Satellite mega-constellations (SMCs), comprising thousands of interconnected satellites, have emerged as critical infrastructure for 6G networks to achieve seamless global coverage. This paper addresses two fundamental challenges in SMC operation: 1) the inherent spatial-temporal traffic heterogeneity with continuously escalating demand, and 2) the diverging quality-of-service (QoS) requirements for diverse traffic types requiring robust end-to-end performance guarantees. To enhance resource utilization while ensuring service differentiation, we propose a novel dual-scale traffic management framework encompassing macroscopic network-level coordination and microscopic node-level adaptation. The macroscopic component formulates a multi-objective optimization framework that strategically allocates transmission paths by simultaneously minimizing inter-satellite link load disparities and end-to-end queuing delays. The microscopic component introduces an adaptive resource allocation mechanism that decomposes end-to-end QoS requirements into per-node service level agreements, employing federated learning based traffic prediction to enable dynamic resource pre-allocation based on real-time load conditions. This hybrid approach achieves load-aware resource provisioning that maximizes traffic completion rates while minimizing inefficient transmissions. Simulation results show our scheme outperforms the on-demand multi-objective optimization approach, improving traffic completion rates by 17.0-27.5% and resource utilization by 23.29-62.34% across varying loads, while reducing latency and enhancing fairness.

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.

Satellite computing networks leverage the placement of computing resources on low-Earth orbit (LEO) satellite communication network to bring computing power closer to users, enabling robust and scalable solutions for emerging applications such as Internet of thing, edge computing and real-time analysis. However, with the explosion of multi-layer LEO mega-constellations(MLMCs), how to use the least number of computing nodes to achieve computing power resource coverage? This paper proposes a computing node deployment algorithm for MLMCs to construct satellite computing network with the minimum number of computing node. Specifically, we analyze the existence conditions of optimal computing node deployment, and derive the expression of the relationship between the number of computing nodes needed on the LEO, the multi-layer satellite network structure and the number of accessible hops of computing nodes. The expression can determine the minimum number of computing nodes needed to be placed on the MLMCs to realize optimal computing node deployment under a certain number of accessible hops. Then, according to the limitation of optimal computing node deployment, a multi-layer satellite network computing node deployment algorithm is proposed to determine the position of the computing resource in MLMCs, which can form a stable computing structure in satellite network. Finally, through simulation, the influence of network scale and computing node deployment on the number of required computing nodes and the effectiveness of signaling delay is analyzed. The simulation results show that this method can achieve better delay with the least number of computing nodes, balance the number of computing nodes and delay, and meet the specific network requirements.

Mega satellite constellations (MSCs) based on low Earth orbit (LEO) satellites and inter-satellite links (ISLs) have become increasingly important due to the seamless coverage and high throughput. Unfortunately, the communication components of satellites are susceptible to radiation-induced single event upsets (SEUs), which lead to the failure of ISLs and the decline in network throughput. In this paper, we study the impact of SEUs on network throughput and propose MSC design algorithms to enhance the throughput. To mitigate the impact of SEUs, each satellite is equipped with low-cost mitigation techniques, under which ISLs experience different levels of impairment. Furthermore, we derive the expressions of network throughput and observe the mismatch between the traffic pattern and the network topology. Based on the expressions, we develop the MSC design algorithm to address the gap for throughput enhancement. Simulation results validate the accuracy of the theoretical results, and demonstrate that the proposed algorithm can effectively enhance the network throughput by 8.42% compared to the classical topology under the impact of SEUs.

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.

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.

A distinctive function of sixth-generation (6G) networks is the integration of distributed sensing and edge artificial intelligence (AI) to enable intelligent perception of the physical world. This resultant platform, termed integrated sensing and edge AI (ISEA), is envisioned to enable a broad spectrum of Internet-of-Things (IoT) applications, including remote surgery, autonomous driving, and holographic telepresence. Recently, the communication bottleneck confronting the implementation of an ISEA system is overcome by the development of over-the-air computing (AirComp) techniques, which facilitate simultaneous access through over-the-air data feature fusion. Despite its advantages, AirComp with uncoded transmission remains vulnerable to interference. To tackle this challenge, we propose AirBreath sensing, a spectrum-efficient framework that cascades feature compression and spread spectrum to mitigate interference without bandwidth expansion. This work reveals a fundamental tradeoff between these two operations under a fixed bandwidth constraint: increasing the compression ratio may reduce sensing accuracy but allows for more aggressive interference suppression via spread spectrum, and vice versa. This tradeoff is regulated by a key variable called breathing depth, defined as the feature subspace dimension that matches the processing gain in spread spectrum. To optimally control the breathing depth, we mathematically characterize and optimize this aforementioned tradeoff by designing a tractable surrogate for sensing accuracy, measured by classification discriminant gain (DG). Experimental results on real datasets demonstrate that AirBreath sensing effectively mitigates interference in ISEA systems, and the proposed control algorithm achieves near-optimal performance as benchmarked with a brute-force search.

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.

Satellite networks with wide coverage are considered natural extensions to terrestrial networks for their long-distance end-to-end (E2E) service provisioning. However, the inherent topology dynamics of low earth orbit satellite networks and the uncertain network scales bring an inevitable requirement that resource chains for E2E service provisioning must be efficiently re-planned. Therefore, achieving highly adaptive resource management is of great significance in practical deployment applications. This paper first designs a regional resource management (RRM) mode and further formulates the RRM problem that can provide a unified decision space independent of the network scale. Subsequently, leveraging the RRM mode and deep reinforcement learning framework, we develop a topology feature-based dynamic and adaptive resource management algorithm to combat the varying network scales. The proposed algorithm successfully takes into account the fixed output dimension of the neural network and the changing resource chains for E2E service provisioning. The matched design of the service orientation information and phased reward function effectively improves the service performance of the algorithm under the RRM mode. The numerical results demonstrate that the proposed algorithm with the best convergence performance and fastest convergence rate significantly improves service performance for varying network scales, with gains over compared algorithms of more than 2.7%, 11.9%, and 10.2%, respectively.

The image task scheduling demand exhibits a thriving trend since China leverages heterogeneous low earth orbit (LEO) satellites to empower the earth observation field for Belt and Road Initiative (BRI) countries and regions. However, the high-dynamic mobility and intermittent inter-satellite links (ISLs) exacerbate communication, caching, and computing (3C) resources scarcity, while inefficient scheduling significantly deteriorates the successful transmission ratio (STR). To tackle this challenge, this paper optimizes the task scheduling strategy. To be specific, we first propose a dynamic resource mapping model (DRMM) that captures the dynamic characteristics of 3C resources through discontinuous ISLs and quantifies resources over continuous time. Based on the DRMM, we formulate an STR maximization problem under heterogeneous resource capacity constraints. A computing resources priority allocation algorithm (CRPAA) is designed to preferentially allocate onboard computing resources for task processing, thereby freeing up transmission and caching resources for additional tasks. Furthermore, the CRPAA leverages an incrementally searching slots range to reduce computational complexity and optimize the scheduling strategy, planning each task individually and clearing it promptly to prevent redundant scheduling while enhancing execution efficiency. The extensive simulation results validate that the proposed algorithm outperforms benchmark approaches.

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.

The aerospace integrated networks (AINs), leveraging satellites and unmanned aerial vehicles (UAVs), offers a promising solution for large-scale Internet of Remote Things (IoRT), effectively ensuring information freshness, i.e., low Age of Information (AoI). However, in resource-constrained and dynamic AIN environment, a key challenge is how to achieve fresh data by efficiently resolving mission conflicts across multiple IoRT devices, which requires advanced scheduling design for collaborative UAVs monitoring and UAVs-satellites data transmission. In this paper, we first construct a mission scheduling framework for collaborative monitoring and transmission utilizing the wide coverage of low earth orbit (LEO) satellites and the mobility of UAVs. Then, by considering constraints such as mission conflicts, energy consumption, and motion characteristics, we characterize the relationship between UAVs trajectories and IoRT demands. Based on this, we propose a multi-agent deep reinforcement learning (MADRL) algorithm that jointly optimizes UAV trajectories and transmission scheduling. The algorithm incorporates a filter layer to optimize UAV cooperation, preventing redundant device IoRT selection and resolving mission conflicts. Simulation results indicate that the proposed algorithm can reduce 22.9% AoI compared to the benchmark.

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.

With the rise of low-altitude economy (LAE), the incorporation of unmanned aerial vehicles (UAVs) with ground networks can assist integrated localization and communication (ILAC) services for ground user equipment (UE). However, the UAV location and number significantly affect communication coverage and localization performance for ground UEs. Additionally, UAV energy consumption and operational lifespan constrain the number of UEs each UAV serves. In this paper, we investigate the UAV deployment and access control assisting with ground stations to provide both communication and localization service to potential UEs. We decompose the UAV cooperation problem into two subproblems, namely minimizing UAV problem and the UAV assignment problem. To minimize the UAV number, we design an iterative greedy search algorithm that utilizes criteria importance through intercriteria correlation (CRITIC) method to dynamically evaluate each candidate UAV selection. After that, we assign the UAV access for each UE and reduce the number of UE served by each UAV to extend the lifespan of the drones. The communication and localization assignment methods are proposed by analysis of the number of communication links per UAV and the redundancy of anchor nodes. Further, we adopt a probability-based search algorithm to solve the assignment problem. Numerical studies are conducted to verify our proposed approach compared to other benchmark methods.

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.

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.

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.

Equipping satellites with computing resources to ensure efficient mission completion has become a pivotal trend in multi-layer satellite networks (MLSNs). The uneven spatial distribution of missions and computing resources across satellites necessitates advanced scheduling of communication and computing resources through satellite collaboration. However, the intricate interactions between communication and computing resources, the dynamic mission arrivals and computing resources, and the difficulty of collaboration across different layers in MLSNs present significant challenges for effective scheduling. This paper proposes a collaborative scheduling framework for low Earth orbit (LEO) and medium Earth orbit (MEO) satellites to support communication-computing integration in the MLSN. To adapt to network dynamics, we introduce a federated aggregation matrix and propose an intelligent MEO-LEO collaborative scheduling algorithm that optimizes the decision-making process under uncertain mission arrivals. Additionally, we design a distributed LEO-LEO collaborative scheduling algorithm that leverages the synergy between inter-satellite communication and computing resources to enhance scheduling capabilities and create communication-computing resource chains that meet mission requirements. Extensive simulations demonstrate that our proposed collaborative scheduling framework significantly enhances the scheduling capability of the MLSN.

The network topology is a typical element that affects the network capacity of low Earth orbit (LEO) mega-constellations. Considering the relative relationship between satellites, most mega-constellations with inclined orbits adopt the classic torus-like topology in which each satellite establishes four inter-satellite links (ISLs) with neighboring satellites. However, do we need so many ISLs to achieve the required capacity? In this paper, we provide an in-depth capacity analysis for quasi-torus topologies in which each satellite is connected by fewer than four ISLs. Based on the topological regularity and constellation scale, we first introduce the models of typical quasi-torus topologies. Considering that all the satellites are not symmetrical, we divide a quasi-torus topology into the same regions exhibiting axial symmetry. Combining the uniform traffic distribution and the symmetry of each region, we derive the closed-form formulae of network capacity by determining the maximum traffic load among ISLs. The formulae provide insights into the impact of ISL distribution and the constellation scale. Simulation results validate the theoretical analysis and show that the quasi-torus topology with fewer ISLs can achieve high capacity comparable to the torus-like topology. The theoretical analysis obtained is instrumental to the topology design of mega-constellations.

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.

Satellite-terrestrial networks (STNs) are emerging as a promising solution for provisioning comprehensive services, such as the Internet of Remote Things (IoRT) and remote sensing, within the realm of 6G wireless networks. Nonetheless, resource failures and the exigencies of diverse mission urgencies exacerbate the intricacies of resource scheduling in STNs, thus impeding the effective alignment of distinct mission requirements with dynamic resources. In light of these challenges, we first mathematically formulate the complex resource scheduling problem in STNs as a stochastic optimization paradigm, endeavoring to maximize the number of successfully accomplished missions. Subsequently, we conceptualize the resource evolution to delineate scheduling dynamics, encompassing potential contingencies of resource discontinuities. Next, we propose an innovative hierarchical deep learning-based mission-driven resource scheduling (HDL-MDRS) algorithm, aimed at optimizing resource collaboration and reconfiguration to amplify network performance within the dynamic ambits characterized by resource disruptions. The HDL-MDRS algorithm achieves a coarse-grained alignment of diverse mission requirements with multidimensional resources. It enhances overall mission fulfillment and network resource utilization efficiency through fine-grained collaboration and reconfiguration among satellites, both within and across different clusters. Notably, the simulation findings substantiate the effectiveness of the HDL-MDRS algorithm, effectively ensuring the requirements of different types of missions in case of the unforeseen resource failures, orchestrated through efficient resource collaboration and on-demand reconfiguration.

The inter-layer connection planning strategy of the multi-layer satellite mega constellations is a key technology to guarantee reliable and rapid transmission for various traffic demands. However, satellites deployed at different orbital heights cause more complicated layer-relative motions and frequently intermittent satellite connections. How to plan inter-layer links (ILLs) to guarantee high-throughput communication and provide reliable satellite relays is extremely challenging. In this paper, we leverage the satellite regular trajectories of each layer to design a hierarchical planning architecture, which consists of two phases, critical satellite selection and dynamic ILL planning. In the first phase, critical satellites are selected by traffic load and delay requirements, and they can connect with other layers to search for efficient relays. Considering the high dynamic motion between layers, we further propose an ILL planning problem between critical satellites and relays to maximize throughput and obtain robust ILLs. To tackle the proposed problem in large spatio-temporal scales, we propose a multi-agent learning-based ILL planning strategy, which can adjust switching directions based on the orbit relative position from critical satellites to relay satellites of various layers and obtain efficient ILLs. Simulation results illustrate that the optimal ILL number is less than 1/3 of its layer scale, and the proposed strategy can enhance throughput by 14.6%, and reduce the switching rate by 60.9% compared to state-of-the-art baseline algorithms.

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.

Network control, including mobility management, resource management and e.t.c., plays a vital role in maintain the effectiveness and reliability of multi-layer low-earth orbit megaconstellations (MLMCs). One of the critical issues in satellite network control is how to choose the position of controller to achieve the optimal state of satellite network control structure with the least number of control nodes, so that any access satellite in the network can reach the control satellites in J hops at most and realize seamless coverage of the MLMCs by the control satellites without overlapping. This paper proposes a seamless coverage control structure to improve the temporal effectiveness of controlling signaling distribution in MLMCs. Specifically, this paper firstly analyzes the influence of the shape and size of satellite beam coverage on the network control structure. Based on the above analysis, the existence conditions of seamless network control structure are deduced through the design of satellite beam inclination angle, where any access satellite can reach the control satellites in J hops at most. Finally, taking access and mobility management function (AMF) as an example, we apply the placement of control units to the analysis of intersatellite handover strategy, and find that compared with AMF placed on ground stations and on middle-earth orbit satellites, the handover delay of the proposed scheme is reduced by 40.78% and 13.24%, respectively.

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.

In a low earth orbit (LEO) mega-constellation, each satellite establishes four inter-satellite links (ISLs) with its neighboring satellites to provide high capacity. However, is it necessary to establish so many ISLs in every mega-constellation? This paper investigates redundant ISLs caused by the structure asymmetry. We illustrate the existence of redundant ISLs in a mega-constellation with$P$orbital planes and$S$satellites per plane when$P\neq S$. According to the relative relationship between$P$and$S$, we determine redundant ISLs in mega-constellations with different scales. Then we prove a proposition ensuring that the capacity experiences only a minor decrease after removing redundant ISLs. Guided by the proposition, we propose an asymmetric redundant ISL removal (ARIR) algorithm to remove the maximum number of redundant ISLs while achieving the expected capacity of the complete four- Islconnecting mode. Simulation results validate that our proposed approach can remove at most 25% ISLs and achieve the expected capacity in the classical Starlink constellation.

Mega satellite networks consisting of hybrid orbit satellites play an important role in the sixth generation (6G) wireless networks. Location management (LM) can ensure service continuity for mobile users and is one of the key technologies in mega satellite networks. Moving satellites (e.g., LEO, MEO)1 and users require repeated location updates, which creates the challenge problem of significant LM overhead. In this paper, we propose a novel dynamic hierarchical LM scheme based on the virtual attachment point (VAP). The approach utilizes MEO satellites to provide LM, and divides the direct association between the user and the satellite into two independent steps, including the association between the user and the VAP, and the association between the satellite and the VAP. With this mechanism, the user’s location no longer needs to be updated due to satellite movement. Besides, a dynamic adaptive location area (LA) scheme is proposed to update the user’s location. The scheme can reduce the update frequency of high-speed mobile users, keep the number of paging satellites controllable, and not increase drastically with the constellation scale, thus reducing the paging overhead. The simulation results demonstrate that the proposed LM technology solution can reduce the total LM overhead by at least 40.6%.

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.

Mobile crowdsensing (MCS) has emerged as a popular and promising paradigm for solving challenging problems by utilizing collective wisdom and resources. However, the system architecture and operational rules for MCS have not been well-defined, and obtaining accurate and reliable results from conflicting data collected by workers is difficult due to discrepancies in sensor quality and privacy protection requirements. In this paper, we combine the methodologies of Dynamic Truth Discovery (DTD), Combinatorial Multi-Armed Bandit (CMAB), and Multi-Attribute Reverse Auction to develop a novel MCS ecosystem, with the objective of maximizing the sensing accuracy-aware utility under the budget constraint. We first establish the data collection model by jointly considering the task completion duration as well as the deviation caused by both endogenous errors and privacy protection-oriented injected noise. Then, we theoretically evaluate the accuracy of truth discovery and quantify the contribution of each worker to MCS to form the worker selection criterion. As the qualities of workers are initially unknown, the platform faces the exploration-exploitation dilemma. Therefore, we apply CMAB to transform the worker recruitment problem into a combinatorial arm-pulling problem and elaborately design an Upper Confidence Bound (UCB) algorithm to achieve a desirable exploration-exploitation tradeoff. Moreover, we design an auction-based payment method for the platform, stimulating workers to provide their quoted price honestly while enabling individual rationality. Extensive simulations and comparison results demonstrate the feasibility and effectiveness of our proposed MCS ecosystem.

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.

To deal with the challenges in mobility management of mega low-Earth-orbit (LEO) satellite constellations with long management delays and high signaling overheads, especially under the existing fixed and limited deployments of ground mobility management entities, the cooperative mobility management mode of medium-Earth-orbit (MEO) satellites and ground stations (GSs) has become an attractive tendency. In this paper, considering with the global non-uniform user distribution and constrained satellite storage resources, we propose a dynamic satellite-ground integrated mobility management strategy (DSG-MMS) to cope with the relative mobility among users, GSs, and satellites, which can dynamically decide the optimal GS/MEO management node with the minimal handover and migration delays. Specifically, the DSG-MMS optimization problem is modeled as distributed Markov decision processes, and a reinforcement learning (RL)-based management node selection method is presented to solve them, where each LEO satellite agent dynamically decides its own management node. To further implement the RL algorithm on the resource-limited LEO satellite agents, a novel tensor-based RL algorithm for DSG-MMS is proposed by means of the streamed low-rank tensor decomposition, where only the small-sized core tensor and factor matrices are kept and updated in the strategic optimization so as to realize low storage and computing overheads as well as fast convergence. We perform simulations for the proposed DSG-MMS with parameter configurations of actual Telesat, Kuiper, and Starlink satellite systems to evaluate the mobility management delay and overhead performances as well as the required satellite storage size for mobility management. Moreover, a case study and an architectural comparison are given to demonstrate the superiority of the proposed DSG-MMS than existing methods.

