WiFi-based passive indoor localization has gained prominence owing to its high accuracy and ease of deployment in GPS-denied environments. However, Channel State Information (CSI)-based systems face challenges, including high data acquisition requirements, significant computational overhead, and limited transferability. In this paper, we introduce DBLoc, a WiFi localization system that leverages beamforming feedback information (BFI), a novel attribute provided by modern WiFi hardware. BFI’s clear-text transmission and stable characteristics make it an ideal choice for localization tasks. We prove that BFI provides a lightweight alternative to CSI, significantly reducing both data acquisition and storage requirements. Compared with traditional deep learning frameworks using convolutional networks, DBLoc employs a pruning-based residual architecture to reduce computational overhead, achieving an inference cost of only 175.7 MFLOPs, thus optimizing performance within an edge-deployment budget. To enable transferability that surpasses current meta-learning approaches, DBLoc incorporates a virtual-domain-based meta-learning algorithm, ensuring robust performance with minimal target-domain data. Additionally, a spatial-encryption mechanism is proposed to safeguard the BFI-based model from eavesdropping. Extensive evaluations demonstrate that DBLoc achieves a median localization error of approximately 0.5 m, while significantly reducing localization accuracy for unauthorized attackers.

In this paper, we propose a three-dimensional (3D) geometry-based stochastic model (GBSM) for double-reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) air-to-ground (A2G) communication systems. We develop the GBSM for dual-RIS channels, where RIS arrays are strategically mounted on unmanned aerial vehicles (UAVs) to reflect signals from the UAV transmitter towards the ground receiver via cascaded RIS links. The flexible trajectories and on-demand deployment of UAVs effectively mitigate the degradation caused by obstructive elements like buildings and trees. Furthermore, we incorporate a beam-domain channel model (BDCM) into the geometric framework to systematically analyze its propagation framework. This approach reduces computational complexity and enables systematic analysis of the system’s propagation mechanisms. The model captures dynamic behaviors in realistic scenarios by integrating real-time kinematic parameters, including velocities and accelerations of the UAV transmitter, ground receiver, and RIS-mounted UAVs. Key propagation characteristics, such as cross-correlation functions (CCFs), autocorrelation functions (ACFs), frequency correlation functions (FCFs), and channel capacity, are analyzed by comparing the proposed beam-domain approach with conventional geometric methods. Simulation results demonstrate that the statistical properties obtained from the beam-domain channel model closely match those derived from the geometry-based stochastic model, validating the accuracy of the proposed approach. Moreover, the beam-domain method significantly reduces computational complexity over traditional geometric techniques, offering valuable insights for designing efficient distributed RIS-assisted A2G communication systems.

The rapid advancements and widespread application of deep neural networks (DNNs), coupled with their reliance on sensitive and private data, have sparked growing concerns regarding data privacy and the ''right to be forgotten''. To address these concerns, machine unlearning has been proposed to efficiently eliminate the influence of specific training data from trained DNNs. However, existing machine unlearning methods struggle with the large number of parameters in trained DNNs, which lead to slow execution and high memory consumption, making them impractical for large-scale models. In this paper, we shift our focus to the small set of weights in the final classification layer of DNNs, which are defined as as ''prototypes'' for different classes. Our key observation is that the prototype associated with the unlearned training data undergoes a significant shift, whereas prototypes of unrelated classes exhibit only minor changes when comparing the prototypes of original and retrained models. Based on this observation, we propose a novel machine unlearning approach that efficiently achieves machine unlearning by directly adjusting the prototypes of DNNs. We first introduce Naive Prototype Surgery (Naive PS), a fast and simplified method that uses a closed-form solution to approximate unlearning effect by directly adjusting the prototype associated with the unlearned data. Next, we propose Prototype Surgery (PS), which incorporates soft label information to fine-tune the prototypes of all classes, to achieve a more effective unlearning. Both methods achieve data unlearning by only modifying the prototypes in the DNNs, thus avoiding the challenges posed by the large number of model parameters. Extensive experiments on four datasets demonstrate that our methods significantly accelerate the unlearning process while achieving comparable results to five existing methods in terms of both unlearning performance and privacy guarantee.

Mobile Edge Computing (MEC) holds excellent potential in Congestion Management (CM) of 6G vehicular networks. A reasonable schedule of MEC ensures a more reliable and efficient CM system. Unfortunately, existing parallel and sequential models cannot cope with scarce computing resources and constrained channels, especially during traffic rush hour. In this paper, we propose a channel-constrained multi-core sequential model (CCMSM) for task offloading and resource allocation. The CCMSM incorporates a utility index that couples system energy consumption and delay, applying Genetic Algorithm combining Sparrow Search Algorithm (GA-SSA) in the branching optimization. Furthermore, we prove that the system delay is the shortest with the FCFS computing strategy in the MEC server. Simulation demonstrates that the proposed CCMSM achieves a higher optimization level and exhibits better robustness and resilient scalability for traffic spikes.

