Integrated sensing and communication (ISAC) emerges as an advanced technology to improve the spectrum efficiency by sharing the same spectrum for both communication and sensing. However, the open nature and the shared spectrum make the privacy a critical issue. Fortunately, covert communication can tackle this issue and provide an additional privacy protection for ISAC. In this paper, we propose a novel multi-user covert ISAC scheme against collusive wardens. Specifically, a dual-functional transmitter senses the wardens while communicating with multiple legitimate users covertly, where the more practical Rician fading is considered. First, we analyze the global detection performance of collusive wardens, where we employ the moment matching to handle the intractable theoretical analysis and computation introduced by Rician fading. Then, we optimize each warden’s detection threshold to achieve the greatest detection, creating the worst scenario for legitimate communication. Under this threat, we maximize the average covert transmission rate through jointly optimizing the power allocation and beamforming. To solve this non-convex optimization problem, semidefinite relaxation and successive convex approximation are adopted to transform it into a convex problem, and a convergence-guaranteed iteration algorithm is developed to obtain the optimal solutions. Simulation results show the superiority of the proposed multi-user covert ISAC scheme while revealing the inherent trade-off among covertness, sensing, and communication.

Low Earth Orbit (LEO) satellite networks are increasingly central to wide-area communication services, yet their capacity is highly sensitive to the inherent vulnerability of satellite and inter-satellite link (ISL) failures. This paper develops an analytical framework for characterizing network capacity under arbitrary failure patterns. Building on bisection cut-set analysis, we formalize capacity bottlenecks through graph partitioning and derive rigorous upper bounds on the network capacity. Within this framework, we further identify and characterize the maximum failure cut set, i.e., a critical subset of ISLs whose removal yields the most severe capacity degradation, and establish its necessity in achieving the worst-case capacity bound. Furthermore, the analytical framework has been shown to be applicable to general LEO networks, and a closed-form expression for capacity degradation has been derived using a 2D torus topology as an example, which takes into account both the number and spatial distribution of the satellite or ISL failure. The results indicate that maximizing the minimum bisection cut set capacity is fundamental to strengthening the intrinsic robustness of the LEO network. This paper provides a theoretical methodology for capacity assessment and offers principled guidelines for topology design in satellite constellations.

In data relay satellite networks (DRSNs), user satellite (US) data is transmitted to ground stations via geostationary (GEO) relay satellites (RSs). As the number of USs increases to enable real-time observation, the limited relay capacity becomes a critical bottleneck. On-board caching and processing at USs before transmission are promising approaches to alleviate relay pressure. However, constrained storage, computation and transmission (SCT) capacities pose significant challenges in meeting stringent delay and reliability requirements. This paper investigates the user capacity of a typical DRSN with integrated SCT processes, which is defined as the maximum number of USs that can be supported under both delay and reliability constraints. These constraints are quantified by delay violation probability (DVP) and data loss probability (DLP), whose expressions are difficult to derive directly due to the inherent coupling of SCT processes. To this end, tight upper bounds of DVP and DLP are derived based on a tandem queuing model with martingale-based analysis, and these bounds demonstrate exponential decay with increasing delay threshold and storage capacity. Based on these insights, a bi-level optimization problem is formulated and a two-step user capacity algorithm is proposed to efficiently obtain the user capacity under joint DVP and DLP constraints. The proposed analysis is conducted using representative DRSN parameters, and the numerical results show that the proposed methods can enhance user capacity by up to 38.2% and reduce computational complexity by around 90%. The results can provide guidance for future DRSN configuration, including satellites deployment and resources allocation.

Driven by diverse applications in the emerging low-altitude economy, modern aerial networks must inherently cater for highly heterogeneous environments, characterized by communication services under mixed service delay constraints and diverse user equipment (UE) mobility. However, such heterogeneity leads to resource allocation conflicts and imbalances, which undermine communication reliability and may result in network unavailability. To address this, we investigate resource management in uplink low-altitude heterogeneous networks. Specifically, we propose a flying access point (FAP)-coordinated multi-point packet delivery mechanism with a unified resource allocation (URA) scheme to efficiently manage spatial, frequency, and temporal resources. This includes subchannel allocation, time slot partitioning, and pilot length design. Then, we derive a lower bound (LB) on network availability (NA) and reveal that extended heterogeneity significantly degrades the LB due to: (a) resource reduction under URA and (b) the independence in ensuring services under heterogeneity. To mitigate this degradation, we derive a closed-form condition on the required number of FAPs by relaxing the LB, thereby ensuring sufficient spatial resources to achieve the target NA. Meanwhile, we derive closed-form expressions for jointly approximating the optimal number of UEs sharing time-frequency resources and the pilot length. This optimization improves resource efficiency for NA by balancing the post-processing signal-to-noise ratio and its associated thresholds to satisfy reliability requirements under heterogeneous conditions. Numerical results validate the analysis and demonstrate that the proposed resource management strategy achieves the target NA under increased heterogeneity, thereby outperforming existing approaches.

The task of unsupervised visible–infrared person re-identification (USL-VI-ReID) aims to retrieve cross-modal pedestrian images without manual annotations. The key challenge lies in achieving semantic alignment to resolve modality bias in the absence of real labels. However, existing methods overly rely on single-modal information in the process of pseudo-label generation without considering cross-modal associations, making it difficult to bridge the modality gap between visible and infrared images. To address these issues, this paper proposes a Bi-level Inter-Modal Modulation Network (BIMM-Net), which employs multi-level cluster structure optimization as a core strategy to drive the establishment of cross-modal semantic associations, ultimately achieving cross-modal alignment at the feature representation level. Specifically, we construct a novel intermediary modality GrayMix from visible images to enhance model robustness against color variations and alleviate modality gaps. To filter out noise in cross-modal matching and establish a shared semantic space between visible and infrared modalities, we further develop a Ternary Pairs Calibration-Convergence module designed for filtering noise from visible-infrared cluster matching, on this basis constructing fused mixture clusters. Building on this mixture cluster space, an Heterogeneous-Isomorphic Alignment Loss is also designed to align the feature distributions of the three modalities, reinforcing cross-modal semantic consistency. In addition, we present a Cross-modal Neighborhood Consistency Clustering method, which facilitates the formation and propagation of cross-modal clusters by selecting high-confidence cross-modal neighbor pairs and refining feature distances. Ultimately, BIMM-Net through the joint modeling of bi-level clustering enables multiple levels to guide each other in refining cross-modal structures, thereby effectively establishing the semantic associations between visible and infrared modalities. Extensive experiments validate the superior performance of the proposed framework, achieving state-of-the-art results in USL-VI-ReID. The source code of this paper is available at: https://github.com/liujuny5920/DIMM-Net.

As quantum machines have scaled up in their number of qubits, significant research has turned towards increasing their fidelity with quantum error correction codes. Although promising results have been shown with the surface code, which only requires near-neighbor connections between qubits, the high qubit overhead of such local codes promises to be problematic. Consequently, recent work has explored non-local quantum LDPC (qLDPC) codes, which have good asymptotic encoding rates. Despite theoretical progress, hardware implementations of these codes have been a longstanding challenge. At the experimental level, demonstrations of movement based communication on atom arrays suggest this is a powerful new primitive to achieve non-local connectivity. Leveraging this, we present a protocol for implementing non-local qLDPC codes in hardware. Our protocol, qSIEVE, is a co-design of such codes with movement in atom arrays. qSIEVE defines a restricted family of qLDPC codes that can be implemented efficiently with systolic movement. We then quantify the utility of qSIEVE in the context of a complete fault tolerant architecture. We compare the cost of implementing benchmark programs in a standard, surface code only architecture and a mixed architecture where data is stored in qLDPC memory with qSIEVE and loaded to surface codes for computation.

Reusing existing terrestrial base stations (TBSs) for ground-to-air (G2A) coverage has emerged as a promising method to enhance communication service for aerial users (AUs). However, due to distinct coverage areas, G2A coverage cannot achieve seamless coverage of ground-to-ground coverage, necessitating flexible TBS beam adjustment to satisfy communication demands of different areas. Moreover, reusing TBSs for G2A coverage inevitably sacrifices coverage performance for ground users (GUs). In this paper, we propose a transmissive reconfigurable intelligent surface (RIS)-enabled coverage method and a time-division beam switching (TDBS) strategy, which allows flexible beam adjustment and simultaneous coverage for AUs and GUs. Specifically, coverage probability (CP) for AUs and GUs is analyzed to evaluate the simultaneous coverage performance. Results show that increasing G2A TBSs enhances CP for AU, which, however, comes at the cost of severely degrading CP for GUs. Moreover, an optimal G2A TBS ratio exists, indicating that simply increasing G2A TBSs is not always effective in coverage improvement. Thus, the TDBS strategy is proposed, where part of G2A TBSs alternately serve AUs and GUs. Simulations demonstrate that the proposed strategy significantly enhances CP for GUs and AUs at high altitudes, with only a slight trade-off in CP for AUs at low altitudes.

Unmanned aerial vehicles (UAVs) can serve as aerial base stations (BSs) to extend the ubiquitous connectivity for ground users (GUs) in the sixth-generation (6G) era. However, it is challenging to cooperatively deploy multiple UAV swarms in large-scale remote areas. Hence, in this paper, we propose a hierarchical UAV swarms structure for 6G aerial access networks, where the head UAVs serve as aerial BSs, and tail UAVs (T-UAVs) are responsible for relay. In detail, we jointly optimize the dynamic deployment and trajectory of UAV swarms, which is formulated as a multi-objective optimization problem (MOP) to concurrently minimize the energy consumption of UAV swarms and GUs, as well as the delay of GUs. However, the proposed MOP is a mixed integer nonlinear programming and NP-hard to solve. Therefore, we develop a K-means and Voronoi diagram based area division method, and construct Fermat points to establish connections between GUs and T-UAVs. Then, an improved non-dominated sorting whale optimization algorithm is proposed to seek Pareto optimal solutions for the transformed MOP. Finally, extensive simulations are conducted to verify the performance of proposed algorithms by comparing with baseline mechanisms, resulting in a 50% complexity reduction.

In this paper, we aim to improve the carbon efficiency (CE) of hybrid energy-supplied cellular networks by jointly optimizing communication and energy resources. The network is powered by both renewable and conventional grid energy. However, the stochastic and intermittent nature of renewable energy causes spatiotemporal mismatches between energy supply and traffic demand, thereby posing a challenge to CE improvement. Moreover, due to the nonlinearity of power amplifiers (PAs) at base stations (BSs), energy dissipation nonlinearly increases with transmit power. As a result, the existing static PA efficiency-based resource allocation may lead to energy inefficiency and CE degradation. On this basis, we formulate a stochastic long-term CE optimization problem that considers PA nonlinearity. Aided by Lyapunov optimization theory, the problem is equivalently transformed into three short-term deterministic subproblems, i.e., traffic flow control, resource allocation, and energy sharing. Leveraging this insight, we propose a queue-aware traffic flow control policy and a second-order cone programming-based resource allocation method to align traffic with PA characteristics. Additionally, a many-to-many stable matching-based energy sharing scheme is developed, where energy-deficient BSs are matched with energy-excessive BSs based on energy loss coefficients. Consequently, energy waste due to energy sharing is reduced, thereby improving CE.