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.

Satellite telemetry, tracking, and command (TT &C) operations are critical to ensuring the normal operation of mega satellite networks. However, the distribution and number of ground stations are limited, making that the existing TT &C mission scheduling methods are difficult to satisfy the TT &C requirements in mega satellite networks, resulting in low TT &C mission completion rates. In this paper, we first construct a space-ground integrated distributed TT &C mission scheduling frame-work utilizing the broad coverage characteristics of geostationary earth orbit (GEO) satellites. Then, we explore the similarity in the TT &C mission scheduling process among adjacent ground stations or GEO satellites, that the TT &C missions execute within the visible time window between satellites and TT &C antennas. Building on this, we share similar features of mission scheduling between the stations through federated learning (FL) and capture the temporal features of TT &C mission scheduling using deep reinforcement learning (DRL) at each station. Therefore, we propose a federated deep reinforcement learning (FDRL) assisting TT &C mission scheduling algorithm in mega satellite networks to enhance TT &C mission completion rates. Finally, the effectiveness of the FDRL algorithm is verified through simulation experiments. Compare to the traditional space-ground integrated algorithm, the FDRL algorithm improves the TT&C mission completion rate by about 26.5 %.

Satellite telemetry, tracking, and command (TT&C) technology plays a critical role in maintaining stable operation and emergency scheduling in mega-satellite networks. However, due to the high-speed movement of satellites, the intermittent connection between the satellite and the ground station creates a dynamic and complex visibility period, leading to temporal resource utilization conflicts during the TT&C mission scheduling process. These conflicts can further exacerbate real-time response for emergency TT&C missions in large-scale satellite networks. To address this issue, we explore the time-aware dynamic characteristics of emergency TT&C missions and their impact on the scheduling time of routine missions. Then we propose an efficient method for reducing resource utilization conflicts based on Dynamic Priority and Minimum Disturbance (DPMD). Building on this method, we formulate the scheduling process of emergency TT&C missions as a reinforcement learning decision problem suitable for dynamic real-time environments. Additionally, we introduce the DRLETMS algorithm (Emergency TT&C Missions Scheduling Algorithm based on Deep Reinforcement Learning) to achieve faster mission scheduling strategies in highly dynamic environments. Simulations demonstrate the superior efficiency of our proposed algorithm in large-scale scenarios compared to typical heuristic algorithms. Furthermore, we examine the impact of various ground station distributions on the real-time TT&C performance of satellite networks under the same satellite scale, which can provide theoretical guidance for designing mega-satellite TT&C networks in the future.

As the type and volume of traffic increase in mega satellite constellations (MSCs), it is still a challenge to use limited resources to improve the traffic completion rate while providing differentiated services for different traffic. Rational allocation of resources depends on the satellite's accurate prediction of non-uniform traffic from the ground. Considering that centralized training methods require high resource occupancy of traffic data transmission and that numerous satellites need to adjust the prediction network frequently when the service area changes, we propose a federated learning-based traffic management strategy (FLTM). We first classify the traffic according to performance requirements such as delay and connectivity, and create network slices to provide differentiated services. Then we train a unified prediction network between multiple service areas in a distributed manner based on federated learning, reducing the frequency of satellites adjusting the prediction network while sharing the traffic information of these areas. To capture spatial and temporal features of traffic, we also propose a prediction framework combining the densely connected convolutional neural network with the Long Short-Term Memory network. Finally, we adopt traffic prediction results to more reasonably allocate resources among slices. Real traffic data verify the accuracy of the proposed prediction framework and simulation results validate that FLTM can provide differentiated services for different traffic and improve the traffic completion rate of MSCs.

The expansion of satellite applications induces the formation of the multi-domain satellite system (MDSS) containing multiple domains with specific applications such as earth resource remote sensing and the Internet of remote things. Resource management is pivotal in enhancing the scheduling capability of the MDSS. However, this is challenging since the dynamic buffer space and communication opportunity, as well as the uncertain data traffic, exacerbate the difficulty of matching satellite resources with data traffic. Moreover, the coexistence of resource competition and collaboration across domains aggravates the dilemma of cross-domain collaboration. In this paper, we propose a hierarchical cross-domain collaborative resource management framework that can flexibly allocate the mission data through local intra-domain and global cross-domain scheduling. Then, to match the uncertain demands of missions with dynamic and limited resources, we propose a multi-agent reinforcement learning-based resource management method to guide collaboration for multi-satellite data carry-forward in a domain. Further, considering resource competition and collaboration in MDSS, we propose a domain-satellite nested matching game data scheduling algorithm to achieve pair-wise stable collaboration of cross-domain satellites. The simulation results indicate that the proposed algorithm improves the amount of offloaded data by 64.4% and 12.7% compared to the non-collaborative and the non-cross-domain schemes, respectively.

Convolutional neural networks (CNNs) have been successfully employed in remote sensing image classification because of their robust feature representation for different visual tasks and powerful graphics processing units (GPUs). The attendant problem is that high computational cost and high memory footprint hindering the application of CNNs for remote sensing applications in resource- and time-sensitive situations. Based on practical deployment requirements, we pioneer a pruning-quantization joint learning model compression method for remote sensing image classification, called co-compression via superior gene (CC-SG). An enhanced evolution algorithm (EEA) is adopted as the agent to search a “superior gene,” and immediately following, a director receives the “superior gene” and gives a compression mask and a resource constraint feedback to the agent. The network is eventually compressed and fine-tuned according to the optimal compression mask. Specifically, we introduce gene age and progressive shrinkage mutation rate to EEA and design a fitness function that balances accuracy and resource constraints. As validated using the UC Merced land-use and NWPU-RESISC45 datasets, the proposed CC-SG demonstrated superiority over other compared model compression approaches. For example, CC-SG attained substantial bit operations (BOPs) compression ratio of 40.04 with 0.956% accuracy increase for VGG-16 on UC Merced land-use dataset and 40.00 with 0.203% accuracy increase for ResNet-56 on NWPU-RESISC45 dataset. The code is available athttps://github.com/fanxxxxyi/CC-SG.

The new generation satellite network is a comprehensive service system that can provide communication, observation, navigation, and other functions. Typically, a system with a specific service function is defined as a domain and the supply and demand relationship of resources, such as communication, storage, and energy resources, are unbalanced among domains. Therefore, it is nontrivial to accurately characterize the cross-domain resource state and coordinate the data transmission policy of interrelated satellites from different domains to make full use of the resources in each domain aiming at improving the resource utilization ratio (RUR) of the whole network. To this end, this paper investigates the cross-domain resource scheduling (CDRS) problem in satellite networks aiming at maximizing the total amount of downloaded transmitted data. We start with the orbit motion law of satellites and construct the satellite orbit feature matrix of each domain to design a hierarchical sparse resource representation (HSRR) scheme to accurately characterize the resource state of each domain in real-time with low complexity. Further, based on the HSRR, we develop a cross-domain dynamic multi-resource scheduling algorithm to solve the CDRS problem by introducing the advantage factor and policy-oriented hyper-parameter. The algorithm can make full use of the resources in each domain to achieve efficient CDRS by dynamically adjusting the data transmission policy of satellites among domains. Simulation results show that the greater the service demand and resource difference among domains, the more significant the performance improvement brought by CDRS, and compared with the existing algorithms, the RUR and resource utilization efficiency have been significantly improved.

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.

The number of satellites in the current low-Earth-orbit (LEO) satellite networks continues to grow to form a mega LEO satellite constellation (MLSC). This large-scale net-working effectively improves network coverage and capacity. However, denser satellite deployment brings more severe chal-lenges to satellite handovers, such as frequent handovers, which exponentially reduce the system performance (e.g. probability of service success (PSS)) when considering inherent handover failures. To ensure service continuity, this paper focuses on the relationship between the constellation scale and handover times under seamless coverage. Specifically, we first conduct spatial geometric analysis and probabilistic analysis to derive three new conditions for MLSC seamless coverage. Then, we analyze the tradeoff relationships between the handover times and satellite coverage duration with the given constellation scale. Furthermore, we construct a mathematical relationship between the constellation scale, handover times, and PSS, indicating the tradeoff between constellation scale and system performance. The analysis effectively guides to design or adjust the constellation scale, satellite altitude, satellite coverage angle, and handover strategy according to the requirements of system performance, which has important theoretical value for MLSC system design and future research.

Satellite-terrestrial networks (STNs) consisting of satellite segment and ground segment have been regarded as a desirable solution for 6G. Efficient cooperative resource scheduling strategies, which cover the cooperation in satellite segment for data relay and the cooperation between satellite segment and ground segment for data downloading, play a pivotal role in enhancing the system performance in STNs. Since the dynamic channel condition and energy feeding greatly influence the network status, cooperative resource scheduling should be adaptive to the future environmental fluctuation. In this paper, we model the cooperative resource scheduling problem in STNs as a resource limited Markov Decision Process (MDP). Considering the fact that satellites are unaware of future environmental status, the traditional static optimization solution is infeasible. Therefore, we propose a Deep Reinforcement Learning (DRL) based Cooperative Store-and-Relay Resource Scheduling Algorithm (CSR-RSA), where inter-satellite links are utilized to coordinate with intermittent satellite-ground links for improving the transmission performance of the network. By exploiting the proposed CSR-RSA, the well-trained neural networks can be obtained to generate the adaptive and cooperative resource scheduling strategy without the knowledge of future environmental status. Simulation results verify the effectiveness of the proposed algorithm compared with traditional algorithms.

As the spaceborne resources are heterogeneous and the satellite network topology changes constantly, various time-varying derivative graphs are designed to represent the data acquisition and delivery process in satellite-terrestrial integrated networks (STNs). However, the time complexity of resource allocation approaches based on assorted time-varying graphs is remaining obstinately exponential. Derived from the satellite vertical coverage feature, we design a more general relationship to involve a group of nodes in a hyperedge rather than the bilateral relationship between two nodes. In this paper, we propose a time-expanded hypergraph (TEH) to contract the adjacency matrix of the network topology. Based on the proposed TEH, the problem is formulated to minimize the consumption of the communication resource in the resource-limited STN while completing the same number of tasks. Since the problem is intractable by exhaustive search, we further propose a hybrid hyperedge and Lagrangian relaxation algorithm to perform optimal resource allocation through an oriented search for the feasible hyperedges to reduce the scale of searching. The simulation results validate that the proposed algorithm can effectively complete the tasks with lower time complexity.

The space–air–ground integrated network (SAGIN)-related issues are attractive in the sixth generation (6G) technologies, which facilitate the global coverage and seamless service. The cooperation of high altitude platforms (HAPs) and low-Earth orbit (LEO) satellites provides the remote area users with comprehensive coverage and service. In this work, we consider the cooperation of HAPs and LEO satellites in SAGIN to serve terrestrial users in the remote area for data collection and transmission. To deal with the periodical motion of LEO satellites, we employ the time expanding graph (TEG) to represent the multiple resources in SAGIN and depict task flow transmission processes. Based on TEG, we aim to maximize the total data received by the ground data processing center in a time horizon, considering multiple resource constraints and flow restrictions. The original problem is formulated in the form of mixed-integer linear programming, which is intractable to obtain the optimal solution by brute-force searching. To alleviate this intractability, we propose the Benders decomposition-based algorithm to obtain the optimal solution within an acceptable time complexity via iterations between the master problem and subproblem. Moreover, to further expedite the solution in large-scale systems, an acceleration algorithm is proposed by handling the master problem with an approximation algorithm and the subproblem with a unit-flow-based algorithm. Finally, simulations are conducted and the numerical results verify the efficiency of the proposed algorithms, and the effects of various network parameters are analyzed as well.

Low Earth orbit (LEO) satellite constellations have become a promising architecture to integrate with terrestrial networks for facilitating communications and Internet of Remote Things (IoRT) services through gateways. Nevertheless, different gateway locations may have various channel conditions and service demands due to differentiated atmospheric conditions, populations, and number of terminal devices required by IoRT services in different areas. Besides, the gateway placement scheme further affects the service coverage performance and the access performance of the network to service demands. Therefore, gateway placement plays a pivotal role in improving network capabilities in the integrated system of LEO satellites and terrestrial networks (ISoLS-TNs). Motivated by the aforementioned facts, in this article, we first formulate the gateway placement problem in ISoLS-TNs as a multiobjective optimization problem to maximize the total revenue of service data demand within coverage while minimizing the average access distance and the number of deployed gateways. In order to enhance network resource utilization and assure local information confidentiality, a distributed resource allocation (DRA) mechanism based on the alternating direction method of multipliers (ADMMs) algorithm is designed to calculate the total revenue of service data demand within coverage. Furthermore, we propose a genetic-based gateway placement algorithm with the ADMM for DRA. Finally, through massive simulations based on real data, we validate the effectiveness of the proposed algorithm in improving resource utilization and coverage performance of the network. In addition, the results also bring lights on the relationship between service data demand distribution and gateway location preference.

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.

Satellite networks (SNs) are heterogeneous networks composed of typical functional domains, such as communication domains, observation domains, etc. Since the resources are independent among domains, and are highly dynamic with the movement of satellites, it is extremely difficult to capture the potential interaction relationship among various resources, even less to realize the coordinated scheduling of cross-domain resources in large-scale SNs. Motivated by these factors, we firstly propose a multi-aspect expanded hypergraph (MAEH) to accurately depict the Spatio-temporal features of resources in various domains as well as functional property, which is presented by different aspects. Particularly, the “aspect” can involve a group of resources with a similar feature in a hyperedge rather than a bilateral relationship between two individuals, thus the MAEH can dramatically reduce the redundant connections. By exploiting the MAEH, we model the multi-domain resource allocation problem in the form of mixed-integer linear programming to maximize the completed tasks. Through the topological nested characteristic of the MAEH, we propose a two-stage scheme to accomplish the resource allocation rapidly with lower computational complexity and to improve the resource utilization ratio efficiently. Simulation results validate that compared with the optimal performance, the executive time of the proposed scheme is average fivefold less under 4% extra communication resource cost. Besides, the resource utilization ratio improves over 15% by cross-domain collaboration.

Compared with traditional observation satellites, agile earth observation satellites are capable of prolonging observation time windows (OTWs) for targets, which significantly alleviates observation conflicts, thereby facilitating imaging data collection. However, it also leads to more uncertainties in determining the start time to image targets within these longer OTWs for an agile satellite network (ASN) to collect imaging data. Furthermore, these collected data are offloaded only within short transmission time windows between data collectors and data sinks, thus resulting in a transmission scheduling problem. Toward this end, this paper investigates joint observation and transmission scheduling in ASNs, aiming at accommodating more imaging data to be collected and offloaded successfully. Specifically, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of scheduled imaging tasks. Then, we explore the hidden structure of this ILP and transform it into a special framework, which can be solved efficiently through semidefinite relaxation (SDR). To reduce computation complexity, we further propose a fast yet efficient algorithm by combining the advantages of the devised SDR method and a genetic algorithm with special population initialization. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the weighted sum of scheduled tasks.

The network control plays a vital role in the mega satellite constellation (MSC) to coordinate massive network nodes to ensure the effectiveness and reliability of operations and services for future space wireless communications networks. One of the critical issues in satellite network control is how to design an optimal network control structure (ONCS) by configuring the least number of controllers to achieve efficient control interaction within a limited number of hops. Considering the wide coverage, rising capacity, and no geographical constraints of space platforms, this paper contributes to designing the ONCS by constructing an optimal space control network (SCN) to improve the temporal effectiveness of network control. Specifically, we formulate the optimal SCN construction problem from the perspective of satellite coverage factors, and apply geometric topology analysis to derive both the conditions for constructing the optimal SCN and the formulaic conclusions for SCN and MSC configurations (i.e., scale and structure). From numerical results, we investigate the tradeoff between network scale, the number of controllers, and control delays in several satellite network control scenarios, to provide guidelines for the MSC control. We also design the optimal SCN for an existing MSC system to demonstrate the effectiveness of the proposed ONCS.

With the growth of global communication service demand, constructing large-scale satellite networks has become the future development trend for improved system performance. However, due to the high-speed orbit motion of satellites, the connection relationship of network topology (CRNT) is complex and changeable. This phenomenon is particularly pronounced in large-scale satellite networks and the existing representation schemes of CRNT for large-scale satellite networks have high space complexity. Therefore, we explore the sparse characterization of the inter-satellite visibility matrix and propose an integrated sparse space-time resource representation (ISST-RR) scheme to efficiently characterize the satellite network communication resources with low complexity from the dimension of time and space. On the basis of the proposed ISST-RR scheme, we further propose a multi-agent reinforcement learning with sparse representation based resource scheduling (MARLSR-RS) algorithm to obtain the optimal resource scheduling policy. Simulations demonstrate the efficiency of the proposed MARLSR-RS algorithm in terms of communication resource utilization. In addition, we investigate the impact of several typical netwrok parameters, e.g., transmission rate of observation satellites on network performance, which can provide a theoretical guidance for system design.

We propose a UAV-relayed covert communication scheme with finite blocklength to maximize the effective transmission rate from the transmitter to the legitimate receiver against a flying warden. The transmitter adopts the maximum ratio transmission and the relay performs Gaussian signaling transmission to cause uncertainty at the warden. First, the optimal detection thresholds are derived at the warden towards the transmitter and the relay, respectively. Then, the hovering location of the warden is optimized to maximize the summation of relative entropies from the transmitter and the relay, which can greatly threaten the covertness. With this worst covert situation, the blocklength and transmit power at the transmitter and the relay are jointly optimized under the constraint of the error detection probability to maximize the effective transmission rate from the transmitter to the legitimate receiver. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-relayed covert communication scheme.

The aerial access network (AAN) is a significant issue in the sixth generation (6G) technologies. In this work, we focus on the terrestrial data collection and transmission by AAN. In detail, high altitude platforms (HAPs) are considered as the aerial access devices and low earth orbit (LEO) satellites assist the data collected by HAPs to complete transmission. To deal with the intractable dynamic topology of AAN, the time expanding graph (TEG) is employed to represent the multiple resources and depict the data flow transmission process. Based on TEG, we aim to maximize the total data received at the ground data processing center, considering the multiple resource restrictions of HAPs and LEO satellites, as well as the flow conservation constraints in TEG. The problem is in the form of mixed integer programming, and it is intractable to obtain the optimal solution, especially in large-scale AAN. To alleviate the intractability, we propose the Benders decomposition based algorithm to obtain the optimal solution within an acceptable time complexity. Simulations are conducted and numerical results verify the effectiveness and efficiency of the proposed algorithm.

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%.