As one of the promising self-powered sensors on Internet of Things (IoT) platforms, unmanned aerial vehicles (UAVs) have attracted much attention for parcel delivery. Their high flexibility and low cost facilitate last-one-mile delivery. However, the limitations of battery capacity and payloads prevent drones from delivering independently over large scales. In this case, it is available to employ vehicles to assist the drones. The vehicles can be private-own trucks and vehicles in public transportation systems (PTSs). Compared to trucks, PTSs, such as buses and trains, do not require extra operating and fuel costs. Given these advantages, this article adopts PTSs to assist UAVs in parcel delivery. Nevertheless, the fixed routes and schedules of public vehicles pose new challenges to the routing and scheduling problem for PTS-assisted multidrone parcel delivery (RSPMD). To tackle the problem, we propose a novel routing and scheduling algorithm, referred to as the PTS-assisted multidrone parcel delivery (PDD) algorithm. Considering the schedules of the public vehicles, the algorithm jointly optimizes the distance and time cost of drones by iteratively combining parts of existing routes. To the best of our knowledge, we are the first to address RSPMD in which UAVs ride public vehicles to deliver parcels in a wide area. Simulation results are finally presented to demonstrate that PDD outperforms existing solutions in terms of effectiveness and efficiency.

Device-to-device (D2D) communication is a key enabling technology to facilely realizing the Internet of Things (IoT) due to its spectral and energy efficiencies features. Exploiting the physical-layer network coding (PNC) and energy harvesting (EH) technology, two-way relaying (TWR) D2D communication can achieve significant performance for IoT in terms of data rate and energy efficiency (EE). In this article, we investigate the EH-aided TWR D2D communication sharing the uplink (UL) spectrum of the traditional cellular networks. We assume that the D2D transmitters, receivers, and participating relays can collect renewable energy (RE) from natural resources. Also, the relays are considered to be powered by radio-frequency (RF) signals utilizing the power splitting (PS) protocol. Subject to the Quality of Service (QoS), power, subchannel assignment, EH, and maximum practical power constraints, two nonconvex mixed-integer nonlinear programming (MINLP) problems are formulated. The two problems provide a tradeoff on either maximizing the TWR D2D link (TDL) rate or its EE depending on the IoT application needs. Based on the particle swarm optimization (PSO) algorithm, we propose the rate and EE tradeoff EH-based algorithm (REET-EH) to deal with these problems. The proposed algorithm can optimally perform the resource allocation (RA), PS factors determination, power allocation (PA), and relay selection processes. The numerical results investigate the performance of the REET-EH algorithm and show its consistency over several parameters. Also, the results illustrate that our proposed algorithm improves the system performance compared with other state-of-the-art algorithms with regard to the D2D link rate and EE.

As the feature size of CMOS technology rapidly shrinks into the very deep submicrometer, the power density is getting higher and the circuit's reliability is seriously impacted by thermal noise. In order to reduce power dissipation, decreasing the supply voltage is a promising way. However, soft errors and decreased supply voltage make circuits behave probabilistic. Fortunately, some applications can tolerate a certain level of operation errors. The tradeoff between energy and reliability provides an opportunity to save the power consumption in the circuit. An asymptotically linear analysis (ALA) strategy is proposed to model the reliability evaluation of combinational logic circuits. Under the assumption that the gate error probability is not more than 0.05, an asymptotic linear matrix A is constructed by using the first-order term of the output error probability. When propagating the gate error probability from the input to the output ports, the ALA strategy only requires one multiplication between the matrix A and the gate error probability vector. There are significant differences in the complexity of computation of the ALA scheme and the Bayesian network (BN) and the probability transfer matrix (PTM) schemes, while its relative error (RE) is no more than 0.08 on the benchmark of LGSynth91. Furthermore, three schemes are proposed to adjust the gate error probability to meet the constraint on the error probability of the probabilistic CMOS (PCMOS) circuit's outputs. The proposed schemes can be performed to search the gate error probability combinations in the logic and circuit design stage of VLSI. During the power optimization, the switching activity factor in the asymptotically linear region is approximately invariant. It makes the power optimal model convex. The energy-saving ratio of the proposed power optimization scheme is 5%-6% higher than the existing schemes. The time consumption of the proposed three schemes is one-thousandth of the time consumed by the BN and PTM schemes. All the three schemes are verified by conducting experiments on the ISCAS'85 and IWLS'15 benchmark circuits. The simulation results establish the feasibility of the ALA and the performance of the proposed schemes.