We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the community detection task as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to identify optimal community structures. We implement a multi-level algorithm that iteratively refines community assignments by alternating between QUBO problem setup and QHD-based optimization. Benchmarking shows our method achieves up to 5.49% better modularity scores while requiring less computational time compared to classical optimization approaches. This work demonstrates the potential of hybrid quantum-inspired solutions for advancing community detection in largescale graph data.

Mega-constellation networks (MCNs) based on low Earth orbit (LEO) satellites have become increasingly important due to the high throughput and seamless coverage. However, due to single-event upsets (SEUs) caused by cosmic radiation, satellites will suffer failure, which deteriorates the network throughput. This paper aims to elucidate the relationship between network throughput and the impacts of SEUs. Taking into account the long-term impacts caused by SEUs, we present the availability of satellites based on the reliability theory. Furthermore, we model the effective data rate of inter-satellite links (ISLs) and find that the upper bound of network throughput $C \propto {\left( {\frac{{1 - {e^{ - \kappa {T_p}}}}}{{\kappa \left( {{T_p} + \gamma } \right)}}} \right)^2}\sqrt {{R_o}{R_h}} $, where κ denotes the SEU rate, and Tpis the scrubbing period for SEU mitigation. Roand Rhdenote the data rates of intra-plane ISLs and inter-plane ISLs, respectively. Consequently, the throughput decline caused by SEUs can be mitigated by adjusting the structure of MCNs. Guided by the throughput upper bound, we propose an optimal throughput constellation design algorithm (OTCDA) to enhance the network throughput considering the impact of SEUs. Experimental results illustrate that the proposed OTCDA can achieve the throughput that is only 6.49% lower than the upper bound.

Beam hopping (BH) has emerged as a critical solution for interference mitigation in mega-constellation satellite networks. Traditional ground-based centralized beam scheduling methods become infeasible in mega-constellations due to prohibitive computational complexity and inadequate responsiveness to bursty traffic demands. Given the non-convex and NP-hard nature of the multi-satellite BH optimization problem, we strategically decompose it into two subproblems. Hence the two-stage on-board BH method based on collaborative satellite clusters is proposed in this paper to address the challenges for efficient BH scheduling. The pre-activated cell selection stage is designed with a mechanism for dynamic updating of cell pre-activation probability to maximize the system throughput. In the cell-satellite matching stage, load balancing across satellites is achieved by minimizing inter-satellite load disparities. Simulation results show that the average throughput could be improved by over 10% compared to the baseline. Moreover, the difference in load between satellites is significantly reduced by 26.38%.

High-reliability and low-latency multipoint-to-multipoint (MP2MP) broadcast is essential for disaster rescue. Multi-connectivity (MC) is a promising high-reliable and low-latency communication technique that can effectively exploit diversity gains for reliability by transmitting duplicate data over independent links. However, MP2MP broadcast may significantly increase communication service loads and burstiness, which impedes the fulfillment of low delay and high reliability. In this work, we first analyze the overall delay-constrained reliability over MC. We find that the overall delay-constrained reliability over MC is ineffectively improved by increasing the independent links to exploit more diversity gains as service loads and burstiness increase, which is the drawback that restricts the potential of MC. Then, we propose a traffic shaping-based MC to unlock its potential in enabling high-reliable and low-latency MP2MP broadcast. Specifically, we introduce a traffic shaping factor Λ ∈ (0,1), and reduce service loads and burstiness per link before MC transmission by evenly dividing service packets across independent links to make the delay-constrained reliability per link exceed 1−Λ. Moreover, we optimize Λ to maximize the overall delay-constrained reliability over MC under a given total independent links by balancing reliability per link with diversity gain. Finally, simulations validate our analysis and show that the proposed traffic shaping-based MC is of great potential to enable high-reliable and low-latency MP2MP broadcast.

In multiple-input multiple-output (MIMO) systems, inverse discrete Fourier transform processing induces phase-dependent subcarrier signal aliasing, thereby elevating time-domain peak-to-average power ratio (PAPR). After being amplified by nonlinear power amplifiers (PAs), these signals suffer from severe in-band distortion, which leads to a deterioration in both energy efficiency (EE) and spectral efficiency (SE). To evaluate the impact, we explore the trade-off between SE and EE in MIMO systems with such distortion. Analytical results reveal that increasing input power causes distortion to dominate over the useful signal, thereby compressing the line integral area of the EE-SE trade-off function and leading to a ring-shaped EE-SE trade-off function. It indicates an exponential increase in energy consumption of MIMO systems. To mitigate distortion-induced performance degradation, a signal-to-leakage-plus-noise ratio (SLNR) precoding method is proposed to solve the signal-to-interference-plus-distortion-noise ratio maximization subproblem. We prove that the optimal SLNR-precoding matrix could be obtained when the PAPR on per antenna is equal. Simulation results show that the proposed method expands the integral area of the EE-SE trade-off curve by more than one-fold and improves the system EE by more than 50% compared to the benchmark.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems have received a lot of attention due to its ability to effectively eliminate inter cell interference, thus increasing the downlink data rates. However, the limited number of the pilot sequences leads to imperfect channel state information (CSI), which significantly degrades the performance in CF-mMIMO systems. Rate-Splitting Multiple Access (RSMA) is widely used to address imperfect CSI due to its ability to flexibly manage interference. Nevertheless, it is still unclear how RSMA affects system energy consumption through which parameters when the number of the pilot sequences is limited in CF-mMIMO systems. This uncertainty hinders the design of systems aimed at reducing energy consumption under pilot constraints. In this paper, we derive the closed-form expression for the data rates with imperfect CSI. Specifically, we can find the energy consumption is related to a quadratic function with the number of pilot sequences as the independent variable. Based on the analysis above, a nonconvex optimization problem is formulated to minimize energy consumption. We use pathfollowing algorithm to approximate the data rates as its concave lower bound, obtaining suboptimal solutions through iterative algorithms. Finally, the simulation results demonstrate that using RSMA can save about 20% of energy consumption compared to the baseline solution when the pilot contamination is serious.

Low Earth orbit (LEO) mega-constellations equipped with laser inter-satellite links (LISLs) is an important part of future sixth generation (6G). However, how solar background noise affects LEO mega-constellations remains an open research topic. To this regard, this paper first derives the spatio-temporal distribution of affected LISLs in LEO mega-constellations at a specific moment, based on the characteristics of the impact, such as its location and duration. This distribution is then generalized to account for all moments during the Earth's rotation, considering the positional relationship between the LEO mega-constellations and the Sun. Additionally, we define two key metrics: the maximum number of affected LISLs (MNAL) and the affected duration ratio (ADR) to quantify the impact on the constellations. Several examples are presented to show that the MNAL decreases as the phase factor increases and increases with rising inclination. The ADR, on the other hand, increases with the phase factor, but initially increases and then decreases as the inclination rises. This work offers theoretical insights that can guide the design of future LEO mega-constellations.

We present PartComposer: a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components. Existing methods either struggle with effectively learning fine-grained concepts or require a large dataset as input. We propose a dynamic data synthesis pipeline generating diverse part compositions to address one-shot data scarcity. Most importantly, we propose to maximize the mutual information between denoised latents and structured concept codes via a concept predictor, enabling direct regulation on concept disentanglement and re-composition supervision. Our method achieves strong disentanglement and controllable composition, outperforming subject and part-level baselines when mixing concepts from the same, or different, object categories. Our code is released in https://github.com/Junyu-Liu-Nate/partcomposer.

Space-air-ground integrated networks (SAGINs) hold great promise in delivering ubiquitous aerial access, effectively meeting the demands for large-coverage on-demand services. Moreover, in 6G networks, the integration of Communication, Computation, Caching, and Control (4C) enables seamless connectivity, efficient data processing, optimized content delivery, and intelligent decision-making for next-generation services. However, various components like unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), satellites, and terrestrial networks each face distinct limitations. In this demo, we first showcase a SAGIN-4C-6G platform capable of establishing a high-capacity backhaul link to the core network while ensuring stable and continuous coverage. Experiments demonstrate that the proposed platform can deliver high-speed, on-demand air-to-ground (A2G) coverage with wireless backhaul, extending over an area of up to 100 km2. Beyond communication enhancement and optimization control, we also illustrate the potential for computation and caching services by deploying the proposed SAGIN platform.

Integrated sensing and communication (ISAC) has emerged as a cornerstone technology for achieving seamless coverage in next-generation networks. Moreover, the advent of extremely large-scale multiple-input-multiple-output significantly enhances ISAC’s potential, facilitating innovative applications in near-field (NF) ISAC. Nonetheless, ISAC networks face numerous challenges, with power consumption being one of the most critical concerns. To address this issue, we propose a novel low-power beamforming approach within the coordinated multipoint (CoMP) ISAC framework. Specifically, our approach involves orchestrating base station (BS) cooperation for seamless coverage and synergistically augmenting the sensing beam with the communication beam to reduce power consumption. By utilizing the NF communication theory, we accurately model signal propagation dynamics and formulate a beamforming optimization problem aimed at minimizing transmit power while adhering to transmission rate and object detection constraints, which is a nonconvex second-order cone programming (SOCP) problem. To overcome the nonconvexity of this problem, we propose a successive convex approximation (SCA)-based beamforming optimization algorithm that ensures convergence. Moreover, we propose a fast-converging algorithm that leverages the unique characteristics of both communication channel and sensing array response vector. Simulation results validate the effectiveness of the proposed scheme for the power minimization problem and yield essential design insights.

Immersive communication, including emerging augmented reality, virtual reality, and holographic telepresence, has been identified as a key service for enabling next-generation wireless applications. To align with legacy wireless applications, such as enhanced mobile broadband or ultra-reliable low-latency communication, network slicing has been widely adopted. However, attempting to statistically isolate the above types of wireless applications through different network slices may lead to throughput degradation and increased queue backlog. To address these challenges, we establish a unified QoS-aware framework that supports immersive communication and legacy wireless applications simultaneously. Based on the Lyapunov drift theorem, we transform the original long-term throughput maximization problem into an equivalent short-term throughput maximization weighted by virtual queue length. Moreover, to cope with the challenges introduced by the interaction between large-timescale network slicing and short-timescale resource allocation, we propose an adaptive adversarial slicing (Ad2S) scheme for networks with invarying channel statistics. To track the network channel variations, we also propose a measurement extrapolation-Kalman filter (ME-KF)-based method and refine our scheme into Ad2S-non-stationary refinement (Ad2S-NR). Through extended numerical examples, we demonstrate that our proposed schemes achieve 3.86 Mbps throughput improvement and 63.96% latency reduction with 24.36% convergence time reduction. Within our framework, the trade-off between total throughput and user service experience can be achieved by tuning systematic parameters.