An efficient resource allocation algorithm plays a pivotal role in the performance improvement of space information networks (SIN). The dynamic of resources and mission requirements in the network has a great influence on resource allocation. Guiding rapid satellite resource allocation to adapt to dynamic changes of the network by discovering the similarity in the structure of changing missions is a key technology to improve SIN performance. In this paper, we firstly formulate satellite resource allocation as a problem aiming to maximize the total network benefits. Then, we propose a satellite resource allocation algorithm based on Hopfield to solve resource allocation problems in SIN. To accommodate the dynamics of satellite network missions and quickly solve resource allocation problems, we further propose a satellite resource allocation algorithm based on transfer learning to learn the node selection strategy in the Hopfield network. Specifically, we use a small number of additional training samples to better adapt to the changes in mission structure. Simulation results validate that the proposed method can achieve high performance while reducing computational complexity.

Non-terrestrial network (NTN) communication is included in the 3GPP standard because of its excellent features such as resistance to ground physical attacks and wide coverage. Since the available power and storage resources are limited on high throughput satellites (HTSs), optimizing the resource allocation can greatly improve the performance of the HTS based communication system. Notably, weather and other factors make the channel status between the satellite and the terrestrial base stations constantly change. In this paper, we first exploit the model-free feature of reinforcement learning to formulate the aforementioned dynamic and unpredictable channel conditions into the power allocation problem in HTS systems. Due to the complexity of environment state and power allocation, a deep neural network is introduced to replace the Q table, and a deep reinforcement learning framework is built. Furthermore, a power allocation algorithm based on a deep reinforcement learning framework is presented. Finally, the simulation results show that the proposed algorithm is superior to existing algorithms in terms of the long-term system throughput performance, and has better adaptability to dynamic channel environment, thereby improving network performance.

In satellite networks, information can be transmitted either directly by inter-satellite links or the movement of satellites carrying. Consequently, how to quantify network capacity, considering both the carrying and transmission capability of satellites is crucial to the deployment of satellite networks. In this paper, we define the capacity of satellite networks consisting of both, and propose a strategy to exploit the mobile carrying of satellites under the constraint of service requirements. Then, we reveal the theoretical relationship between satellite carrying and the network capacity. The theoretical analysis and simulated results show that 1) satellite carrying can improve the network capacity when the service delay constraints could be released; 2) The capacity gain from satellite carrying is influenced by network parameters, such as orbital altitude, number of satellites, and storage capacity.

As the sixth generation (6G) network is under research, and one important issue is the aerial access network and terrestrial-space integration. The Internet of Remote Things (IoRT) sensors can access the unmanned aerial vehicles (UAVs) in the air, and low Earth orbit (LEO) satellite networks in the space help to provide lower transmission delay for delay-sensitive IoRT data. Therefore, in this article, we consider the LEO satellite-assisted UAV data collection for the IoRT sensors. Specifically, a UAV collects the data from the IoRT sensors, then two transmission modes for the collected data back to Earth: 1) the delay-tolerant data leveraging the carry-store mode of UAVs to Earth and 2) the delay-sensitive data utilizing the UAV-satellite network transmission to Earth. Considering the limited payloads of UAVs, we focus on minimizing the total energy cost (trajectory and transmission) of UAVs while satisfying the IoRT demands. Due to the intractability of direct solution, we deal with the problem using the Dantzig-Wolfe decomposition and design the column generation-based algorithms to efficiently solve the problem. Moreover, we present a heuristic algorithm for the subproblem to further reduce the complexity of large-scale networks. Finally, numerical results verify the efficiency of the proposed algorithms and the advantage of LEO satellite-assisted UAV trajectory design combined with the data transmission is also analyzed.

With the growing demand for communications in remote and dispersed areas, Internet-of-Remote Things (IoRT) networks with joint unmanned aerial vehicle (UAV) access and geostationary orbit (GEO) satellite backhaul hold great promise to provide sufficient access services to Internet-of-Things (IoT) users and devices. As the fundamental of the performance optimization of IoRT networks, the performance analysis sheds light on the relationship between the network performance (i.e., backlog, delay, and throughput) and access scale (i.e., the numbers of UAVs and UAV users). Aiming at the challenges brought by the complex network structure (i.e., two-level queuing network along with the converged traffic), we introduce the stochastic network calculus-based min-plus convolution and the leftover service to mathematically describe the complex structure. For the analytical challenges of the continuous-time arrival process and heterogeneous two-level link capacities, we innovatively prove their supermartingale features and further derive the closed-form expressions of the network backlog and delay bounds based on the martingale theory. To pursue higher throughput while guaranteeing delay performance, we formulate a mixed-integer optimization problem of the access scale that contains a nondifferentiable variable derived from a transcendental equation. For the tractability, we propose a three-directional iterative (TDI) algorithm to search the optimal solution of the optimization problem. Simulation results verify the tightness of our performance bounds in contrast to the standard bound and the effectiveness of the proposed algorithm.

Space-air-ground networks play important roles in both fifth generation (5G) and sixth generation (6G) techniques. Low earth orbit (LEO) satellites and high altitude platforms (HAPs) are key components in space-air-ground networks to provide access services for the massive mobile and Internet of Things (IoT) users, especially in remote areas short of ground base station coverage. LEO satellite networks provide global coverage, while HAPs provide terrestrial users with closer, stable massive access service. In this work, we consider the cooperation of LEO satellites and HAPs for the massive access and data backhaul of remote area users. The problem is formulated to maximize the revenue in LEO satellites, which is in the form of mixed integer nonlinear programming. Since finding the optimal solution by exhaustive search is extremely complicated with a large scale of network, we propose a satellite-oriented restricted three-sided matching algorithm to deal with the matching among users, HAPs, and satellites. Furthermore, to tackle the dynamic connections between satellites and HAPs caused by the periodic motion of satellites, we present a two-tier matching algorithm, composed of the Gale-Shapley-based matching algorithm between users and HAPs, and the random path to pairwise-stable matching algorithm between HAPs and satellites. Numerical results show the effectiveness of the proposed algorithms.

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.

Owing to the ever increasing of information privacy requirement, covert communication has gained more and more attention, whose effective range is limited by the trade-off between the covertness and the transmit power. Benefiting from the high mobility and easy deployment, unmanned aerial vehicles (UAVs) can be utilized to expand the range of covert networks. Thus, we propose a UAV-relayed covert communication scheme with finite blocklength to maximize the effective transmission bits from the transmitter to the legitimate receiver against a flying warden. The transmitter adopts the maximum ratio transmission and the relay performs Gaussian signalling transmission to cause uncertainty at the warden. First, the optimal detection thresholds are derived at the warden towards the transmitter and the relay, respectively. Then, the hovering location of the warden is optimized to maximize the summation of relative entropies from the transmitter and the relay, which can greatly threaten the covertness. With this worst covert situation, the blocklength and transmit power at the transmitter and the relay are jointly optimized under the constraint of the end-to-end error detection probability to maximize the effective transmission rate from the transmitter to the legitimate receiver. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-relayed covert communication scheme.

In ultra-dense networks (UDNs), the dense deployment of base stations (BSs) is facing challenges due to the pronounced unbalanced traffic loads, severe inter-cell interference, and uncertain traffic demands. In this paper, we tame traffic uncertainty for the joint optimization of BS activation and user association in UDNs to mitigate interference and balance traffic loads among BSs. Specifically, we address the traffic uncertainty by using chance constraint programming with the known first- and second-order statistics of the uncertain traffic. We formulate the joint BS activation and user association problem as a mixed integer non-linear programming problem, which is then decomposed into a set of user association sub-problems by modeling the BS states (active or idle) as a Markov chain. We solve the user association sub-problem at each BS state by transforming it into a convex problem over the positive orthant. In particular, at each BS state, the candidate serving BSs that lead to the optimal load balancing performance are identified for each user and parts of the user's traffic are offloaded to the identified BSs. Based on the obtained solutions, we propose a distributed near-optimal BS activation and user association scheme. Numerical results demonstrate that our proposed scheme is more robust to traffic uncertainty and provides better load-balancing performance than the existing schemes.

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.

Low earth orbit (LEO) satellite networks will play important roles in the sixth generation (6G) communication system. Software defined network technique is a novel approach introduced to the LEO satellite networks to improve the resource flexibility and efficiency, forming the software defined LEO satellite networks (SDLSNs). How to efficiently allocate the resources of SDLSN to provide services for the terrestrial users is a key issue. Hence, in this work, we explore the service provision for SDLSN via virtual network functions (VNFs) orchestration on the software defined time-evolving graph. In view of the scarce, intermittent and unstable satellite-to-satellite (S2S) links, the problem is formulated to minimize the S2S resource consumption while satisfying the terrestrial tasks, which is in the form of integer linear programming. Since the problem is intractable by exhaustive search, we design a branch-and-price algorithm based on the coupling of Dantzig-Wolfe decomposition, column generation, and branch-and-bound to efficiently acquire the optimal solution. Further, to obtain a faster solution for practical usage, we further design an approximation algorithm for the subproblem and leverage the beam search to accelerate the pruning for the search tree. Finally, extensive simulations are conducted and the numerical results validate the effectiveness of the proposed schemes.

Content caching has limitations on achieving cache gains (e.g., cache hit ratio) in small cell networks, due to limited storages of small base stations (SBSs) and inherent user demand patterns (i.e., initial content preferences). Two effective approaches have been proposed to exploit the potential of content caching: SBS cooperation to utilize cache storage, and proactive content recommendation to shape user demand. In this paper, we investigate cooperative content caching and recommendation to maximize cache gains, while guaranteeing users' satisfaction by recommending appealing content items. We propose a generic framework for cooperative content caching and recommendation, based on which we propose an online and distributed scheme by designing a continuous-time Markov chain (CTMC). In particular, online content caching (a.k.a., content replacement) is implemented by hopping from one cache state to another in the CTMC, while content recommendation is performed heuristically through sequential fixing at each cache state. Besides, we characterize the performance gap between our proposed scheme and the theoretical optimum in terms of cache hit ratio. Simulation results demonstrate that the proposed scheme achieves better cache hit ratios than other schemes in single-BS scenarios, and provides a competitive solution in multiple-BS scenarios.

Satellite networks have been regarded as a promising architecture for supporting the Internet of remote things (IoRT) due to their advantages of wide coverage and high communication capacity in remote areas, which further promotes the development of the satellites for IoRT networks (SIoRTNs). The effectiveness of multi-dimensional resource collaboration has significant impacts on the IoRT data downloading performance. However, the environment ’ s dynamics, e.g., channel conditions and solar infeed process, are unknown in practical scenarios, which poses daunting challenges in making efficient utilization of multi-dimensional resources. Motivated by this fact, we model the joint resource scheduling and IoRT data scheduling problem with the aim of maximizing the amount of the IoRT data of the overall network by applying the model-free reinforcement learning framework. To overcome the limitations of traditional reinforcement learning algorithms, we propose several feature functions by investigating the natural attributes of the multi-dimensional resources of the SIoRTNs, and further exploit the concept of function approximation to approximate the expected downloaded IoRT data given the network state. Furthermore, we propose a state-action-reward-state-action (SARSA) based actor-critic reinforcement learning (SACRL) resource allocation strategy to achieve the optimal resource allocation and IoRT data scheduling with casual information at LEO satellites. Simulations validate the convergence property and the effectiveness of the proposed SACRL algorithm in terms of the amount of the downloaded IoRT data. Particularly, we investigate the impact of typical network parameters on network performance to further provide guidance for future SIoRTN system design.

The satellite data relay networks (SDRNs) hold great promise in 6G communications for the timely offloading of the global traffic. Since the delay performance is regarded as one of the most important metrics reflecting the offloading efficiency, studying its relationship with network parameters becomes really essential to the development and application of the SDRN. However, the complex data offloading process and heterogeneity of traffic arrivals and transmission links pose many challenges to the stochastic delay analysis. To accurately model the data offloading process in SDRNs, we build a series-parallel queuing model with through and cross traffic while considering the propagation delay. On this basis, we respectively propose a propagation delay embedded min-plus convolution method based on stochastic network calculus and a Markov chain method based on Monte Carlo to depict the leftover services of the heterogeneous links received by the per-flow traffic in an aggregate. To eliminate the impacts of the heterogeneity, we uniformly characterize the arrivals and leftover services by their moment generating functions (MGFs) which contain the full moment information, and shield the heterogeneity by deriving the envelopes of the arrivals and leftover services with the help of MGFs, Chernoff bound and union bound. Then, in the light of the geometric relationship between the envelopes of the arrivals and leftover services, we analyze the upper bounds of the stochastic delay, which provides the guidance to the network configuration. Eventually, simulation results verify the effectiveness of the theoretical analysis and further reveal maximum four times the delay difference between the heterogeneous links influenced by traffic type, burstiness, and access number.

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.

Software defined network technique is a novel approach introduced to manage low earth orbit (LEO) small satellite networks. One important challenge is the allocation of the scarce virtualized satellite network resources in space environment. We devise a virtual network functions orchestration based model to implement the virtualized resources management for LEO satellite networks. This model is formulated as an integer linear programming (ILP) problem. Further, we propose a method combining Dantzig-Wolfe decomposition, column generation and branch-and-bound algorithm for the ILP problem to attain the optimal solution. Finally, simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

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.

Mobile-Edge Computing (MEC) could relieve computing pressure and save energy of resource-constrained Smart Mobile Devices (SMDs) via computation offloading. Nevertheless, offloading strategy design for multiuser MEC systems is challenging. Specifically, offloading operations (i.e., terminal execution strategy, access rate, and cloud execution strategy) are not only inner-coupled for each SMD due to parallel local and cloud execution, but also inter-coupled among SMDs due to competition for radio and computation resources. Worse still, the inner- and inter-coupling interplay each other. However, existing works generally weaken this inner-inter-coupling, resulting in an inability to adapt to network differences, terminal capacity differences, and application requirements differences. Hence, only suboptimal performance could be achieved. As motivated, we jointly optimizes terminal execution strategy, radio resource allocation, and MEC computation resource allocation to minimize weighted sum of terminal energy consumption. Additionally, via dynamically matching individual offloading behavior and group's competitive resources allocation, our proposed algorithm could not only reflect mechanism of interaction between inner- and inter-coupling relationship, but also well adopt to diversities of network conditions, terminal capacity, and application requirements to further harvest MEC gain. Finally, simulation results demonstrate that our algorithm significantly outperforms existing schemes, more specifically up to 73.8% less energy consumption.

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.

Satellite Data Relay Networks (SDRNs) play an important role in the data relay from User Satellites (USs) to ground stations by Tracking Data Relay Satellites (TDRSs). For better exploitation of SDRNs, the development of the systematic model and accurate system analysis is essential. To describe the end-to-end data transmission in SDRNs, we construct an MMOO/MMSP/1/K-G/G/1 tandem queuing model where the two parts depict the traffic arrival and transmission service of USs and TDRSs, respectively. Because the active and inactive periods of the data transmission are determined by the visibility between two satellites, classical buffer state based vacation policies become imprecise. Moreover, these two kinds of periods appear alternatively and their duration varies over time so that it is hard to model such intermittent transmission by existing service models. To overcome these difficulties, we propose a Markov Chain Monte Carlo based Markov Modulated Service Process (MMSP) which can tightly match the distributions of the active and inactive periods. In this process, we propose two algorithms to calculate the service state transition probability and the number of the sub-states in each service state, respectively, which guarantees the alternative transition between the active and inactive states as well as the sojourn time spent in each state. For the quality of service analysis, we find the different features of the queue variation under different arrival and service rate conditions. By separately calculating the related mean queue lengths and emergence probabilities, we first derive the expressions of the system loss probability, mean queue length, and mean delay. Finally, we conduct numerous simulations to verify the accuracy of our system model and performance evaluation, which provides the guidance to the buffer design and transmission resource allocation.

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.

In cloud radio access network (C-RAN), computation-intensive tasks can be offloaded from mobile devices (MDs) to the powerful computing node in C-RAN, i.e., baseband unit (BBU) pool, through cooperation radio at remote radio heads (RRHs), for effective task processing and improved user experience. In the existing works, computational resources in the BBU pool are always allocated to MDs exclusively, resulting in poor resource utilization and deteriorative task processing delay. Alternatively, we adopt a sequential computation model to enhance computing performance, which is proved through theoretical analyses in this paper. In this model, a task scheduling issue should be addressed in the BBU pool to determine the optimal processing order for tasks. Then, one task's completion time is jointly determined by its scheduling order and arrival time in the BBU pool. Hence, to minimize the maximum task completion time, we jointly optimize cooperative radio at RRHs and task scheduling in the BBU pool. By leveraging the specific property of formulated problem, we propose an effective computation offloading algorithm to achieve a local optimal solution in block coordinate descent manner. Finally, simulation results present the convergence and advantage of our proposed algorithm.

Constructing energy-efficient data center networks (DCNs) is becoming increasingly significant. In the hybrid DCNs with both wired and wireless links, reconfigurable wireless links can effectively reduce the routing path length and the usage of the switch, thereby greatly reduce the energy consumption DCNs. In this paper, we propose an energy-efficient hybrid flow routing and antenna scheduling scheme for tree-based hybrid DCNs. Firstly, the original problem of hybrid routing and scheduling is decomposed into two subproblems by taking advantage of the wireless energy-saving features. Then, a novel weight-relaxing-rounding algorithm is developed to solve the first subproblem. Specifically, the topology characteristics of the tree-based DCNs is used to perform weight transformation and link remapping, and then to find energy-efficient wired subnet. After that, the relax-and-rounding technology is adopted to obtain energy-efficient wireless links scheduling solution. Finally, the numerical results show that the proposed scheme can achieve a near optimal solution when the network scale is small. As the network scale increases, the proposed scheme is still able to save more energy than the existing algorithm.

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.

Content caching is a promising way to overcome backhaul limitations in small cell networks. However, in such type of networks, small base stations (SBSs) are always deployed with limited cache storages. Thus, it is necessary for SBSs to adjust their contents for better caching efficiency, so as to reduce backhaul traffic. In this paper, we study the content replacement problem to minimize the traffic flowing into the costly backhaul links. However, in small cell networks where SBSs make up backhaul mesh networks, the effectiveness of reducing backhaul traffic depends on the hop distance from the content location to the requesting user. On this basis, we formulate a hop minimization problem that is inherently combinatorial. Through log-sum-exp approximation, we can solve the problem and arrive at a close-form solution with guaranteed performance gap to the optimal solution. By exploiting the properties of continuous-time Markov chain (CTMC), the solution can be implemented by designing a CTMC that can instruct the content replacement process. As a consequence, a concise, efficient, and flexible content replacement strategy is proposed. Simulation results verify our analysis and show that our proposed strategy outperforms the conventional strategies.

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.

Tracking and Data Relay Satellite System (TDRSS) is playing an important role in data relay for user satellites. Subject to the finite number of antenna, the non-negligible antenna slewing time, and the time-varying connectivity of inter-satellite links (ISLs), it is significantly challenging to improve the selection of antenna scheduling sequence to improve performances (e.g., higher throughput, shorter mean queue length, smaller mean scheduling number, etc). To overcome above challenges, we utilize the antenna slewing model, the track model, and the multi-queue single-server queuing model to calculate the satellite-specific attributes such as the practical antenna slewing time, the link availability period and the buffer state. Furthermore, we propose a Heuristic Algorithm based on Optimal Weight (HAOW) considering the obtained satellite-specific attributes to optimize the antenna scheduling sequence. With the optimized scheduling sequence, the network performances are analyzed by the proposed queuing model. Finally, we conduct numerous simulations for the performance comparisons of the proposed HAOW with classical scheduling algorithms.