The enabling Internet-of-Things (IoT) technology has inspired a large number of novel platforms and applications. One popular IoT platform is unmanned aerial vehicles (UAVs, also known as drone). Benefiting from the intrinsic flexibility, convenience, and low cost, UAVs have great potentials to be utilized in various civil applications, including parcel delivery. However, suffering from limited payloads and battery capacities, it is uneconomical for UAVs to perform parcel delivery tasks independently. To conquer the drawbacks of low payloads and battery capacities, people propose to employ both trucks and drones to construct truck-drone parcel delivery systems. However, previous works only leverage either independent drones or truck-carried drones to collaborate with trucks. In contrast, in this article we propose to simultaneously employ trucks, truck-carried drones, and independent drones to construct a more efficient truck-drone parcel delivery system. We claim that such a hybrid parcel delivery system can fully exploit the complementary benefits of the three platforms. We propose a novel routing and scheduling algorithm, referred to as hybrid truck-drone delivery (HTDD) algorithm, to solve the hybrid parcel delivery problem, wherein M drones carried by M trucks, together with N independent drones, cooperate to deliver parcels to customers distributed in a wide region. The experimental results show that our algorithm outperforms the existing solutions which employ either independent drones or truck-carried drones.

Dynamic Adaptive Streaming over HTTP (DASH) has been widely adopted to deal with such user diversity as network conditions and device capabilities. In DASH systems, the computation-intensive transcoding is the key technology to enable video rate adaptation, and cloud has become a preferred solution for massive video transcoding. Yet the cloud-based solution has the following two drawbacks. First, a video stream now has multiple versions after transcoding, which increases the network traffic traversing the core network. Second, the transcoding strategy is normally fixed and thus is not flexible to adapt to the dynamic change of viewers. Considering that mobile users, who normally experience dynamic network conditions from time to time, have occupied a very large portion of the total users, adaptive wireless transcoding is of great importance. To this end, we propose an adaptive wireless video transcoding framework based on the emerging edge computing paradigm by deploying edge transcoding servers close to base stations. With this design, the core network only needs to send the source video stream to the edge transcoding server rather than one stream for each viewer, and thus the network traffic across the core network is significantly reduced. Meanwhile, our edge transcoding server cooperates with the base station to transcode videos at a finer granularity according to the obtained users' channel conditions, which smartly adjusts the transcoding strategy to tackle with time-varying wireless channels. In order to improve the bandwidth utilization, we also develop efficient bandwidth adjustment algorithms that adaptively allocate the spectrum resources to individual mobile users. We validate the effectiveness of our proposed edge computing based framework through extensive simulations, which confirm the superiority of our framework.

In order to achieve seamless integration between heterogeneous wireless networks on the basis of maintaining good compatibility, this paper designs an innovative heterogeneous wireless convergence protocol stack. Based on the protocol stack, the parallel transmission of heterogeneous wireless networks is studied. The optimized resource allocation strategy is adopted to achieve load balancing between heterogeneous networks and high-speed wireless access at the user end. The cross-layer ARQ method is adopted to guarantee service quality.

Edge computing technology can greatly reduce the network load and the user response delay, which can effectively make up the defect of the cloud computing. However, edge storage nodes have much smaller space than cloud computing, therefore it is necessary to select an appropriate cache replacement policy to replace data that some users do not frequently request in order to cache the newly accessed data. This paper focuses on the problem of data replacement and the corresponding replacement strategy when the edge storage node lacks buffer space. Maximum file utility of system (Max-FUS) caching replacement algorithm are proposed to solve this problem. Finally, the edge computing system model, which is established in order to verify the feasibility of the algorithm by MATLAB simulation experiment, shows the Max-FUS can achieve better performance than the existing methods.

We consider the navigation application of 3D sensor networks that can proactively guide the movement of internal users from potential dangers to a safe exit, where a 3D sensor network serves as a reactive system, instead of a monitoring tool or a medium of data acquisition. Most if not all existing efforts in this line concentrate on 2D cases only, and none of them can be readily applied to 3D sensor networks, posing it a non-trivial challenge to design an effective and light-weight navigation protocol in 3D sensor networks. In this paper, we propose the first location-free, distributed, and scalable navigation protocol that can provide a navigation route for users inside the 3D sensor network with guaranteed safety. More specifically, we formulate the navigation problem as the minimum cumulative exposure problem, and design SNP, a navigation protocol based on the so-called 1-manifold skeleton, which offers a safe path with a near-optimal cumulative exposure to dangers. Extensive simulations validate the effectiveness and efficiency of the proposed algorithm.