The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or data-driven schemes, the proposed MDDRA framework employs a model-driven Lyapunov queue to accumulate long-term historical mismatch information and a data-driven Graph Radial bAsis Fourier (GRAF) network to predict the traffic variations under incomplete data, and hence eliminates the reliance on high-precision models and complete spatial-temporal traffic data. We establish the universal approximation property of the proposed GRAF network and provide convergence and complexity analysis for the MDDRA algorithm. Numerical experiments validate the performance gains achieved through the data-driven and model-driven components. By analyzing IREE and EE curves under diverse traffic conditions, we recommend that network operators shall spend more efforts to balance the traffic demand and the network capacity distribution to ensure the network performance, particularly in scenarios with large speed limits and higher driving visibility.

integrated sensing and communication (ISAC) is a promising paradigm to alleviate spectrum congestion and facilitate a variety of emerging Internet of Things (IoT) applications. However, the direct links from the ISAC base station (BS) to the users may be blocked due to the obstacles. In this article, we investigate the double-active reconfigurable intelligent surfaces (RISs) assisted ISAC, where two active RISs are used to establish virtual line-of-sight (LoS) links from the ISAC BS to the users. In addition, the sum of the minimum sensing signal-to-interference-plus-noise ratios (SINRs) among multiple targets during a series of time slots is maximized, subject to Quality of Service (QoS) and transmit power constraints, through the joint optimization of transmit, reflection and receive beamforming. We first transform this nonconvex optimization problem in the dynamic environment into a Markov decision process (MDP), and then propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm to solve it. Moreover, to enhance the generalization and stability, we integrate the generative adversarial network (GAN) into the TD3 algorithm and propose a GAN-TD3-based algorithm to handle the beamforming optimization problem. Compared with the TD3-based algorithm, the proposed GAN-TD3-based algorithm achieves the better performance and higher stability at the cost of higher computational complexity and slower convergence speed. Simulation results are presented to verify the effectiveness of our proposed algorithms and the superiority of the active RIS over the passive counterpart.

Due to the high dynamic of unmanned aerial vehicle (UAV), the beam of UAV-mounted aerial base station (ABS) is difficult to align with ground users (GUs) and macro-cell base stations (MBSs), thereby reducing the communication rate. Towards this end, the channel state information of communication is used to assist onboard radar of ABS to sense the locations of GUs and MBSs for beam alignment to increase communication rate. To clarify the mechanism of mutual assistance between sensing and communication, we first derive the fundamental communication rate lower bound of integrated sensing and communication by utilizing the Cramér-Rao Bound. We find that the sensing power, sensing time, and transmit power between GU-ABS and ABS-MBS mutually influence the bounds of their communication rates with the shared frequency between sensing and communication. Accordingly, the maximizing communication rate problem is established by jointly optimizing transmit power, sensing power, and sensing dwell time allocation, which is decoupled into GU-ABS and ABS-MBS resource allocation subproblems. To reduce the computation complexity, a deep reinforcement learning based algorithm is proposed to solve this problem to replace the successive convex approximation technique. The simulation results demonstrate that the proposed approach is effective in maximizing the communication rate.

Critical thinking, which requires logical analyses on the problems and keeping open-minded to others' viewpoints, is a crucial skill when participating in online discussions. While existing works have explored visualizing the components of an argument in a map, i.e., argument map, to support critical thinking tasks, few of them have incorporated educational elements that aim at training critical thinking in online discussion. In this paper, based on a formative study (N = 57), we develop AMQuestioner , a critical thinking training tool that allows question-driven interactions with argument maps automatically extracted from a post thread. In AMQuestioner , users can explore others' claims with a chatbot via suggested questions and conduct critical thinking exercises by answering generated questions related to any claim in the map. A mixed-design study (N=24) reveals that, compared to a baseline tool without question-driven features, participants after training with AMQuestioner demonstrated significantly more improvements in independently writing arguments that are detailed, specific, and relevant to the topic. Participants with AMQuestioner also exhibited a stronger inclination toward open-mindedness to others' arguments during the three-days training process. We discuss design implications for future critical thinking training tools.

In UAV ad hoc networks (UANETs), transmission range of transmitters is a crucial factor in ensuring network capacity. Inadequate adjustment of transmission range may lead to link disconnection or excessive interference when network topology changes, worsening network capacity. In this paper, we study the impact of transmission range on network capacity characterized by spatial throughput (ST) in UANETs under external jamming and design a power control strategy to achieve optimal transmission range (OTR). Specifically, transmitters and jammers are modeled by a three-dimensional Poisson cluster process and a three-dimensional Poisson point process, respectively. Analysis of ST is accordingly given to illustrate the impact of topology changes and jamming. Afterwards, we analyze ST under transmission range and find that ST scales with the transmission range R as$e^{\kappa _{1}R^{3}}\left ({{1-e^{\kappa _{2}R^{3}}}}\right),\left ({{\kappa _{1},\kappa _{2}\lt 0}}\right)$. This indicates that ST first increases and then decreases with the transmission range. In other words, an OTR exists, which maximizes ST, and it is proved that the OTR scales with node density$\lambda $as$\Theta {\left ({{\lambda ^{-\frac {1}{3}}}}\right)}$. Accordingly, we propose a power control strategy implemented at each transmitter to achieve OTR and enhance ST. Simulation results show that ST adopting OTR linearly increases with$\lambda $, and the proposed strategy shows superiority in enhancing ST under intense jamming among other strategies.

The low-earth-orbit (LEO) satellite constellation networks play a vital role due to their potential to provide high throughput in response to the escalating demands of future communication networks. However, inadequate matching between the constellation topology and traffic distribution would result in network congestion, which degrades the throughput of the LEO network. In this paper, we aim to enhance the throughput of LEO networks through constellation design. Especially, to provide a theoretical guideline for topology design, we prove that the achievable throughput capacity upper bound equals$3\sqrt {2N}\left ({{W_{lx}+W_{ly}}}\right)$, where N represents the constellation size, and$W_{lx}$and$W_{ly}$represent the inter- and intra-plane data rates of the inter-satellite link (ISL), respectively. Aided by this, a throughput capacity maximum topology design (TCMTD) algorithm is proposed to determine the constellation parameters and connection relationships. Consequently, the average path length of the data packet transmission can be minimized and the utilization rate of each ISL can be maximized, thereby achieving the throughput capacity upper bound. Furthermore, a throughput capacity enhanced topology design (TCETD) algorithm is proposed to achieve a near-optimal throughput when some ISLs cannot be constructed due to LoS constraints. Both algorithms take into account the constraints including phase factors and LoS constraints. Simulation results conducted using OPNET demonstrate the effectiveness of the proposed algorithms.

Coordinated multi-point (CoMP) is a promising technique to ensure timely and reliable communications. However, flying access point (FAP) CoMP in aerial disaster emergency networks (ADENs) relies on wireless fronthaul, which is unreliable due to performance loss compared to wired fronthaul. Moreover, FAP movement and the existence of no-fly regions (NFRs) may cause dynamic transmission distances between ground users (GUs) and FAPs. Thus, how to robustly ensure timely and reliable communications via FAP CoMP is an urgent problem in ADENs. In this paper, we introduce wireless-fronthaul-effective-factor (WFEF) to reflect the unreliability of FAP CoMP and analyze network availability (NA), which is defined as the probability that delay and reliability requirements of each GU are satisfied. Through analysis, we first reveal WFEF constraints that prevent CoMP from underperforming non-CoMP. Then, givenKGUs served byLFAPs over the same time-frequency resource, we provide conditionC : P(L−K+1)≫ 1 to ensure timely and reliable communications, where P is a function of system parameters including WFEF and NFRs, etc., and delay and reliability requirements. Finally, we derive that adoptingK= [L/2] alleviates NA degradation due to NFRs by balancing the bandwidth of GUs with spatial diversity and multiplexing gains of CoMP, which enhances the robustness of ADENs.

In this paper, we study the robust throughput capacity of multi-connectivity wireless networks when the network encounters zone node failures. In order to reveal the inherent relationship between the robustness of the network structure and the capability of wireless networks to carry information, robust throughput capacity, which is the product of the fraction of served source and destination (S-D) pairs, the number of S-D pairs and feasible throughput, is defined. It is shown that the robust throughput capacity is$\Theta \left ({{\sqrt {\frac {n}{k\log n}}}}\right)$for$\beta \gt 2$and$\Theta \left ({{\frac {1}{k\log n}\sqrt {\frac {n}{k\log n}}}}\right)$for$1 {\lt }\beta \leq 2$, where n is the number of nodes,$\beta $is the failure exponent and$k\:(\geq 1)$is the connectivity parameter representing the number of disjoint data paths between any two nodes. To balance the tradeoff between the throughput capacity and the robustness of the network structure, the feasible regions of connectivity parameters, which are limited by the failure exponent, are given for$\beta \gt 2$and$1\lt \beta \leq 2$, respectively. Correspondingly, the robust throughput capacity is$\Theta \left ({{\sqrt {\frac {n^{1-\gamma }}{\log n}}}}\right)$and$\Theta \left ({{\sqrt {\frac {n^{1-3\gamma }}{\log n}}}}\right)$, respectively, where$\gamma \in [0,1$) is the robustness exponent. These results can provide guidance for designing network protocols with fault tolerance in large-scale wireless networks.

Disasters severely damage cellular network infrastructures, weakening network communication service capability (CSC) and impeding post-disaster efforts. Therefore, evaluating network ability to resist disasters and recovering CSC are crucial. In this paper, we introduce a novel metric, network capacity scalability (NCS), defined by spatial throughput (ST) and its standard deviation to characterize CSC in disasters. By revealing the impact of disasters on NCS, network resistance to disasters can be reflected. Specifically, a critical disaster intensity (CDI) is derived, below which the effect of disasters on NCS is negligible and networks are disaster-resistant. However, NCS rapidly deteriorates once CDI is exceeded, necessitating recovery strategies. In response, we design a CSC compensation strategy where uncrewed aerial vehicle access points (UAPs) are supplemented, and a critical UAP density maximizing NCS is provided. Notably, compared to ST, NCS can more promptly reflect CSC deterioration, enabling rapider strategy implementation. Moreover, we also demonstrate that fluctuating burst service superlinearly worsens NCS, revealing the network’s poor tolerance to burst service. In this light, we propose a coverage adjustment strategy for terrestrial base stations and UAPs. Simulation results show that the negative effect of burst service can be significantly mitigated, which verifies the effectiveness of the proposed strategy.