Recently, satellite networks have played an increasingly important role in both military and civilian fields. With the continual growth of the network size, the assessment of link criticality is of great significance to protect or attack satellite networks. With regard to the dynamic topologies and store-carry-forward transmission paradigm in satellite networks, detecting critical links should fully consider the relationship of consecutive snapshots and the key performance of the traffic, which raises great challenges. In this paper, we explore critical link sequence of satellite networks from the perspective of delay. We first formulate the problem based on the time-expanded graph model and discuss its convexity. Then, by exploring the space-time relationship between the criticality of different link at different slots, a heuristic critical link sequence detection algorithm (CLSD) is proposed. The simulation proves that deleting the critical link sequence given by the algorithm can effectively prolong the minimum transmission delay of the network and verifies the importance of network vulnerability assessment from the perspective of delay.

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.

Small satellite networks (SSNs) have attracted intensive research interest recently and have been regarded as an emerging architecture to accommodate the ever-increasing space data transmission demand. However, the limited number of on-board transceivers restricts the number of feasible contacts (i.e., an opportunity to transmit data over a communication link), which can be established concurrently by a satellite for data scheduling. Furthermore, limited battery space, storage space, and stochastic data arrivals can further exacerbate the difficulty of the efficient data scheduling design to well match the limited network resources and random data demands, so as to the long-term payoff. Based on the above motivation and specific characteristics of SSNs, in this paper, we extend the traditional dynamic programming algorithms and propose a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future. We further formulate this stochastic data scheduling optimization problem as an infinite-horizon discrete Markov decision process (MDP) and propose a joint forward and backward induction algorithm framework to achieve the optimal solution of the infinite MDP. Simulations have been conducted to demonstrate the significant gains of the proposed algorithms in the amount of downloaded data and to evaluate the impact of various network parameters on the algorithm performance.

The emerging distributed satellite cluster network (DSCN) holds great promise in various practical fields, including earth observation, disaster rescue, and tracking of forest fires. In the DSCN environment, it is essential to achieve the best data delivery performance by coordinating multi-dimensional heterogeneous and dynamic resources. However, in real-world applications, the distribution of long-term data arrival is not often fully known. Motivated by this fact, we propose a distributionally robust two-stage stochastic optimization framework with considering the dynamic network resources and the partially known distribution information of long-term data arrival. Aiming at maximizing the total network reward, we formulate a two-stage stochastic flow optimization problem based on the extended time expanded graph. Then, we introduce an ambiguity set for the uncertain distribution of the long-term random data arrival inspired by the idea from the distributionally robust optimization. On the basis of the proposed ambiguity set, we further propose a data arrival distribution robust two-stage recourse (DADR-TR) algorithm by converting the original stochastic optimization problem into a deterministic cone optimization problem, which is computationally tractable. The extensive simulations have been conducted to evaluate the impact of various network parameters on the algorithm performance and further validate that the proposed DADR-TR algorithm can achieve high data delivery performance without full distribution information of the long-term data arrival.

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 recent years, the dramatically rising mobile data traffic has required an ever-increasing data rates. Ultra-dense network (UDN) is a promising technique to significantly enhance the network capacity by densely deploying small cells. The large amount of traffic also has been characterized by asymmetry and variations in both time and space. Dynamic time-division duplex (DTDD) has been taken into account to accommodate the traffic due to its advantage in the dynamic adjustment of UL/DL configuration. In this work, we propose an analytical framework to investigate the coverage performance of a UDN operating DTDD scheme, where impacts of line-of-sight (LOS)/non-line-of-sight (NLOS) propagation in large-scale fading and small-scale fading are both incorporated. Our results show that the LOS propagation in small-scale fading can substantially improve the DL and UL coverage probabilities in the low-to-middle density region, while the enhancement vanishes in the ultra-dense region where the network coverage is dominated by the LOS propagation in large-scale fading.

Remote sensing satellite (RSS) plays an increasingly important role in satellite networks. Current studies have paid wide attention to system modeling and performance analysis of RSS traffic acquisition, storing and transmission processes. However, in traffic acquisition process, the transitions between the "on" state and the "off" state of the on-board sensor generally exhibit a Markovian feature. Besides, the continuous stream traffic arrives in the "on" states and no traffic arrives in the "off" states. These features have not been sufficiently investigated. Meanwhile, the idiomatic Poisson traffic models are no more accurate, which inevitably brings great challenges to precise performance analysis. Aiming at above features, we present a Markov Modulated Deterministic Process (MMDP) model to simulate the traffic acquisition process. Afterwards, according to the global coverage of relay satellites, we describe the integrated traffic acquisition, storing and transmission processes as an MMDP/D/1/K queueing model. Further, we derive the closed-form expressions of some important quality of service indices (i.e., the loss probability, the average queue length and the average delay). Finally, we conduct numerous simulations to verify the effectiveness of theoretical results.

Caching content in devices and sharing content via device-to-device (D2D) communications is a promising way to offload the cellular data traffic. In fact, the distribution of caching content has a direct effect on the behavior of D2D communications and thus the results of content sharing. On the other hand, the behavior of D2D communications decides whether the cached content can be shared successfully. In other words, there is an interaction between content caching and D2D communications. Moreover, content caching is usually operated in a slow timescale span (e.g, a half hour), while the communication behavior should be performed in a fast timescale span (e.g, several milliseconds), i.e., channel coherence time, owing to the variable nature of wireless channels. Therefore, it is essential to study the channel-aware content caching and sharing problem under two timescale. In this paper, we formulate the problem as a stochastic mixed- integer nonlinear programming (SMINLP), and then approximate the problem as a mixed-integer nonlinear programming. Subsequently, a heuristic algorithm is proposed to handle the approximated problem. Simulation results show that the proposed scheme is more efficient in traffic offloading than that without considering the interactions under two timescale.

User association has redrawn much attention lately, due to the introduction of content caching in small base stations (SBSs). To reduce traffic burden on backhaul links, users are associated with different SBSs when requesting different contents. However, user-perceived delay increases if the serving SBSs that have the desired contents are overloaded. Moreover, the user association problem becomes complex due to the vast number of contents. In this paper, to reduce user-perceived delay as well as backhaul loads, we propose a cluster-level user association scheme where content clustering is leveraged to simplify user association and reduce its complexity. Particularly, similar contents are clustered together according to the content preferences of users and cached contents in SBSs. Thus, the dimensionality of the user problem becomes smaller. On this basis, we propose a distributed cluster-level user association scheme, where each user selects SBSs based on their traffic loads and cached contents. Simulation results show that our scheme based on clustered contents outperforms the traditional schemes.

Mobile-Edge Computing (MEC) could relieve computing pressure of resource-constrained Smart Mobile Devices (SMDs) by offloading computation-intensive tasks to nearby/MEC server. However, how to achieve energy efficient computation offloading for SMDs under application-dependent latency constraints remains challenging in multiuser MEC systems. Specifically, the optimal system operations are not only inner- coupled for each SMD due to parallel local and cloud execution, but also inter-coupled among SMDs due to competition for limited radio resource. Additionally, the inner- and inter-coupling influence each other, which further complicates multiuser offloading strategy design. In this paper, we address such a challenge by jointly optimizing computational speed of SMDs via Dynamic Voltage Scaling (DVS) technology, subcarrier allocation, transmit power per subcarrier, data size sent per subcarrier, and offloading ratio, to minimize weighted sum of mobile energy consumption, resulting in a mixed-integer optimization problem. To tackle this NP-hard problem, we propose a fast-convergent suboptimal algorithm with the Lagrangian dual decomposition. Additionally, simulation results verify that the algorithm converges fast and significantly outperforms existing schemes in energy consumption reduction. Meanwhile, we discover that given latency mean, total mobile energy consumption remains stable or increases with the variance of latency requirements, which could direct admission control in practice.

By integrating software defined network and network function virtualization, software defined satellite networks (SDSNs) can enable flexible virtual network function (VNF) deployment to process and forward end-to-end traffic flows. Since one traffic flow has to go through all its required VNFs, the VNF deployment has a significant impact on traffic routing. In this regard, with time-varying network topology and limited network resources taken into account, we aim to match VNF deployment and routing to fulfill traffic flows' requirements in the SDSN in a cost-effective manner. Specifically, we first evolve the traditional time evolving graph as a software defined time evolving graph (SDTEG) to depict the time-varying network topology and meanwhile, provide a shared platform for elastic network resource provisioning. On this basis, we then formulate a cost minimization problem as a multi-slot integer linear programming problem to make a judicious decision on VNF deployment and routing for each traffic flow. To address this challenging problem effectively, we further propose a heuristic algorithm, referred to as time-slot decoupled algorithm (TDA). Finally, the effectiveness of the TDA as well as the superiorities from the joint optimization of VNF deployment and routing are demonstrated through simulation results.

To satisfy the growing capacity demand of explosive traffic, ultra-dense network (UDN) is proposed as a key technology, where small cells are densified to fully exploit the spatial spectrum reuse gain. The decreasing coverage area of a small cell also leads to the obviously increasing traffic asymmetry in UL and DL within the same cell and among different cells. To adapt to the dynamic and asymmetric traffic within UDN, dynamic time-division duplex (DTDD) can be seen as a promising scheme due to its capability in the flexible adjustment of ratio of UL to DL subframes. In this paper, we propose a load-aware analytical framework to accurately characterize the network performance of a DTDD UDN with the emphasis on the effect of idle mode cells. With a practical multi-slope path loss model, we first derive the void probability of a random SAP and obtain the coverage probability and area spectral efficiency (ASE). Then we evaluate how the SAP void probability, UL/DL configuration and network density affect the network performance. Our numerical results show that the idle mode cells have significant effect on the network performance, which alters the variation tendency of coverage probability and ASE with the increasing network density.

By allocating antenna time blocks to spacecrafts, data relay satellites are of vital importance for the space network to relay data within their visible intervals (i.e., time windows). Existing works concentrate only on the allocation of transmission resources (i.e., antenna time blocks) in time windows and may result in transmission conflicts hard to efficiently resolve, especially when multiple missions are activated simultaneously. To this end, we propose to further integrate computation with transmission resource allocation, to enable data compression so as to alleviate conflicts. Specifically, aiming to maximize the number of completed missions and minimize data loss, we first formulate the joint transmission and computation resource allocation problem as a mixed integer linear programming (MILP) one. Then, for the complexity reduction, we transform the MILP into an integer linear programming (ILP) one by fixing maximal data compression. Meanwhile, by constructing a conflict graph to characterize resource allocation conflicts, a time window scheduling algorithm is proposed to solve the ILP problem efficiently. Next, we further develop a data compression control algorithm to reduce data loss on the prerequisite of invariant mission number. Finally, simulation results show that the space network can benefit from the combination of transmission and computation resources in terms of both mission number and data loss.

Space information network (SIN) is a promising networking architecture to significantly broaden the observation area and realize continuous information acquisition for earth observation. Over the dynamic and complex SIN environment, it is a key issue to coordinate multi-dimensional heterogeneous network resources (e.g., observation resource and transmission resource) in the presence of multi-resource variations and severe conflicts, such that diverse earth observation service requirements can be satisfied. To this end, this paper studies the multi-resource coordinate scheduling problem in SINs. Specifically, we first characterize the relationship among multi-resource using an event-driven time-expanded graph (EDTEG). Based on the EDTEG, observation resource and transmission resource are jointly considered, and an integer linear programming optimization problem is formulated to maximize the sum priorities of the successfully scheduled tasks. An iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling sub-problems, which can be efficiently solved by extended transmission time sharing graph and directed acyclic graph methods, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm and performance impacts of different network parameters.

Mission scheduling algorithms are envisioned as critical to satisfy the increasing mission requirements in broadband data relay satellite networks, which is severely influenced by time-varying inter-satellite contacts (i.e., potential available communication links) and differentiated satellite downlink contacts. Nevertheless, the intertwined effect of such two types of contacts on mission schedules poses daunting challenges for the efficient mission scheduling design. In this paper, to achieve fair performance among user satellites, we maximize the minimum number of successfully scheduled missions over all user satellites by jointly optimizing contact plan design, power allocation (PA) in relay satellites, and mission schedules based on the time-expanded graph. The formulated problem is a mixed-integer nonlinear program optimization problem that is challenging to solve. For tractability purpose, we equivalently decompose the problem into a PA problem and an optimal PA-based mission scheduling (OPA_MS) problem, which is still a mixed-integer linear program. We further devise a new two-stage scheme to efficiently solve the OPA_MS problem. Simulation results validate the significant gains of the proposed algorithm in mission completion number and necessitate the consideration of the time-varying and differentiated inter-satellite and downlink contacts.

Supporting cooperative radio among remote radio heads (RRHs) and elastic cloud service in the baseband unit (BBU) pool, cloud radio access network (C-RAN) is perceived as a promising solution for the next mobile network. In C-RAN, cooperative radio can enhance power saving at RRHs, along with impact on computation effort and power saving in the BBU pool. In turn, power saving at RRHs, benefited from cooperative radio is restricted by the constrained computation capacity provisioned by processors in the BBU pool. Besides, limited fronthauls, which support baseband signal transfer between the BBU pool and RRHs, affect the cooperative radio design and power consumption at RRH as well. By jointly optimizing transmit beamforming among RRHs and processor sleeping in the BBU pool, we exploit such interplay between communication and computation for system power minimization in C-RAN with limited fronthaul and computation capacity. Specifically, we formulate this problem as a mixed-integer non-linear programming problem (MINLP), and then leverage the special structure of the MINLP to make a near optimal decision on the set of active processors and transmit beamforming vectors with high efficiency. Finally, extensive numerical results demonstrate that our proposed algorithms can enforce processor sleeping and reduce system power consumption significantly.

The spatial-and-temporal correlation of interference has been well-studied in Poisson networks, where the interfering base stations (BSs) are independent of each other. However, there exists spatial interdependence including attraction and repulsion among the BSs in practical wireless networks, affecting the interference distribution and hence the network performance. In view of this, by modeling the network as a Poisson clustered process, we quantify the effects of spatial interdependence among BSs on the interference correlation and analytically prove that BS clustering increases the level of interference correlation. In particular, it is shown that the level is a monotone-increasing function of the mean number of BSs in each cluster and a monotone-decreasing function of cluster radius, but is independent of the locations of the clusters. Furthermore, we study the effects of spatial interdependence among BSs on network performance with Type-I HARQ retransmission scheme via considering heterogeneous cellular networks in which small-cell BSs exhibit a clustered topology in practice. We derive the numerically integrable expressions and their bounds for the joint success probabilities, defined as the success probability in multiple successive transmissions, for macro-cell users and small-cell users. It is shown that BS clustering improves the performance of macro-cell users. Further, the level is enhanced by the repulsion between the BSs from different tiers.

In this paper, we consider an energy harvesting cognitive radio network where each secondary transmitter (ST) harvests radio frequency energy from ambient primary transmitters (PTs), and communicates with its secondary receiver (SR) which suffers co-channel interference from PTs. A positive correlation is observed between the harvested energy at the ST and the aggregate interference at the SR, which illustrates that an ST will have a higher probability to harvest enough energy if there is strong interference at the SR. To exploit the positive correlation, we propose an interference threshold-based transmission strategy for STs, so as to protect secondary transmissions as well as increase the amount of harvested energy. We model the battery level of each ST as a finite state discrete-time Markov chain and derive the expression of the secondary spatial throughput as a function of the transmission probability and coverage probability. We investigate the impact of interference threshold and density of PTs on the secondary spatial throughput, and provide guidelines in the optimal design of these two factors to maximize the spatial throughput. To further improve the spatial throughput of a secondary network, we consider the successive interference cancellation strategy at SRs, and reveal its superiority in the high density regime of PTs.

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 this paper, we develop a framework using tools from stochastic geometry and queuing theory to evaluate the flow-level performance of downlink cellular networks with spatio-temporal traffic. Under this framework, we first obtain the mean service rate and the non-empty probability of a scheduled user queue by solving a fixed-point equation, which captures the inherent correlation between the interference and the queue status. By leveraging these results, we then derive closed-form expressions for the mean packet throughput and its bounds, defined as the mean number of packets that can be delivered during a given time duration. Simulation results validate the accuracy of the presented analysis, which can provide useful insight on the design of cellular networks while incorporating the spatial and temporal fluctuations of traffic.

Caching content in devices and sharing content via device-to- device (D2D) communications can help reduce cellular data traffic. However, the content caching in devices will reshape the way the conventional D2D communications work, which has not been fully understood. Therefore, we explore the coupling relationship of content caching and sharing in this paper. Particularly, we first propose a concept of content-topology and give its corresponding graph model, which reveals the relationship among devices, content caching, and D2D links. Subsequently, a heuristic algorithm is proposed to find a content-topology, where the content caching among devices and the link activations for content sharing are well matched. Simulation results show that the scheme based on content- topology outperforms the existing algorithms in terms of the amount of offloaded traffic, the number of link activation, and link efficiency.

In Earth observation satellite networks (EOSNs), imbalance between the observation and transmission opportunities can cause poor network performance, e.g., a reduced number of successfully scheduled targets. To tackle this issue, in this paper, we investigate the multi-dimensional resource scheduling problem in EOSNs to ensure that each EOS can observe an appropriate subset of targets with matched downloading capacity to the destination. Specifically, an optimization problem is formulated and proved to be NP-hard. Then, an iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling subproblems, which are further efficiently solved by acyclic directed graph and particle optimization methods, respectively. Extensive simulations have been conducted to demonstrate the efficiency of the proposed scheduling algorithm.

Cloud radio access network (C-RAN) is perceived as an energy-efficient solution for the next mobile network. The cloud-based baseband unit (BBU) pool is capable of dynamically provisioning computational resources for mobile users to improve hardware utilization such that unused processors can be switched off for power saving in the BBU pool. Besides, cooperative radio among remote radio heads (RRHs) can optimize transmit beamforming to reduce power consumption at RRHs. Thus, to achieve more judicious system power saving, this is need to consider power consumption in the BBU pool and that at RRHs together. In this paper, we aim to minimize system power consumption by jointly exploiting transmit beamforming and processor sleeping. Specifically, we formulate a mixed integer non-linear system power minimization problem, which is hard to solve. For tractability purpose, we transform this problem to an equivalent clustering problem embedded with a series of transmit beamforming problems and processor sleeping problems. On this basis, we first focus on solving the embedded problems with the given clustering and then propose a low-complexity clustering algorithm to search out the optimal clustering with minimum system power consumption. Finally, simulation results show that our proposed algorithms can save system power significantly.

The deployment of long term evolution (LTE) in unlicensed spectrum (LTE-U) is a promising solution to overcome the spectrum shortage. However, the interaction between LTE-U and Wi-Fi in unlicensed spectrum has not been well understood. In this paper, we use stochastic geometry to develop a framework for the co-existence between LTE-U and Wi-Fi in unlicensed spectrum. To reduce the intra-and inter-RAT interference, LTE-U employs an ALOHA-like random access scheme and Wi-Fi performs carrier sensing and energy detection before transmission. We derive the retention probability and the coverage probability of Wi-Fi and LTE-U networks. Based on our analysis, we investigate the effect of network parameters on the coverage probability of these networks.

