standard reward function can easily be used to judge the optimality of the greedy policy without any complicated calculation. Some examples in cognitive radio networks are presented to verify the effectiveness of the mathematical result in judging the optimality of the greedy policy.