Integrating aerial base stations (ABSs) with terrestrial base stations (TBSs) represents a promising architecture for future networks. However, challenges arise from the ABS mobility and complex interference, leading to degradation in the coverage performance, including both the average coverage quality and coverage stability. To address these challenges, we investigate the average coverage quality and coverage stability via the first- and second-order statistical properties of network spatial throughput, respectively. Our findings reveal that the inappropriate ABS deployment, especially the antenna beamwidth, causes the average coverage quality deterioration due to the co- and cross-layer interference surge induced by the overlapping coverage between ABSs and TBSs. Additionally, coverage stability experiences degradation due to the ABS mobility, particularly exacerbated by factors such as the large deployment density and circling radius as well as the small flight height and antenna beamwidth of ABSs. Moreover, it is demonstrated that there exists an optimal ABS antenna beamwidth maximizing the coverage performance. On this account, we propose an antenna beamwidth optimization algorithm as well as a time-efficient but low performance loss alternative antenna beamwidth optimization strategy, aimed at mitigating the overlapping coverage-induced interference and reducing the ABS mobility-induced impact, both of which are validated through numerical results.

Aerial disaster emergency networks (DENs) are of great potential to provide instant and long-duration emergency services in disasters by deploying fixed-wing unmanned aerial vehicle (UAV)-mounted flying access points (FAPs). However, the inherent hovering feature of fixed-wing UAVs may lead to frequent handovers and unstable wireless links between ground users (GUs) and FAPs, which degrades network availability (NA). Especially, NA is the performance metric for DENs, which is defined as the probability of satisfying each GU’s delay and reliability requirements. This study evaluates NA in aerial DENs with FAP coordinated multi-point (CoMP) and GU grouping, where no-fly regions (NFRs) exist in disasters. Specifically, FAP CoMP eliminates frequent handovers, stabilizes wireless links, and provides spatial diversity and multiplexing gains. GU grouping provides frequency multiplexing gain by scheduling bandwidth for GUs. Consequently, the interference among GUs can be managed, and GUs’ delay-constrained reliability can be improved in disasters, where the number of GUs is much greater than that of FAPs. Our results reveal that there exists an optimal number of GU groups K∗, which can maximize NA, given the number of FAPs L. The reason is that K∗balances GUs’ bandwidth with spatial diversity and multiplexing gains. Moreover, NFRs are shown to seriously degrade NA without K∗. Therefore, we optimize the number of GU groups and obtain an approximate optimal number of GU groups ${\hat K^{\ast}} = \left\lceil {\frac{L}{2}} \right\rceil $. Simulation results show that adopting ${\hat K^{\ast}}$ can enhance NA by more than one fold and alleviate NA degradation due to NFRs.

In this paper, we aim to minimize the energy consumption of cellular networks by optimizing communication resources. However, due to the nonlinear characteristic of power amplifier (PA) during the process of wireless signal amplification at base stations (BSs), energy will be dissipated exponentially as the transmit power increases. Consequently, the existing static PA efficiency-based resource allocation method will lead to extra energy consumption in cellular networks. To address the problem, a stochastic long-term energy consumption minimization problem considering nonlinear PA is established. Aided by Lyapunov optimization theory, we prove that the original stochastic long-term energy consumption minimization problem can be equivalently transformed into two short-term deterministic subproblems, i.e., traffic flow control subproblem and resource allocation subproblem. Leveraging this insight, a traffic flow control rule determined by energy queue and traffic data queue is proposed, which could reduce energy dissipation by matching traffic flow with transmit power in nonlinear PA. Then, a second-order cone programming-based resource allocation method is proposed to optimize transmit power further, in which a time-sharing formulation and a first-order Taylor expansion are used to relax discrete subcarrier allocation variables and approximate nonlinear power consumption as linearity, respectively. Simulation results show that our proposed algorithm can effectively reduce average energy consumption of the cellular network, especially when BS density is large.

Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors, and their implications on game strategies. In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games. The key idea of our work is to (1) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual-Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies, in terms of temporal, spatial, and their collective impacts respectively; and (3) provide a unified implementation scheme of Feint behaviors in existing MARL frameworks. The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games; and (3) only incur negligible overheads in terms of time consumption.

The limited energy and computing resources of unmanned aerial vehicles (UAVs) hinder the application of aerial artificial intelligence. The utilization of split inference in UAVs garners attention due to its effectiveness in mitigating computing and energy requirements. However, achieving energy-efficient split inference in UAVs remains complex considering of various crucial parameters such as energy level and delay constraints, especially involving multiple tasks. In this paper, we present a two-timescale approach for energy minimization in split inference, where discrete and continuous variables are segregated into two timescales to reduce the size of action space and computational complexity. This segregation enables the utilization of tiny reinforcement learning (TRL) for selecting discrete transmission modes for sequential tasks. Moreover, optimization programming (OP) is embedded between TRL’s output and reward function to optimize the continuous transmit power. Specifically, we replace the optimization of transmit power with that of transmission time to decrease the computational complexity of OP since we reveal that the energy consumption monotonically decreases with increasing transmission time. The replacement significantly reduces the feasible region and enables a fast solution according to the closed-form expression for optimal transmit power. Simulation results show that the proposed algorithm can achieve a higher probability of successful task completion with lower energy consumption.

Combining intelligent reflecting surface (IRS) with an unmanned aerial vehicle to form aerial IRS (AIRS) swarms is an effective way to enable panoramic full-angle reflection, high configuration flexibility and reliable air-ground line-of-sight (LoS) connections, especially in millimeter wave (mmWave) bands. In this paper, we use stochastic geometry to provide a performance analytical framework for AIRS swarm-assisted mmWave cellular networks, where each base station (BS) has an AIRS swarm to assist downlink communications. To capture the swarm property of AIRSs and the dependence between AIRS swarms and BSs, the AIRSs in each swarm are uniformly and randomly distributed in a finite circular area centered on their assisting BS. Incorporating different LoS/non-LoS propagation characteristics, a two-step user association policy is proposed to pursue an efficient communication link between the BS and its users with the assistance of AIRS swarm. We derive the coverage probability and area spectral efficiency (ASE) by considering three types of interference which are directly from interfering BSs, reflected by active AIRSs of other swarms and reflected by the assisting AIRS for the typical user, respectively. The results reveal that AIRS swarms enhance both coverage and ASE performance and have the optimal height and density that maximize the coverage probability in mmWave cellular networks.

In this paper, a mixed-bouncing based channel model with cooperative space-array-time (S-A-T) consistency and space-array-time-frequency (S-A-T-F) non-stationarity is proposed for sixth generation (6G) multiple-unmanned aerial vehicle (multi-UAV) cooperative communication systems with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies. To model the transmission propagation in multi-UAV integrated channels more accurately, the single-bouncing transmissions and multi-bouncing transmissions in multi-UAV integrated channels are simultaneously modeled and quantified by a cooperative cluster density index for the first time. Meanwhile, the transmissions through line-of-sight (LoS), ground reflection, single-bouncing, and multi-bouncing are captured. To jointly mimic cooperative S-A-T consistency and S-A-T-F non-stationarity in the integrated scattering environment (SE), a new cooperative consistent and non-stationary modeling algorithm is developed based on the frequency-dependent path gain, visibility region (VR), and birth-death (BD) survival probability. The channel parameters related to multi-UAVs are also taken into account in the developed algorithm. The corresponding multi-UAV cooperative channel statistical properties are derived by taking mixed-bouncing transmission into account. Meanwhile, the accuracy of the mixed-bouncing based multi-UAV integrated channel model with cooperative S-A-T consistency and S-A-T-F non-stationarity is validated as simulation results match well with ray-tracing results.

Unmanned aerial vehicles mounted aerial base stations (ABSs) are capable of providing on-demand coverage in next-generation mobile communication system. However, resource allocation for ABSs to provide continuous coverage is challenging, since the high dynamic of ABSs and time-varying air-to-ground channel would result in channel state information (CSI) mismatch between resource allocation decision and implementation. In consequence, the coverage of ABSs is discontinuous in spatial-temporal dimensions, i.e., the variance of user rate between adjacent time slots is large. To ensure the coverage continuity, we design a resource allocation method based on deep reinforcement learning (RDRL). Capable of adaptively tuning neural network structures, RDRL could satisfy coverage requirements by jointly allocating subchannels and power for ground users. Meanwhile, the temporal channel correlation is taken into account in the design of reward function in RDRL, which aims to alleviate the influence of CSI mismatch between method decision and implementation. Moreover, RDRL can apply a pre-trained model of previous coverage requirement to current requirement to reduce computation complexity. Experimental results show that the rate variance of RDRL can be reduced by 66.7% and spectral efficiency of RDRL can be increased by 34.7% compared with benchmark algorithms, which ensures the coverage continuity.

Aerial base stations (ABSs) flying in the air inject wireless networks more flexibility and agility beyond ground base stations (GBSs) to respond to spatio-temporal coverage demand. To fully unlock the potential of ABSs, a high-capacity, flexible and dynamic wireless backhaul provision is necessitated and integrated access and backhaul (IAB) architecture comes into the picture. In this paper, we investigate the availability of IAB architecture in dynamic aerial-terrestrial networks in terms of coverage probability (CP) and further explore the feasible region of IAB to promote aerial-terrestrial coverage enhancement. Specifically, the results show that the capability of IAB to promote aerial-terrestrial coverage enhancement would be diminished with the increases of ABSs flight speed and GBS density. The reason is found that relying on fixed GBSs to provide dynamic backhaul for flying ABSs would come with frequent handovers, which degrades network CP. On this account, to make IAB adapt to dynamic aerial-terrestrial networks, a mobility-adaptable IAB scheme is proposed where a distance thresholdLpis set to alleviate the negative effect caused by handovers. WithLpoptimized, the coverage performance of dynamic aerial-terrestrial IAB network is shown to be increased, especially in dense GBS regime.

This paper focuses on leveraging coordinated multi-point (CoMP) to improve the sum downlink rate in air-terrestrial integrated networks. Considering the CoMP transmission, the problem of maximizing the sum downlink rate possesses two-timescale characteristics, i.e., trajectory planning varies of aerial base station in a long-timescale manner whereas CoMP resource allocation varies in a short-timescale manner. To solve it, a two-timescale parallel framework is proposed. Specifically, the initial two-timescale problem is decomposed into multiple single-timescale subproblems via the alternating direction method of multipliers and then all subproblems are parallelly solved. Furthermore, to resolve the high complexity arising from continuous-discrete hybrid variables in each single-timescale subproblem, we propose an online algorithm that embeds optimization programming (OP) into deep reinforcement learning (DRL). Particularly, discrete CoMP cluster variables in sequential time slots are optimized with DRL to eliminate the need for large-scale combinatorial optimization. Then, the continuous resource allocation variables in each time slot are solved by OP, which is embedded between the output and reward of DRL, to speed up the convergence. Simulation results show that the proposed algorithm achieves less than a 7% total downlink data gap while decreasing the computation time by more than an order of magnitude compared with numerical optimization.