Heterogeneous small cell network is a promising technique in the next generation mobile communications. Many works have been studied in small cells, including resource allocation and interference mitigation, but most studies didn't consider the quality-of-service (QoS) and power consumption. This paper focuses on the power allocation based on non-cooperative scheme to mitigate the interference and increase the energy efficiency in small cells. The delay constraint is introduced in small cells to guarantee the QoS. We reconsider the capacity according to Shannon' capacity formula and bring in the concept of effective capacity. We take the total power consumption of the small cells into account and employ energy efficiency metric to formulate the problem of power allocation. The power allocation problem is modeled as non-cooperative supermodular game, and it is shown to converge to Nash equilibrium, and then it is transformed into a convex optimization problem, which is solved by the multi-agent Q-learning algorithm based on conjecture. The effectiveness of the proposed supermodular game based power allocation is verified by the simulations.

The following topics are dealt with: cellular radio; MIMO communication; wireless channels; optimisation; radiofrequency interference; probability; Long Term Evolution; mobile radio; telecommunication traffic; radio networks.

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.

Shortage of available licensed spectrum is a major barrier to the development of 5G networks. The deployment of long term evolution (LTE) in unlicensed spectrum (LTE-U) is a promising solution to overcome such a barrier. However, the interaction between LTE-U and Wi-Fi in unlicensed spectrum has not been well understood. In this paper, we use stochastic geometry to develop a framework for a multi-radio access technologies (multi-RAT) heterogeneous network, which consists of an LTE-U tier and a Wi-Fi tier. To reduce the intra- and inter-RAT interference, LTE-U employs an ALOHA-like random access scheme and Wi-Fi performs carrier sensing and energy detection before transmission. We derive the coverage probability and spatial throughput of Wi-Fi and LTE-U networks, and perform the asymptotic analysis when the density of Wi-Fi and LTE-U nodes approach infinity. Based on our analysis, we investigate the effect of network parameters on the coverage probability and spatial throughput of these networks. Furthermore, in order to achieve weighted max-min fairness, we optimize the retention probability of LTE-U nodes to maximize the minimum weighted spatial throughput of Wi-Fi and LTE-U networks.

In this paper, we investigate the interference alignment (IA) for a class of partially connected multi-input multi-output heterogeneous networks, which consist of L fully connected pico cells, J partially connected pico cells, and one macro cell. In particular, we investigate the feasibility of IA conditions with no channel extensions, T-independent channel extensions, and T-dependent channel extensions, respectively, for the single beam case. We present three theorems to characterize some necessary conditions, which L, J, T, and the channel diversity order L̅ must jointly satisfy when IA conditions are feasible. These necessary conditions also implicitly impose the constraints on the upper bounds of achievable degrees of freedom with different channel extensions.

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.

Topology control is an important approach to maintain the connectivity of cognitive radio networks (CRNs). Most existing works assumed that secondary users (SUs) must vacate the spectrum reclaimed by primary users (PUs), resulting the inefficient spectrum utilization. In this paper, we consider the simultaneous transmissions of SUs with PUs; meanwhile, SUs are equipped with successive interference cancellation (SIC) to mitigate the interference from PUs, thereby enabling SUs to access the spectrum more aggressively than previous works. Although SIC has been studied in the information theory and signal processing, it is not well investigated in guaranteeing the connectivity of wireless networks, especially the CRNs. On this account, we propose both centralized and distributed SIC-based topology control algorithm to alleviate the impact of the unpredictable activities of PUs and the potential interference between SUs. In particular, we integrate power control with channel assignment to construct a bi-channel-connected and conflict-free CRN with the fewest required channels. Theoretical analysis reveals that the bi-channel-connectivity and conflict-free properties can be ensured by our proposed algorithms. Then, simulation results demonstrate the effectiveness of proposed algorithms in terms of reducing the number of required channels and improving the robustness of topologies, as compared with the prevailing topology control algorithms.

Content caching at base stations (BSs) is a promising technique for future wireless networks by reducing network traffic and alleviating server bottleneck. However, in practice, the content popularity distribution may change with spatio-temporal variation but be unknown for BSs, which is an intractable obstacle for efficient caching strategy design. In this paper, considering unknown popularity distribution, we explore the content caching problem by jointly optimizing the content caching in cooperative BSs, content sharing among BSs, and cost of content retrieving. We tackle the problem from a multi-armed bandit learning perspective, where the learning of the popularity distribution is incorporated with the content caching and sharing process. Specifically, we first propose a centralized algorithm by employing a semidefinite relaxation approach, and we prove that this centralized algorithm learns efficient caching by deriving a sub-linear learning regret bound. To further reduce computational complexity, we propose a distributed algorithm based on alternating direction method of multipliers, where each BS only solves their own problems by exchanging local information with neighbor BSs. Extensive simulation results show the effectiveness of the proposed algorithms in terms of learning content popularity distributions of individual BSs, offloading traffic from the content server, and reducing cost of content retrieving.

Small satellite networks (SSNs) are playing an increasing role in nowadays earth observation due to their less development cost and energy consumption. In SSNs, it is pivotal to transmit a huge amount of data for differentiated missions to ground stations. Nevertheless, due to limited transponders and energy budget, not all contacts, i.e., potential available communication links, are feasible in data delivery. Besides, satellite downlink channel conditions are indeed time-varying due to atmospheric precipitation. Therefore, one daunting challenge is searching for feasible contacts termed as contact plan design with consideration of the differentiation for missions. In this paper, we exploit an extended time-evolving graph to characterize network resources. Based on the graph, we formulate the design of mission-aware contact plan, aiming at maximizing network profit in terms of sum weighted data volume as a mixed-integer linear programming. Due to its NP-hardness, we propose a primal decomposition method to efficiently solve the formulated problem by exploiting its special structure. To further reduce the complexity, we propose a link metric considering the issues of residual energy of satellites, time-varying satellite downlink contact capacity, and the differentiation for missions in the conflict graph. Based on the conflict graph, we devise a heuristic algorithm to design contact plan. Simulation results demonstrate the efficiency of the proposed algorithms and necessitate the consideration of the time-varying downlinks and the differentiation of missions for contact plan design.

Hybrid wireless network is composed of both ad hoc transmissions and cellular transmissions. Under the L-maximum-hop routing policy, flow is transmitted in the ad hoc mode if its source and destination are within L hops away; otherwise, it is transmitted in the cellular mode. Existing works study the hybrid wireless network capacity as a function of L so as to find the optimal L to maximize the network capacity. In this paper, we consider two more factors: traffic model and base station access mode. Different from existing works, which only consider the uniform traffic model, we consider a traffic model with social behavior. We study the impact of traffic model on the optimal routing policy. Moreover, we consider two different access modes: one-hop access (each node directly communicates with base station) and multi-hop access (node may access base station through multiple hops due to power constraint). We study the impact of access mode on the optimal routing policy. Our results show that: 1) the optimal L does not only depend on traffic pattern, but also the access mode; 2) one-hop access provides higher network capacity than multi-hop access at the cost of increasing transmitting power; and 3) under the one-hop access mode, network capacity grows linearly with the number of base stations; however, it does not hold with the multi-hop access mode, and the number of base stations has different effects on network capacity for different traffic models.

Hybrid automatic repeat request (HARQ) is widely used in heterogeneous cellular networks (HCNs) to improve communication reliability. The temporal interference correlation caused by the common set of interferers makes the performance of HARQ more complex, especially for Type-II HARQ where the unsuccessful packets are combined with the new one to decode the packet. In general, due to the complexity of network performance analysis, the existing research focused on the performance of HARQ in single-tier wireless networks without considering cell association or base station (BS) load or the case that the combined number of transmissions is no larger than 2. In view of this, we study the performance of HARQ in HCNs jointly considering the temporally correlated interference, flexible cell association, and BS load. To this end, we adopt the popular HCN model, where different types of BSs in HCNs are modeled as K independent Poisson point processes with different densities and transmission powers. Leveraging the tool of stochastic geometry, we derive the success probability and delay-limited throughput for HCNs with Type-I HARQ and Type-II HARQ, respectively, for any number of transmissions. We show that the network performance in multiple time slots is decided by the performance in a single time slot and the temporal interference correlation. Finally, we conduct simulations to validate our analysis and show that the analysis without considering temporal interference correlation overestimates the performance of HCNs with HARQ.

In this paper, we investigate the energy-saving resource management problem for a new device-to-device (D2D) and cellular coexisting network, where D2D users employ orthogonal frequency division multiple access (OFDMA) and cellular users employ sparse code multiple access (SCMA). This hybrid network can support massive connectivity by exploiting the degrees of freedom in code and space domains, however, the complicated spectrum sharing pattern also leads to serious interference, which further boosts the power consumption of mobile devices (MDs). To tackle this problem, we propose a unified resource management scheme to minimize the total transmit power of all MDs by jointly optimizing mode selection, resource allocation, and power control. First, we analytically get the optimal resource-sharing mode (dedicated mode or reuse mode) for cellular users and D2D users based on the mapping rule between SCMA codebooks and OFDMA resource blocks. For each resource-sharing mode, we reformulate the resource management problems as classical problems in graph theory, and then devise efficient algorithms leveraging the special structure of the constructed graphs. Finally, simulation studies indicate that the network capacity is upgraded with the hybrid multiple access technologies, and the energy efficiency performance is also enhanced through the unified resource management.

In this paper, we jointly consider mode selection, admission control, partner assignment, and power allocation to investigate the sum rate maximization problem in underlay SCMA device-to-device (D2D) networks. Due to its mixed combinatory, we first decouple the problem to devise efficient algorithms. In particular, we propose a channel gain based mode selection criterion and a greedy-style partner assignment scheme, and obtain closed-form solutions for both admission control and power allocation. To further reduce the computational cost, we also exploit the structure of the formulation to devise a much faster heuristic algorithm. Simulation results exhibit the superiority of the proposed algorithms against other schemes.

The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered as a promising scheme to flexibly adjust resource allocation based on UL and DL traffic demands, known as traffic adaptation. In this work, we study how traffic adaptation decreases cell service time and improves energy efficiency (EE) in small cell networks operating dynamic TDD. According to different UL and DL traffic parameters, we classify small cells into K ≥ 1 types, and accommodate the UL/DL configuration for each type of small cells with the objective to minimize the cell service time and maximize the network EE. In comparison with semi-static TDD scheme, dynamic TDD is shown to achieve larger service time gain as the traffic asymmetry between small cells increases. In summary, the proposed analytical framework allows us to elucidate the benefit of traffic adaptation to service time and EE in future dense networks with dynamic TDD.

The correlation of interference has been well quantified in Poisson networks where the interferers are independent of each other. However, there exists dependence among the base stations (BSs) in wireless networks. In view of this, we study the interference correlation in non-Poisson networks where the interferers are distributed as a Matern cluster process (MCP) and a second-order cluster process (SOCP). We obtain the explicit expressions for the interference correlation coefficients under these two cases and find that they are the same if these two cluster processes have the identical cluster radius and average number of each cluster. We also prove that they are greater than their counterpart for the Poisson networks, which indicates the clustering in interferers increases the interference correlation. It is also shown that the value of the correlation coefficient goes up as the the attraction between the interferers increases. Finally, the numerical results show the relation between the correlation coefficients and system parameters.

Cloud radio access network (C-RAN), regarded as a promising green network architecture, facilitates cooperative transmission among remote radio heads (RRHs) while enabling flexible computation provisioning in the virtualized baseband unit pool. By jointly optimizing cooperative transmission, i.e., transmit power allocation with zero-forcing precoding adopted, and computation provisioning, i.e., virtual machine assignment, this paper minimizes the system power consumption comprised of transmit power and processing power in downlink C-RAN. Specifically, subject to per-RRH power constraint (PRPC) and per-MU quality of service constraint, the system power consumption minimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem. To solve the challenging MINLP, we reformulate the MINLP as a minimum weight perfect matching problem to get the initial solution without considering the PRPC. On this basis, a power-aware greedy algorithm is further devised to modify the solution such that the PRPC is satisfied. Finally, extensive simulations show the superiority of the proposed scheme on system power saving and the tradeoff between transmit power and processing power.

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.

In this paper, we study the uplink spectrum sharing in a device-to-device (D2D) underlaid LTE-Advanced network. The fractional power control (FPC) policy is proposed for both cellular user equipments (CUEs) and D2D users to reduce the cross-tier interference. We present an analytical framework to evaluate the effect of FPC on the network performance in terms of coverage probability and D2D user transmission capacity. By exploiting the CUE's location information, we further propose a location-aware spectrum sharing (LoSS) policy which can increase the D2D user's transmission opportunities while satisfying the CUE's QoS constraint. The proposed framework allows to determine the optimal FPC parameter for a CUE at any given location in the macrocell, and provides a more accurate analysis on the spectrum sharing between CUEs and D2D users. From the perspective of D2D transmission capacity, we provide guidelines for the optimal design of FPC in the D2D underlaid LTE-Advanced network.

In the research of physical layer security, cooperative jamming has recently drawn considerable attention. Jammer plays a friendly role to transmit jamming signal to create interference at the eavesdroppers. However, few literatures consider the scenario in which jammer plays a hostile role to interfere legitimate users. In this paper, we study an ad hoc network where legitimate users transmit with the ALOHA protocol in the presence of hostile users. Each hostile user acts as a jammer or eavesdropper with probability q or 1 - q, respectively. We assess the network performance from both sides of the legitimate user and the hostile user. Particularly, from the perspective of the legitimate user, we evaluate how the connection outage and secrecy outage affect the secrecy transmission capacity, and then derive the optimal ALOHA transmission probability that maximizes the capacity. In view of the hostile user, we obtain the optimal jamming probability that can lead to the largest connection outage probability of legitimate users subject to a given successful eavesdropping probability constraint.

Radio Frequency (RF) energy harvesting has been shown to be a promising way to power wireless devices. In this paper, we consider an energy harvesting cognitive radio network model where each secondary transmitter (ST) harvests RF energy from ambient primary transmitters (PTs). To protect secondary transmissions and improve energy efficiency, we propose an interference threshold-based transmission strategy for STs. We model the battery level of each ST as a finite state discrete-time Markov chain (DTMC) and observe a correlation between the harvested energy at the secondary transmitter and the aggregate interference at the secondary receiver (SR). Based on copula theory, we derive the joint distribution of the harvested energy and the aggregate interference, and then derive the energy outage probability and transmission probability of each ST by combining with the Markov chain model. With the tools from stochastic geometry, we analyze the ST's coverage probability. By analyzing the effect of interference threshold on energy outage probability, transmission probability and coverage probability, we provide guidelines for the optimal design of interference threshold to maximize the spatial throughput of secondary network.

Small satellite networks, with the advantage of remarkably less development cost and energy consumption with respect to geostationary relay platforms, are playing an increased role in nowadays earth observation. However, small satellites have limited transponders and energy budget, which makes it necessary to design efficient contact plans to improve the network throughput. This paper addresses such an issue of joint management of the energy and transponder resource to well match the mission demand and network resources. We adopt an extended time-evolving graph to characterize network resources and then, formulate the contact plan design problem with the goal of maximizing the throughput as a mixed-integer linear programming. Since the computational complexity of this problem coupling multiple time slots is prohibitive, we further propose two heuristic algorithms which operate on a slot-by-slot basis to achieve high throughput. Simulation results present the impact of different factors on the network performance and moreover, demonstrate that both our contact plan approaches can achieve high throughput with low complexity.

In this paper, we investigate the energy-efficient transmission problem by resource allocation in SCMA networks. We formulate it as an optimization problem to maximize the network energy efficiency (EE) subject to quality-of-service (QoS) requirements, codebook assignment, power allocation, and subcarrier reuse constraints. Due to its mixed combinatory, we separate codebook assignment and power allocation to devise suboptimal but cost- efficient algorithms. With power equally distributed, we first propose a novel scheme to assign codebooks. We then develop a derivative- bisection based algorithm to optimally solve the resultant power allocation problem by exploiting its quasiconcave structure. Simulation results exhibit the superiority of the proposed algorithms against the existing classical schemes and of SCMA over OFDMA in terms of the network EE.

To improve the spectrum efficiency (SE), sparse code multiple access (SCMA) has been proposed as an candidate for 5G wireless networks. Although SCMA has good SE performance, it suffers from high detection complexity, which may degrade its energy efficiency (EE) performance. To make up for this deficiency, we in this paper jointly consider codebook design (i.e., mapping matrix and constellation graph design) and codebook assignment to investigate the detection complexity minimization problem for uplink SCMA networks. To tackle this hard problem effectively, we first borrow the idea of dual coordinate search to devise a cost-efficient algorithm to determine the mapping matrix and codebook assignment. Based on the mapping matrix, we exploit the multi-dimensional modulation characteristic of SCMA to carefully design the constellations for each codebook to further reduce the detection complexity. Finally, we present some simulations to illustrate the performance gain of our proposed algorithm as compared with other schemes.

In energy-constrained Low Earth Orbit (LEO) satellite constellations, in order to prolong the network lifetime, more traffic should be carried by the satellites with high battery level, which, in turn, may result in congestion in such satellites. To strike a balance, we study the multi-path routing problem which aims at Maximizing network Lifetime while maintaining a Guaranteed network Congestion level (MLGC). Particularly, we formulate such a problem as a linear programming. However, it is time-consuming that solving the proposed MLGC needs to joint multiple time intervals. Therefore, we further design an Energy Aware Multi-path Routing (EAMR) strategy without solving the optimization problem. Simulation results show that the performance of EAMR is comparable with MLGC and moreover, compared with available routing strategies, the network lifetime can be effectively improved while the required congestion level being guaranteed by implementing our proposed schemes.

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.

By migrating baseband processing functionalities into a centralized cloud-based baseband unit (BBU) pool, cloud radio access network (C-RAN) facilitates cooperative transmission among remote radio heads (RRHs) and enables flexible computation provisioning in the BBU pool. In C-RAN, due to the high amount of data transfer from the BBU pool to RRHs through fronthauls, limited fronthaul capacity becomes a key factor when designing cooperative transmission schemes among RRHs. Meanwhile, as computational resources are provisioned to mobile users (MUs) for baseband processing in the form of virtual machines (VMs) in the BBU pool, an effective VM assignment strategy is also with great significance. In this paper, we propose a holistic framework for green C-RAN under the constraint of limited fronthaul capacity, where we jointly optimize hybrid clustering and computation provisioning to appropriately provide a cluster of RRHs and a VM to each MU for cooperative transmission and baseband processing, aiming at minimizing the system power consumption. The system power minimization problem is formulated as an integer non-linear programming problem, which is hard to tackle. For tractability purpose, we transform this problem to an equivalent hybrid clustering problem embedded with a series of VM assignment problems. On this basis, we first achieve the optimal solution for system power minimization with high computational complexity, and then, a greedy algorithm is proposed to solve the hybrid clustering problem for practical implementation. Finally, the simulation results demonstrate that the proposed joint optimization of hybrid clustering and computation provisioning can significantly reduce the system power consumption.