In a densely deployed small‐cell network, part of small‐cell base stations (BSs) are activated to serve users while the rest are sleep to save energy. The uplink performance region is studied here for such network employing orthogonal frequency‐division multiple access. The authors characterise the limit of performance region using the Pareto boundary. This boundary describes all achievable performance results of beamformers and power allocation. Enabling BS allocation increases the spatial degree of freedom by cooperative reception but greatly complicates searching boundary of performance region. To overcome the complexity challenge, a fast and customised iterative algorithm is developed to check if an arbitrary given performance target is feasible. Using the proposed feasibility checking, the authors can characterise the Pareto‐optimal boundary of uplink performance with activated BS allocation. The authors further study two types of BS allocation schemes, namely the network‐centric and user‐centric strategies. Comparison of both strategies is illustrated in the authors’ numerical examples.

We study the energy harvesting problem in the Internet of Things with heterogeneous users, where there are three types of single-antenna users: ID users that only receive information, EH users that can only receive energy, and ID/EH users that receive information and energy simultaneously from a multiantenna base station via power splitting. We aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the ID users and ID/EH users by jointly designing the power allocation at the transmitter and the power splitting strategy at the ID/EH receivers under the maximum transmit power and the minimum energy harvesting constraints. Specifically, we first apply the semidefinite relaxation (SDR), zero-forcing (ZF), and maximum ratio transmission (MRT) techniques to solve the nonconvex problems. We then apply the zero-forcing dirty paper coding (ZF-DPC) technique to eliminate the multiuser interference and derive the closed-form optimal solution. Numerical results show that ZF-DPC provides higher achievable minimum SINR than SDR and ZF in most cases.

The two-user multiple-input multiple-output (MIMO) Gaussian Broadcast Channel (BC) with mixed CSIT (i.e., delayed CSIT plus estimated current CSIT) under general antenna configuration is considered in this paper. Unlike the MISO scenario, obtaining tight characterization of the Degrees of Freedom (DoF) region seems difficult for the general MIMO BC settings. Despite that, the inner bound we provide in this paper is believed to be the best achievable bound so far. One novel ingredient of our scheme is the rate-splitting of the interference-encoded symbols, which provides the possible benefit of DoF gain by accommodating the transmission to the asymmetric receivers. Moreover, the outer bound of DoF region is also presented, which coincides our proposed inner bound in some cases.

In this paper, we propose a distributed precoder design algorithm for suppressing the inter-cell interference(ICI) and maximizing the average throughput of a multi-cell multi-user multi-input multi-output(MU-MIMO) system. Motivated by the recent results of the distributed signal-to-leakage-plus-noise ratio(SLNR) model for ICI coordination(ICIC), we design a joint optimization algorithm to achieve the pareto-optimal average system throughput. Existed SLNR-based algorithms only consider downlink optimization problem of one optimized factor, which will bring a restriction to the further improvement of the system performance. In our proposed algorithm, the SLNR-based downlink precoder and the transmitting power factor will be designed from a joint optimization problem, and the closed-form pareto-optimal solutions of both optimized factors can be obtained through matrix analysis and decomposition techniques. Simulation results show that the proposed distributed joint precoder design algorithm can significantly increase the average cell throughput and improve the resource efficiency while effectively reducing the system overhead.

There are now great interests in antenna selection strategy for spatial multiplexing systems for the reason that it can reduce cost and complexity yet retain a large part of benefits of multiple antennas. In this paper, we focus on the transmit antenna selection criterion which is applicable to V-BLAST systems with OSIC detection. Based on sub-optimal sorting and QR decomposition, an approximate performance analysis of each sub-stream for V-BLAST transmission is done, and a new solution to the transmit antenna selection problems is suggested. Unlike most of the existing works, this criterion takes into account the impacts of ordering and cancellation. Simulation results show that the proposed algorithm behaves well both in outage capacity and in BER performance. Furthermore, due to the nature of QR decomposition, the implemental complexity can be reduced further by utilizing Gram-Schmidt orthogonalization.

As a consequence of the time-varying channel, the orthogonality between subcarriers is destroyed in conventional frequency-domain approaches for orthogonal frequency-division multiplexing (OFDM), resulting in interchannel interference, which increase's an irreducible error floor in proportion to the normalized Doppler frequency. In this paper, we propose a combined channel estimation in time domain and frequency domain. In time domain, the channel statistics like the channel correlation matrix can be achieved by PPS sequence, which may be used for frame synchronization in MIMO system. In frequency domain, in order to reduce the complexity of the estimator, we still apply theory of optimal rank-reduction to linear minimum mean-squared error (LMMSE) estimators, This enables us to achieve performance superior to any other structure without increasing bandwidth or incorporating redundancy. The performance is presented in terms of BER and MSE, and confirms that the estimator is robust to changes in channel characteristics.