In this paper, we consider an unmanned aerial vehicle (UAV)-assisted post-disaster rescue scenario, where UAV-mounted aerial base stations (ABSs) compute tasks related to post-disaster rescue operations while also providing communication services to ground users (GUs). With the limited computation capacity of ABSs, we aim to minimize the task computation queuing delay and ensure the GU communication rate by jointly optimizing ABS-GU association, task offloading, and ABS trajectory. The problem is formulated as a mixed-integer nonlinear program, and a solution is proposed by integrating Lyapunov optimization and actor-critic based deep reinforcement learning. We utilize a model-based successive convex approximation technique in a critic module to acquire an accurate evaluation of actor module output. Simulation results demonstrate the effectiveness of the proposed approach in reducing the task computation queuing delay.

Motion capture has significant applications in fields such as rehabilitation, virtual reality, and beyond. The cost-effective utilization of sparse sensors is crucial. The central challenge lies in achieving full-body motion estimation with a reduced sensor count. This paper proposes a neural network model based on attention mechanism for motion reconstruction with sparse joints. Compared to traditional methods, our method places greater emphasis on the spatial characteristics of motion sequences by using spatial attention, thus having lower reconstruction error, while preserving high visual quality. Furthermore, our method employs two encoders, with each being responsible for motion feature extraction and motion reconstruction, respectively, thereby enhancing robustness when dealing with new data. Experiments also show that our method can handle missing markers problem with low error.

The movement process of organisms contains a large amount of information, which leads to different structure of motion data in different applications. How to construct a data structure to contain more common information of motion is a topic that researchers have been exploring. It is essential to set a series of criteria for the motion data structure to judge whether it can be a good representation of campaign information and fast and efficient readed/writen and calculated by computers before construct it. In this paper, we proposes 12 construction criteria for motion data structures through analysing the main current application scenarios of motion data, the way computer process data and the requirements of deep learning on data. We design a common structure of motion data CSMD based on these 12 criteria, which can be adapted to almost all common application. Our method is 78% faster than traditional methods. We also build a toolkit to process common motion data including C3D, ASF/AMC, BVH, CSMD, etc.

Face recognition using 3D point clouds is gaining growing interest, while raw point clouds often contain a significant amount of noise due to imperfect sensors. In this paper, an end-to-end 3D face recognition on a noisy point cloud is proposed, which synergistically integrates the denoising and recognition modules. Specifically, a Conditional Generative Adversarial Network on Three Orthogonal Planes (cGAN-TOP) is designed to effectively remove the noise in the point cloud, and recover the underlying features for subsequent recognition. A Linked Dynamic Graph Convolutional Neural Network (LDGCNN) is then adapted to recognize faces from the processed point cloud, which hierarchically links both the local point features and neighboring features of multiple scales. The proposed method is validated on the Bosphorus dataset. It significantly improves the recognition accuracy under all noise settings, with a maximum gain of 14.81%.

Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications. The occurrence probability of each path is complex and time variant due to fast changeable altitudes of unmanned aerial vehicles and scattering environments. Considering the A2G channels under urban scenarios, this article presents three novel stochastic probability models for the Line-of-Sight (LoS) path, ground specular (GS) path, and building scattering (BS) path, respectively. By analyzing the geometric stochastic information of 3-D scattering environments, the proposed models are derived with respect to the width, height, and distribution of buildings. The effect of the Fresnel zone and altitudes of transceivers are also taken into account. Simulation results show that the proposed LoS path probability model has good performance at different frequencies and altitudes and is also consistent with existing models at the low or high altitude. Moreover, the proposed LoS and non-LoS path probability models show good agreement with the ray-tracing (RT) simulation method.

Ambient backscatter communication is an energy efficient technique for massive Internet of Things (IoT). Combining with flexibly deployed unmanned aerial vehicles (UAVs), the UAV-aided ambient backscatter communication can establish wireless links for isolated IoT nodes efficiently. In this paper, we investigate the outage performance of the UAV-aided air-ground network with underlaid ambient backscatter communications, where the emitted signals from the air-ground link are leveraged as radio frequency (RF) carrier for ambient backscattering. The air-ground channel is modeled as a probabilistic line-of-sight (LoS) channel with Nakagami-$m$fading. Then, the ground communication is modeled as a non-line-of-sight (NLoS) channel with Rayleigh fading. For the downlink, we derive the expressions of the outage probabilities of the backscatter link and the air-ground link. In addition, the asymptotic cases of infinite transmit power and infinite fading parameter are analyzed. For the uplink, the outage probabilities of the backscatter link and air-ground are analyzed, with the cases of infinite transmit power and fading parameter discussed. Simulation results show that the analytical results match well with the Monte Carlo results, which verifies the effectiveness of the proposed scheme.

Combining with flexibly deployed unmanned aerial vehicles (UAVs) and energy-efficient ambient backscatter communication, the UAV-aided ambient backscatter communication can establish wireless links for isolated IoT nodes efficiently. In this paper, we investigate a UAV air-ground networks with underlaid ambient backscatter communications, where the emitted signal from the UAV is leveraged as radio frequency (RF) carrier for ambient backscattering. First, we establish a system model of the UAV air-ground networks with underlaid ambient backscatter communications. Then, the expressions of the outage probabilities for both the backscatter link and the air-ground link are derived. In addition, the asymptotic outage probabilities of infinite transmit power and infinite fading parameter are analyzed. Simulations show that the analytical results match well with the Monte Carlo results, which verifies the effectiveness of the proposed scheme.

In this paper, we study the impact of node cascading failures and network structure robustness on the capacity of wireless networks. Especially, in order to quantify how much information can be conveyed by wireless networks under node cascading failures, robust capacity is defined to capture the influence of the intensity of the initial failure nodes$m$and the connectivity parameter$k$on capacity. Note that increasing$k$could provide$k$disjoint data paths for any two nodes, thereby combating the cascading failure. Denoting the intensity of the nodes as$n$and$m=n^{\frac{1}{\beta}}$, it is shown that robust capacity$O\left(\sqrt{\frac{n}{k\log n}}\right)$can be achieved when the initial failure exponent$\beta > 2$. In contrast, robust capacity would converge to zero with increasing$n$when$1 < \beta\leq 2$since the data paths of most source-destination pairs are interrupted due to the cascading failures. Moreover, increasing the connectivity parameter$k$, although capable of enhancing the network structure robustness, is shown to degrade cascading failures and robust capacity.

Intelligent reflecting surface (IRS) is a promising technology which can be integrated with non-orthogonal mul-tiple access (NOMA) to improve the secrecy performance. In this paper, we propose an effective IRS-aided secure scheme for NOMA networks against both internal and external eavesdrop-ping. By exploiting artificial jamming (AJ), the transmit power minimization problem of legitimate signals is investigated with both users' QoS demands satisfied via the joint optimization of active and passive beamforming. The non-convex problem is decomposed into two subproblems, which are approximated into convex ones via the semidefinite relaxation (SDR). Then, by means of alternating optimization, the suboptimal solution to the original problem can be obtained. Simulation results demonstrate the superiority of the proposed scheme by combining IRS and AJ against the challenging internal and external eavesdropping.

Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by IBM Qiskit and the qBraid system.

The prosperity of industrialization urges modern ride-sharing platforms to gain profit from efficient management of their resources. Although ride-sharing allows sharing costs and promotes the traffic efficiency by making better use of vehicle capacities, dealing with large amounts of online taxi orders is an inevitable challenge in the current transportation systems, where all drivers have to make immediate and irrevocable decisions about whether to accept current order in a parallel way. Furthermore, in order to achieve global fairness, it is critical for an algorithm to function whenever the first order gets on-line without any observation stage. In this paper, we formulate this online user selection problem as a prophet inequality for independent identically distributed random variables from an unknown distribution. We construct a sample set to avoid the observation stage in an online decision process. Considering the driver-centered ride-sharing scenario, a route schedule algorithm and a sample-driven algorithm with a guarantee of lower bound are proposed to concurrently guide taxi drivers to accept taxi orders and achieve global fairness at the meantime. Finally, we conduct extensive evaluations based on three real-world data sets. The results verify the effectiveness of our proposed algorithm on improving the overall profit, increasing accepted orders and reducing the unoccupied time of the vehicle under the valid ride-sharing constraints.

Intelligent reflecting surface (IRS) is a promising technology which can be integrated with non-orthogonal multiple access (NOMA) to improve the secrecy performance. In this paper, we propose two IRS-aided schemes to enhance the security of NOMA networks for the internal and external eavesdropping, respectively. First, to deal with an internal untrusted user, the secrecy rate maximization problem is formulated by jointly optimizing the active and passive beamforming, while meeting the quality of service (QoS) demand of the untrusted user, decoding order constraints, and unit modulus constraints of IRS elements. Furthermore, considering a worse scenario with both internal and external eavesdroppers, we minimize the transmit power of legitimate signals with both users’ QoS demands satisfied. In this way, the confidential information leakage will be mitigated and more transmit power can be allocated as artificial jamming to attenuate the eavesdropping. To tackle the non-convex optimization, the original problem in each scheme is first decomposed into two subproblems, which are approximated into convex ones via the semidefinite relaxation (SDR). Then, by means of alternating optimization, the suboptimal solutions to the original problems can be obtained. Simulation results demonstrate the superiority of the proposed schemes against the challenging internal and external eavesdropping by combining IRS and NOMA.

In recent years, separating locators and identifiers has been widely applied in satellite Internet of things (IoT) networks. The identifier is the terminal identity, and the locator is used as the identification of routing. Therefore, an enormous mapping server is applied to store the mapping information between the identifier and locator, which needs to be updated timely and effectively. In this paper, a hybrid mapping server is developed to store the mapping table items, applied in the Global and Local aggregation nodes. Local aggregation nodes could cache the mapping table items locally and accelerate the mapping query by completing the query locally. In general, due to the limited storage space of Local aggregation nodes, only a proportion of the mapping information obtained from Global aggregation nodes could be stored, making the selection of cached mapping table items essential. In our work, a prefetch and cache replacement method (PCRA) based on Thompson sampling is proposed to select the appropriate mapping table items to cache, which combines the characteristics of the large number of satellite IoT terminals and fast terminal moving speed. It is shown that, compared with the traditional cache strategies, PCRA could improve the cache hit rate by around 10%.