In cellular networks, since Media Access Control (MAC) layer plays a key role in every access equipment, it fascinates that little progress on multiple access protocol could save considerable energy. Accordingly, this paper studies a novel MAC protocol, i.e., the power division multiple access (PDMA) protocol, with the purpose of green communication. As a fundamental study of PDMA, we first propose a power division multiplexing (PDM) scheme, analogous to the time division multiplexing and frequency division multiplexing. It is proved that the transmit power could be divided into multiple regular power segments (PSs) to simultaneously transmit multiple independent information/data streams in peer to peer communications. Based on our fundamental studies of PDM, an orthogonal PDMA (OPDMA) protocol is proposed to utilize multiplexing and degraded channel gains for energy saving. By adopting the orthogonal PSs proposed in OPDMA, multiple information streams in different channels could be transmitted efficiently and concurrently with quality of service guarantee. This paper shows that the proposed OPDMA not only has low computational complexity as the conventional Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) protocols but also gains better energy efficiency, which consists with the energy saving requirement in green communications.

Device-to-device (D2D) communications underlaying a cellular infrastructure have been recognized as an important network-organization architecture in 5G networks. In these scenarios, existing works have explored the impacts of one or several factors among mode selection, admission control, partner assignment, and power allocation on the network performance. In this paper, we put forward an optimization framework that considers all of these coupled factors to investigate the spectrum sharing problem in D2D networks. In particular, we introduce an objective that combines the access rate and the network sum rate and then maximize it subject to users' quality-of-service requirements and resource allocation constraints. Due to its mixed combination, we focus on designing cost-efficient and easy-to-implement algorithms instead of finding globally optimal but exponentially complex solutions. By decomposition, we first devise two novel mode selection criteria and an admission-prioritized partner assignment scheme and obtain closed-form solutions for both admission control and power allocation. Moreover, we present a simple but interesting geometric interpretation on the physical implication of admission conditions. To further reduce the computational cost, we also exploit the structure of the formulation to devise a much faster heuristic algorithm, which usually runs at an order of millisecond. Simulation results show the low computational complexity of the proposed algorithms and exhibit their superiority against other schemes in terms of the access rate and the sum rate.

Network control strategies for energy-efficient operation of HetNets need to match the dynamics of spatial and temporal traffic loads and to stabilize the network. In this paper, we develop a stochastic optimization framework, which formulates spatially inhomogeneous traffic distributions and time-varyingly random traffic arrivals and guarantees network stability, to investigate the energy conservation problem in HetNets. In particular, we jointly optimize base station (BS) operation, user association, subcarrier assignment, and power allocation to minimize the average energy consumption. We devise an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the Steerable Energy ExpenDiture algorithm (SEED), to solve the problem. To deal with a highly coupled and mixed combinational subproblem in the SEED, we separate optimization variables for suboptimal but cost-efficient and easy-to-implement algorithm design. By this, we develop closed-form solutions for both user association and subcarrier assignment, a fast and tuning-free algorithm that provably achieves at least local optimality for power allocation, and a greedy-style heuristic algorithm for BS operation with polynomial complexity. Simulation results exhibit that the SEED usually converges fast, can flexibly tune the power-delay tradeoff, and can significantly reduce energy consumption against other existing schemes.

This paper investigates the problem of revealing the tradeoff between energy efficiency (EE) and delay in device-to-device (D2D) communications underlaying cellular networks. Considering both stochastic traffic arrivals and time-varying channel conditions, we formulate it as a stochastic optimization problem, which optimizes EE subject to the average power, interference-control, and network stability constraints. With the help of fractional programming and the Lyapunov optimization technique, we develop an algorithm, referred to as the TRADEOFF, to solve the problem. To deal with the nonconvex and NP-hard power allocation subproblem in the TRADEOFF, we adopt the prismatic branch and bound algorithm to find its globally optimal solution, where only a linear programming needs to be solved in each iteration. Thus, the TRADEOFF serves as an important benchmark to evaluate performance of other heuristic algorithms and is usually cost-efficient. The theoretical analysis and simulation results show that the TRADEOFF achieves an EE-delay tradeoff of [O(1/V),O(V)] with V being a control parameter and can strike a flexible balance between them by simply tuning V.

The advent of simultaneous wireless information and power transfer (SWIPT) offers a promising approach to providing cost-effective and perpetual power supplies for energy-constrained mobile devices in heterogeneous cellular networks (HCNs). As energy efficiency (EE) has been envisioned as a key performance metric in 5G wireless networks, we consider a multiple-input single-output (MISO) femtocell cochannel overlaid with a Macrocell to exploit the advantages of SWIPT while promoting the EE. The femto base station sends information to information decoding (ID) femto users (FUs) and transfers energy to energy harvesting (EH) FUs simultaneously, and also suppresses its interference to Macro users. We maximize the information transmission efficiency (ITE) of ID FUs and energy harvesting efficiency (EHE) of EH FUs, respectively, with the QoS of all users, and investigate their relationship. We formulate these problems as fractional programming, which are nontrivial to solve due to the nonconvexity of ITE and EHE. To tackle these problems, we devise two beamformers namely zero-forcing (ZF) and mixed beamforming (MBF), and then propose an efficient algorithm to obtain the optimal power under both beamformers. Simulation results demonstrate that MBF provides better ITE and EHE than ZF, and there exists a tradeoff between ITE and EHE in general.

The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered a promising scheme that is able to adjust the resource allocation to the instantaneous UL and DL traffic conditions, also known as traffic adaptation. In this paper, we study how traffic adaptation and energy harvesting can improve the energy efficiency (EE) in multi-antenna small cell networks operating dynamic TDD. Given the queue length distribution of small cell access points (SAPs) and mobile users (MUs), we derive the optimal UL/DL configuration to minimize the service time of a typical small cell, and show that the UL/DL configuration that minimizes the service time also results in an optimal network EE, but does not necessarily achieve the optimal EE for SAP or MU individually. To further enhance the network EE, we provide SAPs with energy harvesting capabilities, and model the status of harvested energy at each SAP using a Markov chain. We derive the availability of the rechargeable battery under several battery utilization strategies, and observe that energy harvesting can significantly improve the network EE in the low traffic load regime. In summary, the proposed analytical framework allows us to elucidate the relationship between traffic adaptation and network EE in future dense networks with dynamic TDD. With this work, we quantify the potential benefits of traffic adaptation and energy harvesting in terms of service time and EE.

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.

In this paper, we study the inter-linked problems of wireless service provider (WSP) selection of users and bandwidth allocation of WSPs in multi-tier heterogeneous cellular networks employing the approach combining stochastic geometry and game theory. In particular, the expected average user achievable rate is calculated by modeling the distributions of users and base stations (BSs) as independent homogeneous Poisson point processes. Moreover, a hierarchical game framework is presented to model the complicated interactions among users and WSPs. Wherein, the evolutionary game, non-cooperative game, and multi-leader multi-follower Stackelberg game models are, respectively, adopted to formulate the competition among users, competition among WSPs, and cyclic dependence between users and WSPs. According to backward induction, the formulated Stackelberg game would be solved after the formulated evolutionary game and non-cooperative game are sequentially studied. For the evolutionary game, both the closed-form expression and the asymptotically stability of its evolutionary equilibrium (EE) were analyzed. Then, conditioned on the obtained EE, the existence of Nash equilibrium (NE) for the non-cooperative bandwidth allocation game is established; furthermore, a sufficient condition for the uniqueness of the NE is derived. Finally, extensive simulation results verify both the validity of our analysis and the effectiveness of the proposed scheme.

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.

This paper focuses on a class of important two-hop relay mobile ad hoc networks (MANETs) with limited-buffer constraint and any mobility model that leads to the uniform distribution of the locations of nodes in steady state, and develops a general theoretical framework for the end-to-end (E2E) delay modeling there. We first combine the theories of fixed-point (FP), quasi-birth-and-death process, and embedded Markov chain to model the limiting distribution of the occupancy states of a relay buffer, and then apply the absorbing Markov chain theory to characterize the packet delivery process, such that a complete theoretical framework is developed for the E2E delay analysis. With the help of this framework, we derive a general and exact expression for the E2E delay based on the modeling of both packet queuing delay and delivery delay. To demonstrate the application of our framework, case studies are further provided under two network scenarios with different MAC protocols to show how the E2E delay can be analytically determined for a given network scenario. Finally, we present extensive simulation and numerical results to illustrate the efficiency of our delay analysis as well as the impacts of network parameters on delay performance.

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.

In this work, the inter-linked problems of bandwidth allocation for wireless service providers (WSPs) and WSP selection for users in heterogeneous cellular networks (HCNs) are addressed by employing a multi-hierarchical game framework. Wherein, while the interaction between users are modelled using evolutionary game theory, the interactions between competing WSP's are modelled as a non-cooperative spectrum bandwidth allocation game (N-BAG). Moreover, the interaction between the WSPs and users is modelled as a multi- leader multi-follower Stackelberg game. After that, for the formulated evolutionary game, the existence and uniqueness of the evolutionary equilibrium (EE) was investigated. Conditioned on the obtained EE, the existence of a Nash equilibrium (NE) for the proposed N-BAG has been further proven and an offline algorithm to achieve the equilibrium state was proposed. Finally, simulation results verify the validity of our analysis and demonstrate that a unique NE would be achieved by the HCNs adopting the developed algorithm.

In heterogeneous cellular networks (HCNs), the interference received at a user is correlated over time slots since it comes from the same set of randomly located base stations (BSs). This results in the correlations of link successes, thus affecting network performance. Under the assumptions of a K-tier Poisson network, strongest long-term averaged biased-received- power based BS association, and independent Rayleigh fading, we first quantify the correlation coefficients of interference. We observe that the interference correlation is independent of the number of tiers, BS density, signal-to-interference-ratio (SIR) threshold, and transmit power. Then, we study the correlations of link successes in terms of the joint success probability over multiple time slots.We show that analysis without considering the temporal interference correlation underestimates the joint success probability. Moreover, we explore the effects of BS density, transmit power and user association bias on the joint success probability. In particular, BS density and transmit power affect the joint success probability of the overall network by influencing the association probability of each tier. We also reveal that the unbiased cell association outperforms the biased cell association in terms of the joint success probability. Finally, we conduct simulations to validate our analysis.

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users. A large number of applications demand for primary user behavior correlation in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship of primary users by introducing a Bayesian network (BN) based framework. How to learn such a BN structure is a long standing issue, not fully understood even in the statistical learning community. To solve such an issue in CR, this paper proposes a BN structure learning scheme which incurs significantly lower computational complexity compared with previous ones. Thus, with this scheme, cognitive users could efficiently understand the statistical pattern of primary networks.

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.

Hybrid wireless networks are networks that are composed of both ad hoc transmissions and cellular transmissions. Many existing works have analyzed the capacity of hybrid wireless networks. By assuming the uniform traffic model that a source node would select a random node as the destination, the network capacity is a function of number of nodes and number of base stations. Nevertheless, the real network traffic pattern is related to the social behaviors of users. In this work, we study the capacity of hybrid wireless networks with the social traffic model under the L-maximum-hop routing policy. If two nodes are within L hops away, packets will be transmitted in the ad hoc mode; otherwise, packets are transmitted through the base stations. To our best knowledge, we are the first to study this problem and develop the capacity as a function of number of nodes, number of stations, traffic model parameters, and L.

An important consideration for LEO satellite networks is choosing suitable routes to prolong the network lifetime while stringently guarantee the throughput requirement. However, both the highly dynamic network topology and intrinsically time-varying renewable energy availability pose great constraints and challenges in designing such routing schemes. To solve the problem, we resort to Capacity Region Evolving Graph (CREG) and formulate the throughput constrained maximum lifetime routing problem. Unfortunately, solving the problem without exploiting its special structure is indeed time-consuming, since multiple time intervals must be jointly handled. Two efficient routing algorithms, namely, Maximum Lifetime Routing (MLR) and Shortest Path-based Progressive Routing (SPPR), are thus proposed to reduce the execution time of solving the routing problem. Specifically, MLR decomposes the problem into multiple independent subproblems without trading its optimality, while SPPR exploits the deterministic mobility of satellite networks without solving the optimization problem. Simulation results verify that prolonged network lifetime and balanced traffic distribution can be obtained for both the routing algorithms.

In this paper, we investigate the capacity of two- layered satellite networks. Particularly, we propose a unified mathematical framework to formulate the relationship between network capacity and architectural parameters. Then we study the capacity of three typical scenarios. The analytical solutions show that the capacity of individual layer increases linearly with the link bandwidth of that layer. It also increases when there are more orbits and more satellites in each orbit. Moreover, when each LEO satellite can only connect to the nearest MEO satellite, the network capacity is approximately equal to the total capacity of the two layers, and is independent with the architectural parameters such as altitude of both layers and elevation angle of LEO satellites. When each LEO satellite is allowed to connect to all the MEO satellite in its coverage, the network capacity can be further increased. As the coverage size is impacted by the architectural parameters, the network capacity in this case is non-decreasing with the altitude of the MEO layer, and is non-increasing with the altitude of the LEO layer and the elevation angle of the LEO satellites.

A Multilayered Satellite Network (MLSN) tends to be a promising architecture in facilitating global ubiquitous broadband communication. However, unbalanced traffic distribution among its satellite layers should frequently occur, where the lower layers could get relatively congested while the upper layers remain underutilized. This unfair distribution of network traffic can lead to large end-to-end delay and severe throughput degradation. To cope with the above issue, we propose a Tailored Load-Aware Routing (TLAR) strategy to optimally distribute traffic load among the multiple satellite layers, so that the overall traffic congestion in the MLSN is minimized. In TLAR, an optimal portion of network load, which is decided based upon the newly arrived traffic estimation and theoretical analysis of traffic congestion rate in each layer, is detoured through the upper layer. The performance of the proposed routing method has been validated through extensive simulations, which demonstrate that TLAR can significantly alleviate traffic congestion, achieve low end-to-end delay and sustain improved throughput.

The quality of service (QoS) performance analysis is of great guiding significance for a QoS guarantee of secondary users (SUs). However, the QoS performance of SUs has not been well studied, especially how it is impacted by the traffic property of primary users (PUs). In this study, the authors propose a method to analyse the queue performance of the SU when the active and inactive durations of PUs follow general distributions. To characterise the non‐memoryless property of the channel when the distributions of PUs active/inactive durations are general distributions, they propose a two‐dimensional Markov chain to model the states of the channel. By using this Markov chain, they derive the effective capacity (EC) function of the cognitive radio network. On the basis of the EC function and the effective bandwidth function, the queue performance of the SU, that is, the stationary tail distribution of the queue length, is estimated. The author's result can not only be used to calculate other QoS metrics, such as the buffer overflow probability and throughput, but also provide some guidelines for QoS guarantee of the SU. Finally, they verify their work by comparing the analytical results with simulation results.

In next-generation wireless networks, energy efficiency optimization needs to take individual link fairness into account. In this paper, we investigate a max-min energy efficiency-optimal problem (MEP) to ensure fairness among links in terms of energy efficiency in OFDMA systems. In particular, we maximize the energy efficiency of the worst-case link subject to the rate requirements, transmit power, and subcarrier assignment constraints. Due to the nonsmooth and mixed combinatorial features of the formulation, we focus on low-complexity suboptimal algorithms design. Using a generalized fractional programming theory and the Lagrangian dual decomposition, we first propose an iterative algorithm to solve the problem. We then devise algorithms to separate the subcarrier assignment and power allocation to further reduce the computational cost. Our simulation results verify the convergence performance and the fairness achieved among links, and particularly reveal a new tradeoff between the network energy efficiency and fairness by comparing the MEP with the existing algorithms.

In this paper, we propose interference alignment (IA) schemes for downlink multiple-input-multiple-output heterogeneous networks (HetNets) with partial connectivity, which is induced by the path loss and the low transmission power of small cells. Specifically, we consider two partially connected scenarios of HetNets. In the first scenario, we focus on the partial connectivity among small cells, whereas in the second scenario, we further consider the partial connectivity between the macrocell and small cells. For the first scenario, we first propose a two-stage IA scheme by exploiting the heterogeneity and partial connectivity of HetNets. Then, the influence of the number of served macro users on system degrees of freedom (DoFs) is investigated. In particular, we derive the condition under which serving one macro user achieves more DoFs than serving multiple macro users and design an algorithm to find the optimal number of served macro users to maximize the system DoFs. Afterward, we study the second scenario and extend the two-stage IA to this scenario. The simulation results show that the proposed IA schemes can significantly improve the system sum rate. Moreover, by considering the partial connectivity between the macro cell and small cells, the system performance can be further improved.

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.

Energy efficiency (EE) is very crucial for future wireless communication systems, especially for cognitive radio networks (CRNs). The EE performance relies on channel state information (CSI) of channels. Besides, the interference from secondary users (SUs) to primary users (PUs) also closely depends on CSI in underlay CRNs. However, available works on EE usually assume that CSI is perfect, which is often inaccurate in practical systems. Thus, in this paper we investigate the robust EE maximization problem in underlay CRNs with multiple SUs and PUs. Assuming CSI error to be bounded, we consider that all channels lie in some bounded uncertainty regions. From the perspective of worst-case optimization, we formulate it as the max-min problem with infinite constraint, which is nontrivial even without this constraint. This is because that the outer-maximization problem is non-convex and the inner-minimization problem is a concave minimization problem known as NP-hard in general. We propose a scheme to handle this problem via the fractional programming and global optimization techniques. Particularly, we efficiently solve this problem in two special cases. Simulation results validate that our proposed scheme can improve the worst-case EE of SUs distinctly and strictly guarantee the quality-of-service (QoS) of PUs under all parameters' uncertainties.

Energy harvesting is a promising technique to reduce conventional grid energy consumption, which caters for 5G visions on the green evolution of current cellular networks. To fully exploit the harvested energy, an inefficient factor caused by the battery leakage must be taken into account to tackle the energy dissipation problem, which triggers a new dimensional optimization related to the transmission time. However, most approaches are studied for perfect battery models and neglect the optimization for the transmission time. In this paper, we formulate the battery leakage process into our model to explore the grid energy conservation problem by jointly considering admission control, power allocation, subcarrier assignment, and transmission time determination in cellular networks powered by grid and renewable energy. To tackle this problem, we exploit the Lyapunov optimization technique to develop an online algorithm, referred to as leakage-aware dynamic resource allocation policy (LADRA). Specifically, the LADRA only needs to track the current system states (e.g., channel and energy conditions) but without requiring their prior-knowledge. Furthermore, we prove that the minimum grid energy consumption value can be achieved by our proposed algorithm asymptotically. Simulation results verify the correctness of the theoretical analysis, as well as exhibit the performance improvement against other algorithms in terms of grid energy consumption and queue backlog.

Existing works have addressed the tradeoffs between any two of the three performance metrics: throughput, energy efficiency (EE), and delay. In this paper, we unveil the intertwined relations among these three metrics under a unifying framework and particularly investigate the problem of EE-guaranteed throughput-delay tradeoff in interference-free wireless networks. We first propose two admission control schemes, referred to as the first-out and first-in schemes. We then formulate it as two stochastic optimization problems, aiming at throughput maximization (in the first-out scheme) or dropping rate minimization (in the first-in scheme) subject to requirement of EE (RoE), stability, admission control, and transmit power. To solve the problems, the EE-Guaranteed algorithm for throUghput-delAy tRaDeoff (eGuard), respectively called eGuard-I and eGuard-II in the first-out and first-in schemes, is devised. Moreover, with guaranteed RoE, we theoretically show that the eGuard (I and II) can not only push the throughput arbitrarily close to the optimal with tradeoffs in delay but also quantitatively control the throughput-delay performance on demand. Simulation results consolidate the theoretical analysis and particularly show the pros and cons of the two schemes.

