In this paper, we investigate the joint beamforming and power allocation problem in multi-antenna Cellular Vehicle-to-Everything (C-V2X) networks under uncertain channel state information (CSI). Our objective is to minimize the beamforming vector at the base station and the transmit powers of vehicle users while satisfying probabilistic quality-of-service (QoS) constraints. To address the uncertainty of CSI, we first develop an ellipsoid-based uncertainty set learning approach, which models the uncertain CSI as a symmetric ellipsoid. Building on this uncertainty set, we propose a robust counterpart transformation method to reformulate the joint beamforming and power allocation problem into a deterministic semi-definite problem without probabilistic constraints. Through our analysis, the ellipsoid-based uncertainty set exhibits significant conservatism when applied to uncertain CSI with asymmetric distributions. To mitigate this conservatism, we propose a support vector clustering (SVC)-based uncertainty set learning approach, which can tightly enclose the distribution of uncertain CSI. To further simplify the spatial structure of the SVC-based uncertainty set, we develop aK-Medoids-based equivalent set construction (KMSC) approach, significantly reducing the number of variables in the resulting robust equivalent problem. Finally, we conduct extensive simulations to evaluate the performance of our proposed robust approaches and compare them with non-robust methods.

This paper investigates a robust resource allocation for reconfigurable intelligent surface (RIS) aided vehicle-to-everything (V2X) communications with imperfect channel state information (CSI). To satisfy the diverse quality-of-service (QoS) requirements of V2X communications, we aim at maximizing the sum capacity of cellular user equipments (CUEs) while guaranteeing the outage probability constraints of vehicular user equipments (VUEs). Then, the considered problem is decomposed into the subproblems of power, spectrum and RIS phase shift op-timization. A graph-based power allocation method is presented to transform the non-convex power allocation subproblem into a tractable one and obtain the closed-form solutions. A worst-case conditional value-at-risk (CVaR) approximation-based method is developed to convert the RIS phase optimization subproblem into a convex semidefinite programming (SDP) problem. We propose a low-complexity learning-based alternating optimization approach which alternately optimizes three subproblems to obtain a near-optimal solution. Simulation results demonstrate that the proposed approach outperforms other benchmark methods.

In this paper, we study the resource allocation in high mobility vehicle-to-everything (V2X) networks with only slowly varying large-scale channel parameters. For satisfying the diversity requirements of different types of links, i.e., low delay for vehicle-to-infrastructure (V2I) connections and ultra-reliability for vehicle-to-vehicle (V2V) connections, we formulate a joint power, spectrum and vehicle local computing ratio allocation problem to minimize the delay of V2I links whilst satisfying the V2V reliability constraint. For solving the formulated problem, a Feasible Region Transformation Method is firstly developed to convert the probabilistic V2V reliability requirement into a computable constraint. In addition, a Robust Signal to Interference Plus Noise Ratio (SINR) Modified Method is proposed to give the computable expression for the V2I throughput. Then, a parallel Deep Neural Network (DNN) framework is designed for the resource allocation in V2X networks, where one is the transmit power control unit and the other is the local computing ratio allocation unit. After that, a Feedback-oriented Learning Method is proposed to train the parallel DNN-based resource allocation framework, in which the output of DNN is used as feedback to dynamically revise the training loss function along with the training process. Afterwards, the Hungarian method is employed to obtain the optimal spectrum matching. Finally, we conduct the simulations to show that the proposed learning-based algorithm has better performance compared with other general algorithms.

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user’s throughput whilst ensuring a chance constraint for D2D. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, the chance constraint under these uncertainty sets is transformed into computation convenient convex constraints. To overcome the conservatism of symmetrical-geometry-based approach, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with the obtained convex constraints. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

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.

The resource allocation in vehicle-to-everything (V2X) communications face a great challenge for satisfying the heterogeneous quality of service (QoS) requirements of the ultra-reliable safety-related services and the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. In this paper, we first consider an optimistic scenario where the distribution of uncertain channel state information (CSI) can be deterministic and accurately known at the eNB. Then, a low-complexity resource allocation approach is developed, in which the probabilistic QoS constraint of V2V is transformed into a computable optimization constraint. To deal with the scenario with unknown uncertain CSI distribution, we develop a distributionally robust resource allocation approach for converting the intractable chance constraint of V2V into a deterministic semidefinite constraint based only on the first- and second-order moments of uncertain CSI. For alleviating the conservatism of above approach, a support-based distributionally robust resource allocation approach is developed to tighten the semidefinite constraint by utilizing the support information of uncertain CSI. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

Vehicle-to-everything (V2X) communications face a great challenge in delivering not only the low-latency and ultra-reliable safety-related services but also the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. This paper focuses on the robust resource management of V2X communications with the consideration of channel uncertainties. First, we formulate a transmit power minimization problem, whilst guaranteeing the different quality-of-service (QoS) requirements. To achieve the robustness of QoS provisions against channel uncertainties, a statistical leaning approach is developed to learn the uncertainties from the data samples of the random channel coefficients as a convex ellipsoid set, which is also called high-probability-region (HPR). Then, the highly intractable power minimization problem is converted into a second-order cone program by the robust optimization approach. Afterwards, we propose a joint set partitioning and reconstruction mechanism to further reduce the total transmit power by pruning the rough HPR into a more precise uncertainty set, which leads to a trackable second-order cone program and a linear program. Finally, we prove that the network performance can be effectively enhanced by the improvement mechanism. Simulation results verify the effectiveness of the robust resource allocation approaches over the non-robust one.

Long-term evolution (LTE) and wireless local area network (WLAN) are often presented as opposing technologies. Hence, efficient partitioning of the spectrum resources carries critical importance for achieving the coexistence of these on the unlicensed spectrum band. In this paper, we firstly develop an online spectrum partitioning algorithm, which needs little signal transmission and exchange between coordination manager and networks. Then, we focus on the convergence analysis of the online spectrum partitioning algorithm, which is difficult due to the time-varying wireless channels. To overcome this challenge, we model the algorithm and network dynamics as the stochastic differential equations (SDE) and show that the algorithm convergence is equivalent to the stochastic stability of a virtual stochastic dynamic system constructed by the SDEs. Then, we give the sufficient condition about the algorithm convergence and the upper bound on the tracking error of the spectrum partitioning algorithm under exogenous variations of time-varying channel state information (CSI). Based on the insights of the impact of time-varying CSI on algorithm convergence, an online compensative spectrum partitioning algorithm is developed to offset the tracking error caused by the disturbance of time-varying CSI. Through performance evaluation, we show that the coexistence performance efficiency will come at low expense of algorithm complexity and signal overhead.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.