In next-generation wireless networks, high-mobility unmanned aerial vehicles (UAVs) are promising to provide content coverage, where users can receive sufficient requested content within a given time. However, trajectory planning for multiple UAVs to provide content coverage is challenging since 1) UAVs cannot provide content coverage for all users due to the limited energy and caching storage, and 2) the trajectory planning of UAV is coupled with each other. Moreover, the complete information based trajectory planning methods are unusable since UAVs cannot obtain prior information on the rapidly changing environment. In this paper, we investigate the multi-UAV trajectory planning for energy-efficient content coverage. We first formulate an energy efficiency maximization problem considering recharging scheduling, which aims to reduce the total length of trajectories of UAVs under the quality of service (QoS) constraints. To settle environment uncertainty, the trajectory planning problem is modeled as two coupled multi-agent stochastic games, whose equilibrium constitute the optimal trajectory. To obtain the equilibrium, we propose a decentralized reinforcement learning algorithm, which can decouple the two games. We prove that the proposed algorithm can converge to the optimal solution of the Bellman equation with a higher rate compared to the centralized one. Moreover, simulation results show that the energy efficiency of the proposed algorithm is smaller than 5% compared the optimal, which is obtained with the prior information of environments.

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

In cache-enabled wireless networks (CWN), the unbalanced traffic distribution due to the node's sociality may lead to local congestion, which significantly degrades system throughput. Especially, nodes prefer to share content with those that have social relationships with them, which may result in heavy traffic load in the nodes with great social relationships. Therefore, it is crucial to capture the interplay among sociality, content caching and traffic distribution. In this paper, we design a caching strategy through jointly considering sociality and load balance to maximize the throughput capacity. To this end, efficient betweenness (EB) is adopted to quantify the traffic distribution, where EB is the number of content delivery paths through a node. Aided by EB, the impacts of key system parameters including sociality and caching strategy on throughput capacity are elaborated. According to the critical condition of the steady state in CWN, we formulate an optimization problem aiming to maximize throughput capacity. Due to the non-convexity of the initial problem, we propose an effective heuristic algorithm to solve it, which can balance traffic load according to the node's sociality and transmission capacity. Simulation results show that the proposed algorithm can increase the throughput capacity by 35.7% against benchmark approaches.

In this work, we investigate the location error of a fingerprint-based indoor system with the application of hybrid received signal strength (RSS) and channel state information (CSI) fingerprints. It manifests that exploiting correlation between RSS and CSI could effectively reduce location error. On this basis, we propose a hybrid RSS/CSI localization algorithm (HRCL), which is designed based on the deep learning. The HRCL fully exploits quick construction of fingerprint database with the coarse-grained RSS and rich multipath information of the fine-grained CSI. The RSS and CSI with high correlation are selected to construct fingerprint database, aiming to improve localization accuracy. Moreover, the deep neural network is trained for location estimation. Especially, experimental results validate that the location error of HRCL can be reduced by 64.4%, compared with the existing localization method. Moreover, the location error of HRCL can be reduced by 29.1 %, compared with HRCL without RSS/CSI selection by correlation coefficient.

Although the joint design of content caching and retrieving has the potential of relieving the backhaul pressure, either caching or retrieving would significantly influence the distribution of interference in caching-enabled small cell network (CSCN). The complicated interference, if not properly managed, would inversely deteriorate the network performance. In this work, we further investigate the tradeoff of content caching and retrieving in term of network spatial throughput (ST). Specifically, the analysis manifests that, compared to the condition of caching less content, ST is more likely to be reduced with increasing amount of retrieving content when more content is pre-cached by small cell base station (SBS). To maximize the ST, we formulate an optimization problem, which is solved by the joint design of content caching and retrieving. Moreover, a critical SBS density is derived from the optimization result, beyond which less content should be retrieving if more content is pre-cached by SBS. Therefore, the tradeoff between content caching and retrieving could be captured. More importantly, it is shown that a backhaul-free region exists, where the maximization of ST under joint optimization is identical to that under caching optimization. This indicates that the backhaul pressure can be significantly relieved through caching optimization in dense CSCN.

While Wi-Fi is of great potential for indoor localization, the access points (APs) deployment in realistic indoor environments is particularly challenging due to the impact of various obstacles, e.g., walls, pillars or bookcases. The diverse obstacles create the troublesome non-line-of-sight and the multipath effect, which deteriorate the localization accuracy. In this paper, we study the effect of obstacles on the localization error and formulate the AP planning problem as a AP selection problem. This problem is decomposed into two subproblems, i.e., AP selection problem and error minimization problem. The AP selection problem aims to choose the minimum number of APs to satisfy the preset accuracy requirement. Furthermore, the error minimization problem improves the localization performance through optimizing the AP positions and array orientations. Extensive simulations show that our proposed method is adaptive to the obstacles and it achieves higher localization accuracy compared with the existing deployment method.

Fixed-wing unmanned aerial vehicles (UAVs) are of great potential to serve as aerial access points (APs) owing to better aerodynamic performance and longer flight endurance. However, the inherent hovering feature of fixed-wing UAVs may result in discontinuity of connections and frequent handover of ground users (GUs). In this work, we model and evaluate the performance of a fixed-wing UAV network, where UAV APs provide coverage to GUs with millimeter wave backhaul. Firstly, it reveals that network spatial throughput (ST) is independent of the hover radius under real-time closest-UAV association, while linearly decreases with the hover radius if GUs are associated with the UAVs, whose hover center is the closest. Secondly, network ST is shown to be greatly degraded with the over-deployment of UAV APs due to the growing air-to-ground interference under excessive overlap of UAV cells. Finally, aiming to alleviate the interference, a projection area equivalence (PAE) rule is designed to tune the UAV beamwidth. Especially, network ST can be sustainably increased with growing UAV density and independent of UAV flight altitude if UAV beamwidth inversely grows with the square of UAV density under PAE.

Exploiting the time-varying feature of wireless channels, retransmission could enable reliable data transmission. However, the growing deployment of small cell base stations (BSs) would induce significant temporal interference correlation. In consequence, once the current transmission fails, the subsequent ones are likely to fail as well. In this light, we investigate the retransmission performance in dense networks with temporally correlated interference in terms of network spatial throughput (ST). Our results reveal that the impact of temporal interference correlation on the retransmission performance critically depends on the BS density. Specifically, temporal interference correlation would cause a greater network ST attenuation when the BS density is closer to the critical density, under which network ST is maximized. Moreover, the impact of temporal interference correlation is shown to be cumulative as the number of retransmission attempts increases. Furthermore, towards efficient retransmission, we adopt and optimize a P-Activation strategy (PAS). It is shown that the optimized PAS is able to effectively mitigate the overwhelming strength and temporal correlation of interference. As a result, the retransmission performance in improving network ST is significantly enhanced in dense networks, while the variation of network ST with BS density exhibits sigmoid trend instead of the previous near-bell shape.

In mobile edge computing (MEC), the computation offloading of massive users could cause the task uploading congestion to deteriorate the users' offloading delay. The non-orthogonal multiple access (NOMA) enabled MEC is envisioned to address this issue by allowing multiple users to simultaneously upload their tasks on one subchannel. However, the differentiated uploading delay of users may make task uploading completion inconsistent with NOMA decoding order, which complicates the co-channel interference and restricts NOMA to reducing the uploading delay. In this paper, we characterize the interaction between the differentiated uploading delay and co-channel interference for a pair of NOMA users. Furthermore, we propose a computation offloading scheme to reduce the users' average offloading delay by jointly optimizing offloading decision and resource allocation. Specifically, the proposed scheme first obtains the optimal power allocation based on the characterized interaction and the closed-form solution of computation resource allocation by convex programming. Then, the NOMA user pairing and offloading decision are iteratively determined by semidefinite relaxation and convex-concave procedure. Simulation results show that the proposed scheme effectively mitigates co-channel interference under differentiated uploading delay of users and outperforms in reducing the users' average offloading delay and increasing the number of users to offload tasks.

Non-orthogonal multiple access mobile edge computing (NOMA-MEC) is proposed to enhance the connectivity between the edge node and users for low- latency computation offloading. However, it is asynchronous for users to complete the task uploading, which complicates the co-channel interference between NOMA users to affect overall offloading delay. In this paper, we first characterize the impact of this asynchronism in task uploading on interference management in NOMA enabled computation offloading. The optimal power allocation is proposed to coordinate the co-channel interference between both NOMA users. Then, we propose a multi- user offloading scheme to jointly optimize offloading decision and NOMA user pairing, aiming to minimize the users' average delay on executing their tasks. The proposed offloading scheme is designed by formulating a binary nonlinear problem, which is solved by the proposed relaxation method and heuristic algorithm. Simulation results demonstrate that compared with other NOMA based schemes, our proposed scheme can effectively reduce the average delay of users and increase the number of users to perform computation offloading.

With the growing deployment of small cell base stations (BSs), the impact of backhaul congestion on the performance of small cell networks becomes dominant. To capture this effect, we present a conjoint framework integrating backhaul architecture with the access network. In particular, we evaluate the performance of ultra-dense networks (UDNs) in terms of network spatial throughput (ST), supposing that backhaul is conveyed to BSs through millimeter wave (mmWave) links by gateways. Notably, in contrast to diminishing to zero in previous work, network ST is shown to converge into a saturation under the given gateway density since the limited backhaul capability of gateways would stabilize the interference distribution of access network. Moreover, despite the benefit of enhancing backhaul capacity, over-deployed gateways would result in the potential intercell interference, which degrades network ST of UDN. On this account, we further study the optimization of gateway density to balance the tradeoff between enhancing backhaul capacity and mitigating intercell interference. It indicates that the application of mmWave beamforming at gateways leads to an increase in optimal gateway density, under which network ST could be further improved.

Interference serves as the most dominant factor that quantitatively and qualitatively impacts the performance of ultra-dense networks (UDN). Especially, since interference of different time slots basically comes from the same set of interfering base stations (BSs), the temporal interference correlation becomes more significant with the growing deployment of network infrastructures. In this light, we develop an analytical framework to investigate the impact of temporal interference correlation on UDN in terms of network spatial throughput (ST) in this work. In contrast to the available research, which indicates that the interference correlation is independent of BS density, we show that a growing BS density would exacerbate the influence of interference correlation on the performance of UDN. In particular, when the BS density is closer to the critical density, under which network ST could be maximized, the ST attenuation caused by temporally correlated interference is more significant. Moreover, the effect of temporal interference correlation on network ST is additive over time slots. For instance, a greater number of transmissions of hybrid automatic repeat request (HARQ) would result in a more significant effect of temporally correlated interference on network ST.