Over the last decade, the growing amount of uplink (UL) and downlink (DL) mobile data traffic has been characterized by substantial asymmetry and time variations. Dynamic time-division duplex (TDD) has the capability to accommodate to the traffic asymmetry by adapting the UL/DL configuration to the current traffic demands. In this work, we study a two-tier heterogeneous cellular network (HCN) where the macro tier and small cell tier operate according to a dynamic TDD scheme on orthogonal frequency bands. To offload the network infrastructure, mobile users in proximity can engage in device-to-device (D2D) communications, whose activity is determined by a carrier sensing multiple access (CSMA) scheme to protect the ongoing infrastructure-based and D2D transmissions. We present an analytical framework for evaluating the network performance in terms of load-aware coverage probability and network throughput. The proposed framework allows quantification of the effect on the coverage probability of the most important TDD system parameters, such as the UL/DL configuration, the base station density, and the bias factor. In addition, we evaluate how the bandwidth partition and the D2D network access scheme affect the total network throughput. Through the study of the tradeoff between coverage probability and D2D user activity, we provide guidelines for the optimal design of D2D network access.

Green communications are becoming an inevitable trend for future wireless network design, meanwhile, as a promising technique, distributed antenna systems (DAS) cater for this evolution. In this paper, we focus on the problem of devising globally optimal antenna selection and power allocation algorithm in downlink DAS to achieve energy efficiency (EE) maximization. We formulate it as a mixed-integer nonlinear programming (MINLP), which maximizes EE subject to rate requirements, transmit power, and antenna selection constraints. By equivalent transformation, an iterative antenna selection and power allocation algorithm is proposed based on nonlinear fractional programming theory, and branch and bound methods. Our algorithm ensures global optimality and thus, it provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. Simulation results show that the computation complexity can be dramatically reduced comparing with exhaustive search, as well as demonstrate that a significant gain can be obtained in terms of EE against the schemes without antenna selection.

Over the last decade, mobile data traffic has risen dramatically and the amount of UL and DL transmissions has been characterized by substantial asymmetry and time variations. Dynamic time-division duplex (TDD) has the capability to accommodate to the traffic asymmetry by adapting the UL/DL configuration to the current traffic demands. In this work, we study a two-tier heterogeneous network (HetNet) where the macro tier and small cell tier both operate dynamic TDD and use orthogonal frequency bands. We propose a policy that leads to different associations in UL and DL, and derive the load-aware coverage probability. We evaluate how the association policy affects the system performance and derive the UL/DL configuration, base station density, and bias factor that maximize the per tier or network-wide coverage probability.

We investigate the local connectivity of cognitive radio ad hoc networks (CRAHNs), i.e., node degree and probability of node isolation. The local connectivity of CRAHNs depends on not only its own network parameters but also the primary networks. To analyze the local connectivity, we use stochastic geometry and probability theory to derive the distribution of node degree, probability of available spectrum and probability of node isolation of the Secondary Users (SUs). The relation between the local connectivity of CRAHNs and the parameters of both primary and secondary networks is given. Theoretical analysis and simulation results indicate that the average node degree of SUs scales linearly for increases in the density of SUs with the slope determined by the density of Primary Users (PUs). It also indicates that the SUs' node isolation probability is largely determined by the density of PUs.

In this paper, we investigate the interference mitigation in femtocell networks, where femtocell access points (FAPs) are allowed to cooperate as different cooperative groups to allocate resources. We model the femtocell cooperation characteristics as a coalition formation game in partition form with non-transferable utility. Furthermore, a distributed coalition formation algorithm is proposed to enable each FAP to decide to depart from or join in a coalition independently, moreover, we devise a low complex iterative algorithm to optimize the allocation of each coalition's multi-dimensional resources for maximizing its FAPs' payoffs. By applying our proposed coalition formation scheme, a Nash stable FAP partition is formed and FAPs in each coalition can effectively exploit the cooperative gain to mitigate the interference and maximize the sum rate. Numerical results are provided to corroborate our proposed studies.

In this paper, by jointly considering antenna selection and power allocation, we address the energy efficiency (EE) maximization problem with delay performance taken into account in downlink distributed antenna systems (DAS). To characterise system EE, we first define a revenue-cost (RC) function as the weighted difference between sum transmit rate and total energy consumption. We then formulate the problem as a stochastic optimization model, which maximizes the long-term average RC value subject to network stability (used to depict delay performance) and average power constraints. An Energy-Efficient Antenna selection and Power allocation Algorithm (EE-APA) is proposed based on Lyapunov optimization technique. The EE-APA adapts to time-varying channel conditions and stochastic traffic arrivals without requiring any corresponding prior-knowledge. Moreover, the theoretical analysis shows that the EE-APA can not only push the EE arbitrarily close to the optimal at the cost of delay performance, but also quantitatively control the EE-delay performance. Numerical results validate the adaptiveness of the EE-APA and the correctness of the theoretical analysis.

In this paper, we investigate the individual channel estimation for three-node one-way relay network (OWRN), where both source and destination are equipped with multiple antennas. Without resorting to the composite channel estimation, as did in the traditional work, we directly estimate the individual channels from an iterative linear minimum mean-square-error (LMMSE) estimator. The closed-form least square (LS) channel estimator is also derived through matrix unitary diagonalization to provide a good initialization for the iterative LMMSE estimator. To make the work more complete, we present two performance lower bounds: Bayesian Cramér lower bound (BCRB) and linear estimation lower bound (LELB), for the proposed algorithm. Numerical results are provided to corroborate our proposed studies.

In this paper, by jointly considering subchannels, power, and Modulation and Coding Scheme (MCS) allocation, we address the sum-rate maximization problem in OFDMA downlink systems. We formulate the problem as an integer linear programming (ILP), which maximizes the system sum-rate subject to the minimum rate requirements of users and total transmit power constraint of base station. To solve the formulation with low complexity, we propose a two-level iterative Subchannels, Power, and MCS allocation Algorithm (SPMA) by exploiting Tabu Search (TS). At each iteration, the SPMA firstly assigns MCS to users and then allocates subchannels and power based on a SubChannels and Power allocation Algorithm (SCPA). Particularly, the SCPA maximizes the system sum-rate by first satisfying the minimum rate requirements with the least transmit power. Simulation results show that the SPMA outperforms the existing algorithms in terms of sum-rate and average rate per user, as well as demonstrate that the sum-rate is distributed flexibly among users in instantaneous channel conditions with the SPMA.

Since the seminal work of Grossglauser and Tse [1], the two-hop relay algorithm and its variants have been attractive for mobile ad hoc networks (MANETs) due to their simplicity and efficiency. However, most literature assumed an infinite buffer size for each node, which is obviously not applicable to a realistic MANET. In this paper, we focus on the exact throughput capacity study of two-hop relay MANETs under the practical finite relay buffer scenario. The arrival process and departure process of the relay queue are fully characterized, and an ergodic Markov chain-based framework is also provided. With this framework, we obtain the limiting distribution of the relay queue and derive the throughput capacity under any relay buffer size. Extensive simulation results are provided to validate our theoretical framework and explore the relationship among the throughput capacity, the relay buffer size and the number of nodes.

In this paper, we address the energy conservation problem with the quality of service (QoS) requirements taken into account for the physical downlink shared channel (PDSCH) in LTE/LTE-Advanced networks. By jointly allocating the modulation and coding schemes (MCS), resource blocks (RB), and power, we first propose a standards-compliant QoS-oriented power control algorithm (SQPC) for realistic systems to save energy. Specifically, with an appropriate MCS allocation, the proposed algorithm can tailor the power to match the QoS requirements. However, the SQPC saves energy at the cost of RB utilization. To this end, we further devise an Enhanced SQPC algorithm (ESQPC) to strike a balance between RB allocation and energy consumption. Finally, simulation results show that the proposed algorithms have the advantage of reducing nearly half of energy consumption compared to the existing algorithm, as well as demonstrate that the ESQPC can improve RB utilization against the SQPC.

Mobility load balancing (MLB) is an important use case of Self-Optimizing Networks (SONs). To combat the commonly encountered issues in conventional MLBs, such as the blind offloading without equilibrium and optimality guarantees, we propose an Enhanced MLB (ELB) scheme to conquer these problems with the double threshold design including the common trigger threshold and the fairness-aware ending one. First, we introduce the rationale behind our idea with simple analysis, then the newly presented the fairness-aware ending threshold is given by modeling the fairness metric during the optimal target cell selection process. Based on these analysis, we propose the ELB scheme with a double-threshold design. Simulation results show that the presented ELB scheme can well improve the system performance and the user experience quality.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs that operate on the same channel requested by the PUs will be affected, resulting in a possible network partition. Therefore, how to maintain the connectivity of CRNs when PU appears is a critical problem. In this paper, we propose a topology control algorithm to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using minimum number of channels. Theoretical analysis shows that the CRN can maintain connectivity upon any single channel interruption by PUs. The simulation results demonstrate that the proposed algorithm can reduce the number of required channels efficiently and preserve energy spanner property.

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.

Successive Interference Cancellation (SIC) is a potentially powerful technique for improving the performance of multi-hop wireless networks, owing to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. In this paper, we address the problem of finding the route with maximal end-to-end spectral efficiency in multi-hop wireless networks, under the constraint of optimal bandwidth sharing. By taking advantage of SIC, more transmission opportunities are exploited by the nodes along the selected path. We formulate a cross-layer optimization framework to quantify the spectral efficiency improvement with SIC and then make use of several structural properties to derive exact solutions. Additionally, three SIC-based routing alternatives with low computational complexity are proposed, on the basis of the conventional shortest path algorithm, to obtain spectrum-efficient routes. Numerous simulation results verify that SIC can bring significant gains in terms of spectral efficiency.

The deployment of smallcell eNodeBs (SeNBs), overlaid on existing macrocell eNodeB (MeNB) is widely accepted as a key solution for improving spectral efficiency (SE). However, both SeNB and MeNB may suffer significant performance degradation due to inter/intra-tier interference. Meanwhile, the energy efficiency (EE) is another promising requirement especially when SeNB/MeNB are densely deployed. In this paper, a utility function with an α-adjustable parameter is proposed to achieve an optimal tradeoff between EE and SE. Then, an energy-aware capacity offload between the MeNB and multiple SeNBs is formulated as a Nash bargaining game, which is significantly simplified during the following analysis. To attain a win-win optimality for both the relatively involved SeNBs and MeNB, an energy-aware trigger of source-MeNB, interference-related right selected target-SeNB, and mutual interference compensation are all provided in this paper, which help to attain more dimensions of diversities and gains. Finally, simulation results show the improved performance of our proposed scheme.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs transmitting on the same channel will be affected when the channel is requested by the PUs, thereby resulting in a possible network partition of CRNs. Therefore, how to maintain the connectivity of CRNs considering the activity of PUs is a critical problem. In this paper, we propose a centralized and a distributed topology control algorithm respectively to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using the minimum number of channels. In the power control phase, we tailor the topology for the channel assignment in the second phase. In the channel assignment phase, we utilize the graph coloring algorithm to achieve conflict-free transmission by assigning a channel to each SU. Theoretical analysis and simulation study show that the derived topology can maintain connectivity in the event of any single channel interruption by PUs. Simulation results also demonstrate that the proposed algorithms can efficiently reduce the average number of required channels for achieving bi-channel-connectivity and conflict-free transmission and ensure that the minimum power paths in the original network preserved in the final topology.

We aim to maximize system throughput subject to constraints on both short-term and long-term fairness in terms of Jain's index in single cell downlink OFDMA systems, where the transmission power is fixed. While it is accepted that short-term fairness implies long-term fairness, we find that this is not always true. Noting that long-term performance metric is the average of short-term ones, we point out that it depends on the averaging method and the fairness definition. We prove that short-term throughput Jain's index implies long-term throughput Jain's index. Therefore, we can remove the long-term fairness constraint if it is looser than the short-term constraint. Otherwise, we heuristically replace the long-term fairness constraint by a cumulative fairness constraint. We relax the considered discrete subchannel and slot allocation problem into a continuous convex problem, which can be efficiently solved. Then, the discrete resource allocation is derived by rounding the optimal solution. The analysis indicates that the rounding error is small. Simulation results show that we obtain a good suboptimal solution with small deviations from the optimal relaxed system throughput and the Jain's index constraints. Moreover, comparing with the strategies that take into account only long-term fairness, we guarantee both long-term and short-term fairness.

The architecture of decentralization makes future wireless networks more flexible and scalable. However, due to the lack of the central authority (e.g., BS or AP), the limitation of spectrum resource, and the coupling among different users, designing efficient resource allocation strategies for decentralized networks faces a great challenge. In this paper, we address the distributed channel selection and power control problem for a decentralized network consisting of multiple users, i.e., transmit-receiver pairs. Particularly, we first take the users' interactions into account and formulate the distributed resource allocation problem as a non-cooperative transmission control game (NTCG). Then, a utility-based transmission control algorithm (UTC) is developed based on the formulated game. Our proposed algorithm is completely distributed as there is no information exchange among different users and hence, is especially appropriate for this decentralized network. Furthermore, we prove that the global optimal solution can be asymptotically obtained with the devised algorithm, and more importantly, in contrast to existing utility-based algorithms, our method does not require that the converging point is one Nash equilibrium (NE) of the formulated game. In this light, our algorithm can be adopted to achieve efficient resource allocation in more general use cases.

Admission control is important in achieving QoS guarantees in multi-hop wireless networks. An efficient admission control algorithm requires an accurate estimation of the end-to-end delay distribution of the network. In this paper, we propose a method to estimate the end-to-end delay distribution under the general traffic arrival process and Nakagami-m channel model. We firstly propose a novel two-dimensional Markov Chain to model the node behaviors in a multi-hop multi-rate IEEE 802.11 network that is subject to interference and error prone channel. By combining the basic Probability theory and Network Calculus, we analyze the delay a packet experiences at each hop along a path. The per-hop delay result is used to develop the distribution of the end-to-end delay of a randomly chosen path. We then develop an admission control scheme for the traffic with stochastic QoS guarantees. Finally, through simulation results, we verify the accuracy of our analytical model and the effectiveness of the proposed algorithm.

In this paper, we investigate the fundamental tradeoff between energy efficiency (EE) and delay for time-varying and interference-free wireless networks. We formulate the problem as a stochastic optimization model, which optimizes the system EE subject to network stability and the average and peak transmit power constraints. By adopting the fractional programming theory and Lyapunov optimization technique, a general and effective algorithm, referred to as the EE-based dynamic power allocation algorithm (EE-DPAA), is proposed. The EE-DPAA does not require any prior knowledge of traffic arrival rates and channel statistics, yet yields an EE that can arbitrarily approach the theoretical optimum achieved by a system with complete knowledge of future events. Most importantly, we quantitatively derive the EE-delay tradeoff as$[O(1/V),O(V)]$with$V$as a control parameter for the first time. This result provides an important method for controlling the EE-delay performance on demand. Simulation results validate the theoretical analysis on the EE-delay tradeoff, as well as show the adaptiveness of the EE-DPAA.

Multi-hop heterogeneous cellular networks (MHCNs) consist of conventional macro cellular networks overlaid with an irregular deployment of low-power base stations (BSs), where the communication between BSs and mobile users can be established through a single hop or multiple hops. By modeling different kinds of randomly located BSs as K tiers of independent homogeneous Poisson Point Processes, we first explore the capacity of downlink MHCNs and derive the expression of capacity under Rayleigh fading channels. Particularly, the capacity gain achieved by cell splitting and multi-hop relaying is quantified for the first time. We then study the effects of BS density, transmit power, and signal-to-interference-plus-noise-ratio (SINR) threshold on the capacity of MHCNs. More importantly, we obtain the spectral efficiency enhancement condition under which the increase of BS density and transmit power improve the spectral efficiency, thereby enhancing the capacity. One interesting observation is that at a given SINR threshold, the capacity increases with BS density when all the tiers have the same SINR threshold. Moreover, the capacity of some special networks (i.e., heterogeneous cellular networks, multi-hop cellular networks, and conventional cellular networks) are derived directly by specializing some system parameters in our results. Finally, numerical studies and simulations are conducted to validate our analysis.

An analytical approach to quantifying the routing overhead in wireless ad hoc networks is presented in this paper. We find that in addition to the traditional control overhead and sub-optimal routing overhead, the retransmissions of discarded packets due to buffer overflow in receiver nodes on a route will consume extra bandwidth, which increasing the routing overhead. In this paper, we focus on the impact of packet loss process, analytical expressions for routing overhead and minimal packet loss rate are also derived. A simulation comparing retransmission-aware routing and a hypothetical optimal reactive routing protocol is used as a supplement of our theory, which shows that there still has a great potential to reduce the overhead to improve the network capacity.

In wireless resource allocation, improving system throughput and simultaneously enhancing user fairness are two fundamental but contradictory objectives. As for fairness, both short-term and long-term fairness are of significant importance. However, less effort has been dedicated to explore the optimal tradeoff between system throughput and the two mentioned fairness in terms of widely used Jain's index. In this context, we aim to maximize system throughput subject to constraints on both short-term and long-term fairness in single cell downlink OFDMA systems. The difficulty of this issue lies in that the considered subchannel and slot allocation problem is a nonlinear integer programming problem, and furthermore seems to be non-causal. To overcome these challenges, we first relax the integer variables. Second, we prove that short-term fairness ensures long-term fairness so that the long-term fairness constraint is redundant and can be removed. Third, the problem is decomposed into a sequence of short-term convex optimization problems that can be easily solved. Numerical results show that the proposed method achieves a good suboptimal solution with small deviations from the optimal relaxed system throughput and the Jain's index constraint.

Enhancing system throughput and improving user fairness are two basic but contradictory objectives for resource allocation in wireless cellular networks. To obtain an efficient tradeoff between these two goals, Network Utility Maximization (NUM) framework has been adopted with log-utility to obtain proportional fairness among all the users in the network. However, such tradeoff can not control the bias towards throughput or fairness. In this paper, we focus on α-fairness NUM in Soft Frequency Reuse (SFR) based cellular networks, where SFR is an attractive frequency reuse technique to mitigate Inter-Cell-Interference (ICI) and α can be utilized to adjust the tradeoff. The difficulty of the considered issue comes from that it is a Mixed Integer Programming (MIP) problem taking into account both intra-cell user scheduling and inter-cell user association. To overcome this challenge, the α-fairness NUM problem is decomposed into two subproblems, which are dealt with one by one. First, maximize intra-cell utility by user scheduling and second, maximize network utility by distributed user association. Numerical results show that the proposed algorithm approaches the optimal solution of the α-fairness NUM problem. Also, we get a better tradeoff between throughput and fairness, where fairness is measured by Jain's index. Particularly, we improve the maximum Jain's index from about 0.3 to about 1.