This study is to construct the autoregressive models for the low‐voltage broadband power line communication (PLC) channel noise by two machine learning algorithms, namely the least square support vector machine (LS‐SVM) and wavelet neural networks. The main work is to compare the two classical machine learning algorithms and also compare them with the traditional Markovian–Gaussian method. To verify their availability and ability to adapt to the time‐variant PLC channels, noise measurements for low‐voltage PLC channels in indoor and outdoor scenarios are carried out. The accuracy and efficiency of the two models are studied and compared based on a large amount of measurement data. The results show that both of the noise models can simulate and adapt to the time‐variant low‐voltage broadband PLC channels very well. The LS‐SVM model is found to have shorter simulation time and higher accuracy. Moreover, the proposed noise models are also compared with the traditional Markovian–Gaussian model. The results show that both the proposed noise models exhibit higher accuracy and lower complexity, especially that the LS‐SVM is more appropriate to be applied as a noise generator in PLC link and network level simulations instead of the current Markovian–Gaussian model.

Unmanned aerial vehicles (UAVs) have been extensively applied as aerial access points to assist the terrestrial wireless network. Despite the inherent potential, nevertheless, it still remains to explore whether the gain of UAV access points (UAPs) could be fully harvested, which is critically dependent on the factors including the flight altitude and deployment density of UAPs. In this light, we investigate the performance of a downlink UAV integrated terrestrial cellular network (UTCN) and analytically study the influence of varying UAP altitude and density on the spatial throughput (ST) of UTCN. In particular, we obtain the UAP altitude upper bound, below which more line-of-sight (LOS) connections could be provided to improve network ST. Otherwise, cross-layer interference over the LOS paths becomes dominant, which results in significant degradation of network ST. More importantly, we reveal the limitation of the application of UAPs by showing that there exists a critical UAP density, beyond which network ST would encounter a rapid decrease. To fully exploit the potential of UAPs, we further tailor a probabilistic interference avoidance scheme and study the optimization of the UAP activated probability. Remarkably, network ST could be substantially improved using the optimized activated probability, i.e., network ST could increase with the growing UAP density and converge to a positive constant instead of zero in the dense UAP regime. Therefore, the results of this paper could provide insight on the deployment and optimization of UTCN.

This paper aims at shedding light on the impact of unsaturated traffic on the performance of uplink non-orthogonal multiple access (NOMA) transmissions. Nevertheless, the unsaturated traffic gives rise to the discontinuous interference and the inherent interaction of queues, which in turn highly complicates the performance evaluation. By utilizing tools from queuing theory, we first explicitly characterize the stable throughput region, which represents the region of traffic arrival rates on the condition that the queuing delay converges in distribution to a bounded random variable. In light of this, the critical condition under which NOMA can extend the stable throughput region of orthogonal multiple access (OMA) is derived. Then, we propose an algorithmic solution to evaluate the average delay incurred from both queuing and transmission. It is interestingly found that the superiority of NOMA over OMA in terms of average delay heavily hinges on the temporal traffic dynamics of each user. In particular, NOMA enjoys a clear advantage when the traffic arrival rate of the user with stronger channel condition considerably exceeds the traffic arrival rate of the user with weaker channel condition. The derived results can provide helpful guidance to fully leverage the comparative advantages of NOMA under various traffic conditions.

Signal processing on antenna arrays has recently received extensive attention in the area of angle-of-arrival (AoA)-based indoor localization. Although sufficient array elements can improve the resolution in the AoA estimation, the array orientation has not been well exploited in research into the localization performance. In this paper, we investigate the effect of array orientations on the performance of AoA-based indoor localization systems. Appropriate array orientation can efficiently reduce the uncertainty in AoA estimation, thereby improving the localization accuracy. Accordingly, we present OpArray, an accurate indoor localization system based on flexible array deployment. First, OpArray designs an array deployment scheme, which establishes the foundation for accurate AoA estimates. The deployment scheme can be easily implemented through array rotations so as to optimize array orientations at receivers. Second, OpArray incorporates two refined phase preprocessing algorithms to mitigate the impact of negative factors, which exist in the practical implementation. In addition, aided by an improved AoA estimation algorithm, OpArray can localize a target on commercial off-the-shelf Wi-Fi platforms. Our experiments in a multipath-rich indoor environment show that OpArray achieves a median localization error of 0.5 m and the 80th percentile error is 1.0 m, which outperforms the state-of-the-art localization systems.

Label distribution learning (LDL) is a newly arisen machine learning method that has been increasingly studied in recent years. In theory, LDL can be seen as a generalization of multi-label learning. Previous studies have shown that LDL is an effective approach to solve the label ambiguity problem. However, the dramatic increase in the number of possible label sets brings a challenge in performance to LDL. In this paper, we propose a novel label distribution learning algorithm to address the above issue. The key idea is to exploit correlations between different labels. We encode the label correlation into a distance to measure the similarity of any two labels. Moreover, we construct a distance-mapping function from the label set to the parameter matrix. Experimental results on eight real label distributed data sets demonstrate that the proposed algorithm performs remarkably better than both the state-of-the-art LDL methods and multi-label learning methods.

In this paper, we investigate the resource allocation for mobile edge computation offloading in non-orthogonal multiple access (NOMA) cellular networks. Leveraging NOMA, the massive connectivity can be supported to enable multiple cellular users to simultaneously upload their computation-intensive tasks on the same orthogonal resources, which improves spectral efficiency and reduces transmission delay. However, the co-channel interference in non- orthogonal spectrum sharing may potentially degrade the achievable rate of offloading computation tasks. Moreover, the overall delay of all cellular users in finishing computation offloading will increase if the computation resources at the edge server are not properly allocated. To minimize the maximum overall delay of all users, we formulate an optimization problem that jointly allocates communication resources and computation resources. Due to the non-convexity of the primal problem, we divide it into three subproblems. By exploiting their specific structures, an efficient algorithm is designed to obtain the suboptimal solution with low computational complexity. Simulation results are presented to demonstrate that our proposed algorithm can effectively reduce the overall delay of cellular users and fully exploit the benefit of NOMA on spectral efficiency, especially when the number of users is large.

In this paper, we investigate the influence of limited backhaul on the performance of caching enabled ultra-dense small cell networks (USCN). In particular, an analytical framework has been presented to evaluate the performance of USCN in terms of area spectral efficiency (ASE). It is shown that the constraint of backhaul capacity would significantly influence the performance of content caching and retrieving in USCN. For instance, it is shown that, if the resulting interference is not properly handled, caching more content would result in a decrease in ASE of USCN in the unlimited backhaul regime. This contradicts with the limited backhaul regime, in which network ASE could be notably improved if more content is pre-fetched. Moreover, it is shown that network ASE would experience a faster diminish as well if more content is retrieved via backhaul. The reason is that USCN turns from interference-limited into backhaul- limited regime with the growing BS and user densities. On this account, we have designed a probabilistic content retrieving strategy to relieve the bottleneck brought by limited backhaul. Targeting at maximizing the ASE of USCN, we further optimize the content retrieving probability (CRP). With the derived sub-optimal CRP, it is shown that the scaling law of network ASE (with varying BS density) could be fundamentally improved. Therefore, the results of this work could provide insight on the application of caching in USCN with limited backhaul constraint.

In this paper, we evaluate the performance of a downlink unmanned aerial vehicle (UAV) assisted cellular network (UACN). In particular, an analytical framework is developed to derive network spatial throughput (ST), an indicator to network capacity, in UACN. Under the framework, we investigate the influence of deployment density and altitude of UAV access points (UAPs) on the performance of UACN. For instance, the closed-form expression of UAP flight altitude upper bound is obtained. Rising the UAP altitude below the upper bound, more line-of-sight (LOS) connections could be provided to improve network ST. Otherwise, cross- layer interference over the LOS paths becomes dominant and accordingly network ST would be significantly degraded. Moreover, to reveal the fundamental limitation of the integration of UAPs, we derive the critical UAP density through a special case study. If the density of deployed UAPs is greater than the critical density, network ST would encounter a rapid decrease. The results could provide insight on the application of UAVs in the next-generation terrestrial wireless networks.

In this paper, we investigate the limitation of multi-user multiple-input multiple-output (MIMO) in ultra-dense networks (UDNs) and investigate how to overcome the limitation by designing efficient interference management strategies. Specifically, it is shown that the area spectral efficiency (ASE), an indicator to network capacity, would approach zero with over-deployed base stations (BSs) even when multi-user MIMO is applied. Worse still, it manifests that the multi-user gain of MIMO cannot be harvested in UDN due to the overwhelming interference. In particular, the maximal ASE is shown to be degraded by increasing the number of served users in each cell. To alleviate the bottleneck brought by interference, we have designed and optimized two simple but efficient BS activation policies. It is shown that the application of the optimized BS activation policy could improve network capacity scaling law and boost the potential of multi-user MIMO in UDN. Remarkably, the ASE is shown to increase with BS density λ even when λ and user density are sufficiently large. Moreover, the maximal ASE in the λ → ∞ regime is shown to increase with the number of served users in each cell, which contradicts with the all-BS-on case.

Capable of significantly reducing cell size and enhancing spatial reuse, network densification is shown to be one of the most dominant approaches to expand network capacity. Due to the scarcity of available spectrum resources, nevertheless, the over-deployment of network infrastructures, e.g., cellular base stations (BSs), would strengthen the inter-cell interference as well, thus in turn deteriorating the system performance. On this account, we investigate the performance of downlink cellular networks in terms of user coverage probability (CP) and network spatial throughput (ST), aiming to shed light on the limitation of network densification. Notably, it is shown that both CP and ST would be degraded and even diminish to be zero when BS density is sufficiently large, provided that practical antenna height difference (AHD) between BSs and users is involved to characterize pathloss. Moreover, the results also reveal that the increase of network ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and network performance, we further study the critical density, under which ST could be maximized under the CP constraint. Through a special case study, it follows that the critical density is inversely proportional to the square of AHD. The results in this work could provide helpful guideline towards the application of network densification in the next-generation wireless networks.

In the paper, we propose an effective long-term real-time tracking method to address the problem of robustness and tracking failure in visual tracking with UAVs. Most existing trackers only consider short-term tracking, therefore are unable to cope with partial and complete occlusion, which finally leads to object drifting or loss. Our method still follows the tracking-by-detection framework. However, after choosing kernelized correlation filter as the tracker baseline, we introduce the confidence of candidate patches to measure tracking reliability, and trigger redetection process with random forest and learned object model when needed. We further improve object update strategy to make the object model with memory more robust against object drift. Extensive experiment results on UAV videos show that our algorithm performs better than widely used TLD, KCF, and LCT methods.