Successive Interference Cancellation (SIC) is a new physical layer technique which enables the receiver to either partially cancel the interfering signals or receive more than one desired signal at a time. By fully exploring the potential advantages of SIC, we develop an SIC Aware Routing protocol, referred to as SAR, aiming at enhancing the overall end-to-end throughput. An SICable condition is defined, by which our routing protocol can discover the links with potential SIC opportunities to improve the overall throughput. By using the concepts of spatial resource consumption and bandwidth efficiency, we characterize the benefits of SIC effectively. Based on the concepts, we design an SIC aware routing metric to discover the paths with high throughput and less spatial resource consumption. Simulation results show that our routing protocol achieves significant gains in network throughput and SIC ratio compared with minimum hop count routing and conventional interference aware routing.

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.

Owing to the capability of accumulating mutual information from the transmission of previous nodes, rateless codes can boost the network performance considerably, and hence have sparked much interest recently. However, how to efficiently schedule multi-hop multi-relay transmissions with the aid of rateless codes remains a challenging work. Particularly, it requires high complexity to find an optimal route due to its inherent combinatorial nature. In this paper, we formulate the optimal transmission scheduling as a dynamic programming (DP) problem by defining a novel state and constructing a tree-structured state transition diagram. It is from a point of view of DP that we further propose two low-complexity algorithms, namely S-DIRAC and Fano-DIRAC, with negligible performance loss based on the idea of sequential decoding of convolutional codes. Simulation results indicate that the low-complexity algorithms almost always find the optimal solution and show the superiority of routing with mutual information accumulation compared to conventional shortest path routing.

Interference management is a hot issue in Heterogeneous Networks (HetNets), which is very crucial for the performance promotion in heterogeneous cellular networks with full frequency reuse. Focusing on mitigating the interference between Macrocell and Femtocell, we propose the IM-Torch (Interference Mitigation via Traffic Offloading in Macro/Femtocell + WiFi Heterogeneous Networks) scheme to handle this problem via traffic offloading. We formulate it as a Mixed Integer Nonlinear Program which is hard to solve, and design a two-step heuristic algorithm to solve this problem. In the first self-scheduling step, Femtocell tries to reallocate the power and PRBs (Physical Resource Blocks) for HUEs (Home User Equipment) to mitigate the interference. After the failure of the first step, Femtocell offloads some necessary HUEs with data services to WiFi and re-adjusts the resources for HUEs to alleviate the interference. The analysis and simulations validate that IM-Torch scheme can greatly alleviate the interference in HetNets and thus improve the system total throughput of Femtocell while guarantee the QoS of Macrocell and HUEs with real time applications.

Traditional load balancing schemes only considered two cells cooperation that is less likely to succeed. A novel scheme, load balancing by cells- cooperation-chains (C$^3$LB), is proposed. C$^3$LB establishes multi-level cells-cooperation-chains (C$^3$) to transfer traffic and extents the conditions of traffic transfer. We formulate a minimum-level C$^3$ selection problem and propose a simple algorithm to solve it. In addition, we present a C$^3$LB protocol to execute the found C$^3$. Numerical results show that as the maximum allowed levels of C$^3$ increase, the system call blocking probability decreases. Finally, we give the proposed value of the maximum allowed levels.

End-to-end delay analysis is an important element of network performance analysis in multi-hop wireless networks. In this paper, we analyze the end-to-end delay of wireless networks with general traffic model and capacity-varying channel. A new concept of residual effective capacity using Effective Bandwidth theory and Effective Capacity theory is presented, which allows us to calculate the cumulative distribution function of queuing delay. We derive a formula to calculate the average end-to-end delay for multi-hop wireless networks and validate our analysis through simulations.

We investigate the interactive behavior and strategic decision-making between multiple secondary users (SUs) and primary users (PUs), both of which are end-to-end performance aware in cognitive networks. A Stackelberg game is utilized to formulate the spectrum utilization maximization problem after the complex interference situation is analyzed. Especially, an interference power cap (IPC) function predefined by PUs as a pricing function is introduced into the utility function design of SUs to guarantee QoS of PUs, as well as to decouple constraints. Further, an asymmetric information situation can be formed by considering PUs as leaders who employ the optimal water-filling algorithm, and the closed-form power strategy of SUs can be derived. And accordingly, SUs as followers can observe the available information to do more foresighted decision by learning. What is more, we prove the optimality and existence of the deceived solutions. Numerical results demonstrate that the proposed distributed algorithm provides more spectrum revenue and better QoS guarantees to PUs with limited iterations.

Most of previous topology control algorithms that aim to extend the network lifetime focus only on the energy consumption of transmissions, and thus construct a static topology without adaptation to the varying energy consumption rates at different nodes. As a result, the network lifetime has not been prolonged to the most extent as expected. However, other topology control algorithms that consider the residual energy levels of nodes have not addressed the problem of fault tolerance. In this paper, we propose an adaptive topology control algorithm, Residual Energy-aware Shortest Path (RESP), which not only balances the energy consumption of different nodes but also provides fault tolerance. Particularly, RESP is able to ensure k-edge connectivity and preserve the minimum-weight path. Simulation results show that RESP extends the network lifetime and is superior to other existing localized fault-tolerant algorithms.

With the development of heterogeneous networks, concurrent transmission of Multimode-User Equipment (MUE) within multiple Radio Access Technologies (RATs) can improve transmission reliability and boost system performance. In this paper, some novel splitting strategies combining with different queuing management architectures are presented to obtain the multi-radio transmission diversity gain effectively. Two- dimensional discrete-state continuous-time Markov process is used to analyze our strategies and closed-form solutions have been made. Simulation results demonstrate that our proposed flow splitting strategies utilize the system resources efficiently and outperform current strategies.

Network performance is strongly dependent on network topology, especially in scale-free network in which nodal-degree distribution is a power-law distribution. In this paper, a routing protocol for cognitive scale-free networks, called as CSRP (Cognitive Scale-free Routing Protocol), was proposed. In CSRP, each node predicts the numbers of queuing packets in its neighbor nodes when routing decisions are made, so that the optimal path is established with the tradeoff between the shortest path length and load bearing. Based on the comprehensive understanding of network traffic distribution and intelligent routing decision, CSRP can reduce network congestion dramatically. Further, we analyze the critical threshold of arrival rate where the network status changes from free to congestion. This value can reflect the maximum capacity of a system handling its traffic. Under the same network scenario, CSRP has the best critical value. Compared with other existing routing protocols, CSRP is more successful in keeping delay low and more traffic flow can be allowed into the network.

From an energy-saving perspective, the authors investigate the downlink power control issue of the two-tier heterogeneous networks (HetNets) using a cognitive radio train of thought. The authors consider the HetNets scenario of one macro-cell evolved-NodeB (eNB) and multiple femto-cell Home evolved NodeBs (HeNBs) cooperatively coexisting to provide better services. A specific HeNB allows macro-mobile station (macro-MS) previously associated with eNB to access to it for better signal-to-interference plus noise ratio (SINR) guarantee. As a reward, the macro-MS pays a certain of revenue to HeNB as the incentive mechanism for this HeNB's downlink extra power consumption, which is manifested in the design of the price function. The throughput bound of Macro-MSs in HeNB cell is given. Then, the authors select the SINR as the performance measure and formulate the power control of selected HeNBs as multi-constrained optimisation problem. Meanwhile, the authors derive the sub-optimal and closed-form power control of individual HeNB, based on which the authors design the distributed algorithm with the aid of eNB and HeNBs cooperatively to implement the pricing information exchange. Simulation results show the improved performance of the convergence, the energy-efficiency measured by ‘energy-per-bit’ and the throughput of the ‘Proposed-Cognitive-x’ power control algorithm.

Scalability of routing protocols is one of the most important open problems in large scale wireless networks. In this paper, a routing protocol for large scale wireless network, called as SCR (Small-world based Cooperative Routing protocol), was proposed based on the small world phenomenon and cooperative communication. In SCR, each source node selects its short-cut node, through which the path length to the destination node is greatly reduced. The cooperative communication link is formed to decrease the hops between the sender node and its short-cut node to match the small world phenomenon. We show that in a network with nXn nodes, the average path length of SCR is O [n log n)/[Mq)], 1≤q≤log n, where M is the number of cooperative nodes. If the average hops between the sender node and its short-cut node is approximately equal to one hope by using the cooperative communication link, the average path length of SCR is O[(log n)2/q] . Compared with other existing routing protocols, the SCR has much shorter path length and low routing overhead.

Vector perturbation (VP) is a prominent precoding technique attracted a lot of attention in recent years. Until now, however, various extended VP techniques proposed to apply in multiuser precoding are almost restricted to one antenna configuration of each user. The restriction does not meet the development of next generation wireless systems. So, the well-known block diagonal (BD) algorithm and VP is naturally combined and proposed, named BD-VP for short, to solve this problem. However, the BD-VP completely suppressing multi user interference (MUI) at the expense of noise enhancement results in performance degradation. To overcome the shortcoming of BD-VP, we propose generalized channel inversion VP (GCI-VP) algorithms. Analysis and simulation results show that the proposed ZF GCI-VP is equivalent to the BD-VP, while the algorithm MMSE GCI-VP I and MMSE GCI VP II greatly outperform the BD-VP.

In this paper, a MIMO routing protocol called as DZMRP (Double Zones MIMO Routing Protocol) for MIMO ad hoc networks was proposed. The DZMRP is a hybrid routing protocol that proactively maintains routes within a local zone of the network, which referred as the local routing zone and is divided into diversity zone and multiplex zone. Different updating frequencies of the changes of link connectivity are associated with the diversity zone and multiplex zone, so that the routing maintenance overhead is decreased dramatically. By leveraging the multiplex and diversity gains of MIMO links for the high data rate and the range extension respectively, the DZMRP improves the efficiency of a reactive route query/reply mechanism. Compared with other existing routing protocols such as ZRP and MIR, the DZMRP has much higher end-to-end throughput and lower packet delivery delay due to the dramatic reduction in protocol overhead.

Cooperative communications achieve tremendous improvements in system performance. Meanwhile, it tends to change the conventional access and transmission schemes in wireless networks. Thus, CoopMAC is proposed and analyzed for fully connected WLANs. It improves the performance of the network dramatically by enabling additional collaboration from other nodes. However, how does it work in multi-hop Ad Hoc networks is not well-understood yet. In this paper, we first propose an analytical model to evaluate the performance of CoopMAC for multi-hop Ad Hoc networks with consideration of hidden node problem. Our analysis results show that the cooperative transmissions are frequently interrupted by hidden nodes' interference. To mitigate the effect of hidden node, an enhanced CoopMAC (ECoopMAC) is further proposed without introducing any overhead and complexity, which jointly optimizes the order of handshake and the helper selection metric. By increasing the probability of successful cooperative transmission and decreasing the interference to other flows, the saturated throughput and access delay of multi-hop Ad Hoc networks are improved by ECoopMAC remarkably. Extensive simulations evaluate the performance of our analytical model and protocol, the results of analysis and simulation match well. Compared with CoopMAC, the saturated throughput is improved up to 12% on average.

Previous works on network coding capacity for wireless networks have taken the assumption that the network is stationary. In this paper, the mobility of ad hoc networks is considered as a key factor influencing network capacity, and a new and unified analytical expression of the capacity of a mobile ad hoc network applying network coding is derived under the two main mobility models, including random waypoint and random walk. The simulation results show that under the mobility condition, the network capacity of mobile ad hoc networks applying network coding still exhibits a concentration behavior around the mean value of the minimum cut.

Through sensing and utilizing available network resources, cognitive network can obviously increase network performance. In this paper, a distributed on-demand routing protocol with traffic awareness (TACR) is proposed for cognitive wireless network. This routing protocol establishes the path based on the cognition and reasoning of traffic loads in a network and it also meet quality of service (QoS) requirements by introducing autonomous intelligence-Q-learning. Simulation results show that the TACR routing protocol shortens the average end-to-end delay significantly and also improves the average throughput.

Topology control with per-node transmission power adjustment is an effective way to extend network lifetime. However, due to the commonly used assumption of homogeneous wireless networks with uniform maximal transmission power, most topology control algorithms suffer from performance degradations in practical applications where physical characteristics of each node may be different. Hence, it is valuable to take heterogeneous networks into consideration. In such an environment, however, most of existing algorithms mainly consider the energy consumption caused by transmitting, meanwhile ignore the residual energy of network nodes, thus in fact they can not balance energy consumption efficiently. In this paper, a localized distributed topology control algorithms ESATC (Energy-aware Self-Adjust Topology Control) is proposed for extending network lifetime of heterogeneous wireless Ad Hoc networks. Based on overall consideration of power consumption and residual energy of two end nodes, ESATC builds a dynamic network topology that changes with the variation of node energy. Without location information, each node self-adjusts its transmission power according to the network information collected locally, which makes our algorithm suit for large scale networks. Theoretic analysis and experiment results show that ESATC provides routing with an underlying topology with bi-directional reachability and minimum-cost property. Compared with other algorithms, it can extend the lifetime of networks dramatically.

In this paper, we propose the optimal resource allocation strategies for energy constrained coded cooperative networks. By combining power control and multi-relays selection with LOC adjustment, our schemes aim at providing higher QoS and extending network lifetime. We consider the TDMA based scenario where one node acts as the source and the other nodes can be selected as relay nodes in a time slot and each node is limited by separate power constraint. Firstly, we build an optimization model for minimizing and balancing the energy consumption under QoS constrains. After that, when the LOC is constant, a closed form optimal solution is got by KKT conditions, which results in a dynamic resource allocation strategy. Moreover, based on solution for the fixed LOC, a suboptimal scheme is further proposed for a variable LOC via one dimension searching. The simulation results show that our schemes can improve network QoS and prolong network lifetime dramatically comparing with other existing cooperative resource allocation schemes.

Topology control via per-node transmission power adjustment has been shown effective in extending network lifetime. However, most of existing algorithms construct static topologies which fail to consider the residual energy of network nodes, thus in fact they can not balance energy consumption efficiently. To address this problem, a lightweight distributed topology control algorithm EDTC (Energy-aware Dynamic Topology Control) is proposed in this paper. Based on the link metric reflecting both the energy consumption rates and residual energy levels at the two end nodes, EDTC generates a dynamic network topology that changes with the variation of node energy. In addition, without the aid of location information, each node determines its transmission power according to local network information, which reduces the complexity and overhead of EDTC greatly. Theoretic analysis and experiment results show that EDTC preserves network connectivity and minimum-cost property and compared with other algorithms, it can extend network lifetime more remarkably.

Biconnectivity is the baseline graph theoretic metric of fault tolerance to node failures which can keep network connectivity while allowing unexpected node failures. In this paper we analyze this property for one-dimensional wireless Ad Hoc networks with finite nodes, which finds many applications in the real world, such as bus networks built along freeways and sensor networks deployed along frontiers. With common adopted assumption of uniform node distribution for static networks, the formula for the critical transmitting range that realizes network biconnectivity with certain probability is derived. Simulation results validate the accuracy of our conclusion and confirm its efficiency in practical network design.

Large numbers of applications and technical mechanisms in wireless ad hoc networks requiring that the network is connected, so critical nodes, whose removal will disconnect the network into two or more separate components, will play an important role in wireless ad hoc networks. In this paper, a novel critical node detection algorithm-DMCC (detection algorithm based on midpoint coverage circle ) is presented. DMCC is a distributed algorithm which adapts the dynamic topology adaptively, detects the critical node faster and more reliably, and decreases the detection overheads efficiently.

Compared with the small overhead of data payload in sensor network, the overhead of MAC address is significant from the point of view of energy-saving. A distributed algorithm (virtual grid spatial reusing-VGSR) for MAC address assignment is presented in this paper, which is a low energy cost algorithm and reduces the size of the fixed MAC address greatly based on the mapping of geographical position. Moreover, VGSR algorithm scales well with the network size and achieves the optimum performance by adjusting the communication range of sensor nodes. In typical scenarios, the MAC address size is 5 bits and the corresponding average size is only 3.86 bits, which outperforms other existing approaches

To improve the binary exponential backoff algorithm (BEB)'s channel throughput of IEEE 802.11 protocol, we propose a novel collision reduced multiple access (CRMA) protocol based on slow contention window decrease mechanism and runtime optimization method. CRMA protocol can record the current backoff stage of the latest successful transmission under the overload network precisely. As a result, it decreases the collision times and improves the channel utilization.

Satisfying critical QoS requirements in next generation networks poses major challenges, due to its intrinsic complexity of network resource allocation. This paper considers the problem of load balance based bandwidth allocation for delay sensitive services. For satisfying deterministic end-to-end delay requirement, a simple and efficient algorithm for path level optimal bandwidth allocation, path level equal ratio allocation algorithm (P-ERA), is developed first. Based on P-ERA, another algorithm for network level optimized bandwidth allocation, network level equal ratio allocation algorithm (N-ERA), is also presented for much more complicated network circumstances. N-ERA algorithm features appropriate route selection and balanced bandwidth allocation, and can adaptively avoid, or at least delay, the emergence of network bottleneck when a network is heavily loaded. Extensive simulations indicate that N-ERA algorithm can make full use of network bandwidth and admit more services, even if they are delay critical, than other ones dealing with the same problem. And the less computation complexity of N-ERA algorithm makes it of great application value.

One of the principal disadvantages of orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Carrier frequency offset can be divided into two parts: an integer one and a fractional one. The integer frequency offset has no effect on the orthogonality among the subcarriers, however causes a circular shift of the received data symbols, resulting in a BER of 0.5. The maximum likelihood (ML) estimation algorithm of the integer frequency offset is derived under the assumption that the channel impairments only consist of additive noise. Simulation results show that it can perform well even in a time-dispersive channel. Its performance is assessed and compared with the conventional method by computer simulations for the additive white Gaussian noise (AWGN) channel and a multipath fading channel.

The IEEE 802.11 multiple access control protocol was modified for use in multi-channel, multi-hop ad hoc network, through the use of a new channel-status indicator. In particular, we have evaluated the improvement due to the multi-channel use. We report in this paper on the results of the throughput per node and the end-to-end delay for the modified IEEE 802.11 protocol for different network sizes. Using these results, we were able to propose a number of throughput scaling laws. Our simulation results show that the throughputs per node with multiple channels for the line and the grid ad hoc network topologies will increase by 47.89%, and by 1.39-163%, respectively, for networks with 16 to 64 nodes, as compared with that of single channel.

A novel routing protocol for ad hoc networks - DSARP (Delay Sensitive Adaptive Routing Protocol) - is presented in this paper. According to DSARP, the reliable route for delay sensitive traffic can be supplied and the route can be selected based on the constrained condition - "the shortest route and the lowest average delay". Therefore, the "hotspot" on the shortest path can be avoided. Meanwhile, DSARP can provide a QoS guarantee and improve the performance of the network. Simulation results show that DSARP performs better than the DSR routing protocol used in ad hoc wireless networks.

The wireless ad-hoc network is self-organizing. rapidly deployable and without fixed infrastructure. The hosts in ad-hoc networks communicate with each other over a wireless channel without any centralized control. The basic problem is to obtain a distributed routing scheme so that any mobile host can transmit/receive data from any other host in the network. As we know, in terms of the way in which nodes obtain information, routing protocols for ad-hoc networks have been classified as table-driven and on-demand. In table-driven routing protocols, the interactive mode of the control message has a great effect on network performance. In this paper, a novel concept of "different interactive mode for different scale of network" is presented. The network performance is optimized by using appropriate periodical or nonperiodical interactive mode based on the scale of the network. A different interactive mode is given for different scales of the network by theoretical analysis and algorithm simulation. The simulation result is of great practicability.