This paper presents an accurate indoor localization system with irregular deployment of antennas. It can be feasibly deployed on commodity Wi-Fi infrastructures, without any hardware or firmware modifications. Aided by elaborate phase processing and an enhanced angle of arrival (AoA) estimation algorithm, our proposed system could provide higher localization accuracy under the coverage of two line-of-sight (LOS) access points (APs) compared to the state-of-the-art localization systems, where at least three APs are utilized to achieve the same accuracy. To be specific, a pertinent phase compensation and sanitization algorithm is designed to eliminate the additional factors that will distort the genuine channel state information (CSI). On this basis, we make the antennas irregularly deployed at each AP such that the linear array symmetry is removed and more AoAs can be obtained. In particular, with two APs equipped with 3 antennas irregularly deployed, up to 4 AoAs could be obtained (more than 2 AoAs with linear array), which provides the ability to localize a target in a 3-D space. Our experiments in a multipath rich indoor environment show that our system achieves a higher localization accuracy than the state-of-the-art localization systems, namely, a median error accuracy of 1.2 m in 2-D localization and 1.45 m in 3-D localization with two APs.

Sparse code multiple access (SCMA) has been recently proposed for the future wireless networks, which allows nonorthogonal spectrum resource sharing and enables system overloading. In this paper, we apply SCMA into device-to-device (D2D) communication and cellular hybrid network, targeted at using the overload feature of SCMA to support massive device connectivity and expand network capacity. Particularly, we develop a stochastic geometry-based framework to model and analyze SCMA, considering underlaid and overlaid modes. Based on the results, we analytically compare SCMA with orthogonal frequency-division multiple access (OFDMA) using area spectral efficiency (ASE) and quantify closed-form ASE gain of SCMA over OFDMA. Notably, it is shown that system ASE can be significantly improved using SCMA and the ASE gain scales linearly with the SCMA codeword dimension. Besides, we endow D2D users with an activated probability to balance cross-tier interference in the underlaid mode and derive the optimal activated probability. Meanwhile, we study resource allocation in the overlaid mode and obtain the optimal codebook allocation rule. It is interestingly found that the optimal SCMA codebook allocation rule is independent of cellular network parameters when cellular users are densely deployed. The results are helpful in the implementation of SCMA in the hybrid system.

In this paper, we integrate device-to-device (D2D) communications with coordinated multi-point (CoMP) in downlink cloud radio access network (C-RAN), targeting at using the proximity nature of D2D communications to improve system spectral efficiency. In particular, using stochastic geometry, we first derive the signal-to-interference ratio (SIR) distribution at a typical downlink user and a typical D2D receiver, considering zero-forcing beamforming (ZFBF) as the CoMP scheme. Based on the results, we analyze the effect of D2D communications on enhancing the area spectral efficiency (ASE) of the CoMP enabled C-RAN system. Numerical results show that increasing the cooperative cluster size would potentially degrade the system ASE when the network is heavily loaded. Furthermore, it is observed that the addition of D2D links, despite introducing excessive cross-tier interference to downlink users, can provide significant gains if properly designed.

Device-to-Device (D2D) communication underlaid cellular networks has been regarded as a technology with great promise to provide higher transmission rate, lower latency and better energy efficiency in services between user terminals in the future fifth generation (5G) wireless network. In this paper, we consider the resource allocation problem to enhance the system performance. Specifically, we use hypergraph to characterize the interference among cellular uplinks and D2D links when sparse code multiple access (SCMA) is applied as the multiple access strategy. Targeting at maximizing system sum rate, we propose an Interference-Aware Hypergraph based Codebook Allocation (IAHCA) algorithm. Using IAHCA, each orthogonal SCMA resource, i.e., SCMA codebook, is allowed to be shared by one cellular uplink and more than one D2D links. As a consequence, available SCMA resources can be fully exploited, thereby effectively achieving higher system throughput and activating more D2D links. Simulation results confirm that IAHCA outperforms conventional graph based algorithm and other hypergraph based algorithms.

As a promising multiple access technique for 5G wireless networks, sparse code multiple access (SCMA) has been put forward to support massive connectivity and enhance network performance. In this paper, we develop a theoretical framework using stochastic geometry to evaluate the performance of SCMA ad hoc networks. Under this framework, we first derive an explicit matrix form for the successful transmission probability. We then consider two area spectral efficiency (ASE) maximization problems without and with link reliability constraint to investigate the ASE gain of SCMA over OFDMA networks and the tradeoff between the ASE and link reliability, respectively. In particular, we obtain the optimal medium access probability (MAP) to solve both problems. Both numerical and simulation results exhibit that, compared to OFDMA networks, an asymptotically 160% gain in the ASE and a nearly 91% improvement in the transmission opportunity can be achieved by SCMA networks with typical settings.

Coordinated multipoint (CoMP) is an efficient technique to increase cell-edge coverage probability and throughput in cloud radio access network (C-RAN). In this paper, we integrate device-to-device (D2D) communications with CoMP by applying a distance based mode selection rule for downlink users in C-RAN, exploiting the proximity of D2D communications to improve system spectral efficiency. Using stochastic geometry, we first derive the signal-to-interference ratio distribution at a typical downlink user and a typical D2D receiver when two types of CoMP schemes, namely, zero-forcing beamforming (ZFBF) and noncoherent joint transmission (NC-JT), are applied in C-RAN. In addition, we analytically compare ZFBF and NC-JT using rate coverage probability. Meanwhile, we analyze the effect of D2D communications on enhancing the area spectral efficiency (ASE) of CoMP enabled C-RAN system. Numerical results show that increasing the co-operative cluster size would potentially degrade the system ASE when the network is heavily loaded. Furthermore, it is observed that enabling D2D mode in C-RAN can effectively offload the traffic of C-RAN and provide significant ASE gains if mode selection threshold is properly designed. Lastly, it is analytically demonstrated that spectrum resources can be better exploited by D2D users if they coexist with ZFBF enabled radio units (RUs) rather than NC-JT enabled RUs.

Abstract In this paper, we consider the distributed opportunistic scheduling problem for the Orthogonal Frequency Division Multiplexing OFDM‐based device‐to‐device (D2D) communications, where D2D links contend for access to the dedicated spectrum with limited assistance from cellular infrastructures. Particularly, a synchronous distributed opportunistic scheduling protocol under fairness constraints (DO‐Fast) is prompted. In DO‐Fast, a round‐robin strategy is integrated with the opportunistic scheduling to tackle the trade‐off between system throughput and access fairness. Moreover, without instantaneous channel state information at receivers, we incorporate a priority allocation scheme, where access priorities are assigned randomly in a local fashion. Consequently, DO‐Fast is robust against imperfect channel estimates and inaccurate channel state information ordering. In addition, the opportunistic strategy in DO‐Fast is distinguished from the existing ones in that efficient spatial reuse is exploited by allowing concurrent transmissions based on the signal‐to‐interference ratio scheduling criterion. Meanwhile, access opportunities are moderately granted for poor quality links by the round‐robin strategy for fairness considerations. We analyze and compare three practical scheduling strategies in terms of the access probability. We also evaluate access fairness through Jain's Index. It is shown via numerical and simulation results that DO‐Fast could achieve efficient spectrum utilization and guarantee the short‐term fairness. Copyright © 2014 John Wiley & Sons, Ltd.

The integration of Device-to-Device (D2D) communications into cellular networks, albeit improving spectrum efficiency, may inevitably lead to cross-tier interference between cellular users and D2D users. In this paper, we endow D2D users with the capability of power control to address the cross-tier interference and theoretically analyze the benefits of power control in enhancing the transmission capacity region (TCR). In particular, based on transmission capacity, the TCR is defined as the enclosure of all feasible combinations of transmitter intensities in cellular and D2D networks. We first employ the stochastic geometry framework to derive closed-form expressions of the TCR for two prevalent spectrum sharing modes, i.e., reuse mode and dedicated mode. As for the reuse mode, we then study how to enlarge the TCR through initializing the power levels of cellular users and D2D users. Finally, the dedicated mode is compared with the reuse mode through TCR. Specifically, given the same target rate for cellular users and D2D users, the reuse mode is shown to outperform the dedicated mode in terms of the TCR when 2α/2≤ θ + 2, where α and θ are, respectively, the path loss exponent and decoding threshold. The analysis provides useful guidance for spectrum regulation and design of efficient power control techniques in D2D integrated cellular networks.

In this paper, we characterize the transmission capacity region (TCR) in D2D integrated cellular networks when two prevalent interference management techniques, power control and Successive Interference Cancellation (SIC) are utilized. The TCR is defined as the enclosure of all feasible sets of active transmitter intensities in cellular and D2D systems. Closed-form approximate expressions of TCR are derived for two spectrum sharing modes, i.e., reuse mode and dedicated mode. The analysis provides insights into the impact of network parameters, interference management methods, as well as bandwidth allocation policy on the TCR. Moreover, we compare the reuse mode and dedicated mode in terms of TCR. Specifically, with power control, given the same target rate for cellular users and D2D users, the TCR of the dedicated mode is shown to be entirely enclosed by that of the reuse mode when 2α/2 ≤ θ+2, where α and θ are, respectively, the path loss exponent and decoding threshold. However, with SIC utilized, numerical results show that when θ > 1, better performance can always be achieved by the reuse mode in terms of TCR. The results can serve as a guideline for the design of efficient interference management techniques and spectrum regulation in D2D integrated cellular networks.

In this paper, we study joint link scheduling and power control with successive interference cancellation (SIC), aiming at maximizing the α-utility. The joint link scheduling and power control with SIC (PCSIC) problem is formulated to be a mixed-integer non-linear programming (MINLP), which is NP-hard. In order to solve the problem, we first decompose the MINLP into three sub-problem and then propose an iterative algorithm. We compare our strategy with the scheme without power control from the perspective of system throughput, fairness index and energy consumption. Numerical results show the noticeable performance improvement of the proposed strategy.

In this paper, we consider the distributed scheduling problem for the OFDM based device-to-device (D2D) communications. In order to fully exploit the spatial diversity of the channel variation as well as provide access fairness for all D2D links, we propose a synchronous Distributed Opportunistic scheduling protocol under Fairness constraints (DO-Fast). DO-Fast incorporates the opportunistic scheduling with a round-robin strategy. By exchanging local Channel State Information (CSI) in a distributed way, the opportunistic scheduling strategy enables the links with better channel conditions to take precedence for higher access priorities. It leads to more concurrent transmissions and higher system throughput than the random scheduling strategy, where links are allocated with priorities in a random manner regardless of channel conditions. Meanwhile, we prompt a round-robin strategy so that the D2D links would take high priorities alternately, which guarantees the short-term fairness requirements of the links with poor channel conditions. We show via simulations that DO-Fast achieves throughput improvement over the existing scheduling protocol from the network perspective with acceptable delay performance.