The rapid advancement of the satellite industry offers unprecedented opportunities for enabling Internet of Everything (IoE) applications over satellite networks. A key characteristic of such applications is that computation cannot begin until the entire application data has been fully received at the destination. To meet strict end-to-end delay constraints, minimizing the total application delay is essential. However, this requirement violates the optimal substructure property commonly assumed in traditional shortest path routing problems. Existing routing solutions often overlook these unique computation constraints and rely on substructure-preserving heuristics, resulting in suboptimal delay performance. Moreover, they lack reliability in producing delay-guaranteed routing solutions, which leads to low task completion ratios under stringent application deadlines. To overcome this problem, we propose FlexSatIoEa routing scheme that allows for flexible buffering data over satellite networks. FlexSatIoE formulates this routing problem as an integer linear programming (ILP) problem, to provide the optimal solution. As the network scales, considering the computational intractability of ILP, FlexSatIoE further modifies the storage time-aggregated graph to comprehensively model the satellite networks’ compute, storage and transmission resources. Based on the graph extension, FlexSatIoE designs an efficient routing algorithm, enabling flexible use of buffer resources by using a flow reassignment mechanism. We conduct extensive experiments over the setting of real-world satellite networks. The results show that FlexSatIoE reduces the average delay and increases the number of completed tasks by up to 50% and 40% respectively, as compared to the existing schemes, demonstrating the superior capability and reliability of FlexSatIoE in ensuring deterministic application delays.

Time-Triggered Ethernet (TTEthernet) has been widely applied in many scenarios such as industrial internet, automotive electronics, and aerospace, where offline routing and scheduling for TTEthernet has been largely investigated. However, predetermined routes and schedules cannot meet the demands in some agile scenarios, such as smart factories, autonomous driving, and satellite network switching, where the transmission requests join in and leave the network frequently. Thus, we study the online joint routing and scheduling problem for TTEthernet. However, balancing efficient and effective routing and scheduling in an online environment can be quite challenging. To ensure high-quality and fast routing and scheduling, we first design a time-slot expanded graph (TSEG) to model the available resources of TTEthernet over time. The fine-grained representation of TSEG allows us to select a time slot via selecting an edge, thus transforming the scheduling problem into a simple routing problem. Next, we design a dynamic weighting method for each edge in TSEG and further propose an algorithm to co-optimize the routing and scheduling. Our scheme enhances the TTEthernet throughput by co-optimizing the routing and scheduling to eliminate potential conflicts among flow requests, as compared to existing methods. The extensive simulation results show that our scheme runs >400 times faster than standard solutions (i.e., ILP solver), while the gap is only 2% to the optimally scheduled number of flow requests. Besides, as compared to existing schemes, our method can improve the successfully scheduled number of flows by more than 18%.

Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.

In this work, we investigate resource allocation strategy for real time communication (RTC) over satellite networks with virtual network functions. Enhanced by inter-satellite links (ISLs), in-orbit computing and network virtualization technologies, large-scale satellite networks promise global coverage at low-latency and high-bandwidth for RTC applications with diversified functions. However, realizing RTC with specific function requirements using intermittent ISLs, requires efficient routing methods with fast response times. We identify that such a routing problem over time-varying graph can be formulated as an integer linear programming problem. The branch and bound method incurs$\mathcal{O}(\vert \mathcal{L}^{\tau}\vert \cdot(3\vert \mathcal{V}^{\tau}\vert+\vert \mathcal{L}^{\tau}\vert )^{\vert \mathcal{L}^{\tau}\vert })$time complexity, where$\vert \mathcal{V}^{\tau}\vert$is the number of nodes, and$\vert \mathcal{L}^{\tau}\vert$is the number of links during time interval$\tau$. By adopting a k-shortest path-based algorithm, the theoretical worst case complexity becomes$O(\vert \mathcal{V}^{\tau}\vert !\vert \mathcal{V}^{\tau}\vert ^{3})$. Although it runs fast in most cases, its solution can be sub-optimal and may not be found, resulting in compromised acceptance ratio in practice. To overcome this, we further design a graph-based algorithm by exploiting the special structure of the solution space, which can obtain the optimal solution in polynomial time with a computational complexity of$\mathrm{O}(3\vert \mathcal{L}^{\tau}\vert +(2\log\vert \mathcal{V}^{\tau}\vert +1)\vert \mathcal{V}^{T}\vert )$. Simulations conducted on starlink constellation with thousands of satellites corroborate the effectiveness of the proposed algorithm.

How to allocate network paths and their resources to minimize the delivery time of data transfer tasks over time-varying networks? Solving this MDDT (Minimizing Data Delivery Time) problem has important applications from data centers to delay-tolerant networking. In particular, with the rapid deployment of satellite networks in recent years, an efficient MDDT solver will serve as a key building block there.The MDDT problem can be solved in polynomial time by finding the maximum flow in a time-expanded graph. A binary-search-based solver incurs O(N•log N•Γ) time complexity, where N corresponds to time horizon and Γ is the time complexity to solve a maximum flow problem for one snapshot of the network. In this work, we design a one-pass solver that progressively expands the graph over time until it reaches the earliest time interval n to complete the delivery. By reusing the calculated maximum flow results from earlier iterations, it solves the MDDT problem while incurring only O(nΓ) time complexity for algorithms that can apply our technique. We apply the one-pass design to Ford-Fulkerson algorithm and evaluate our solver using a network of 184 satellites from Starlink constellations. We demonstrate >75× speed-up in the running time and show that our solution can also enable advanced applications such as preemptive scheduling.

Virtual Reality (VR) applications delivered over wireless networks have attracted interest from academia and industry. The delay of VR applications is mainly composed of computing delay and communication delay. Although cloud computing centers have adequate computing power, accessing them requires long communication delay. Mobile edge computing (MEC), which offloads the computing power from the cloud computing center to the edge, is regarded as a feasible way to alleviate communication delay. However, due to the differences in the capability and location of MEC nodes, the selection of MEC nodes will affect both the computing delay and communication delay. In this paper, we focus on the joint representation of computing and communication resources and the selection of the optimal MEC node. First, we adopt graph-based joint computing and communication resources (GCC) model for VR applications routing and formulate the VR routing problem as an ILP problem. Then we design a Computing Nodes Expanded (CNE) algorithm, which allows us to use the Dijkstra algorithm to quickly obtain the optimal computing node and the path of shortest total delay. Finally, we run numerical experiments to evaluate the performance of the proposal algorithm. Simulation shows that the CNE algorithm can reduce the total delay by 42.9% and increase the delay satisfaction ratio by 23.3% compared to other benchmark algorithms.

Earth observation satellites using asynchronous ground-assisted federated learning (FL) can avoid transmitting massive raw image data to ground. However, current FL approach uses only satellite-to-ground-station links, causing long delay for model parameter transfer. A promising direction to reduce delay is to use inter-satellite links. We identify that ground-assisted asynchronous FL requires a satellite to send all data of its model parameter to ground before ground station can start to update the model. This new feature prevents current routing algorithms (e.g., CGR) from being applicable. Therefore, we propose an FL task-aware routing and resource reservation (FLRRS) scheme to optimize the delay of FL model parameter transfer. First, we formulate the problem as an integer linear programming (ILP) problem, which is non-convex and intractable. Thus, we enhance the storage time-aggregated graph to model computing, storage and transmission resources of satellite network, and propose a graph-based routing and resource reservation algorithm. The numerical simulation based on a real-world satellite network shows that FLRRS runs much faster than CVXPY solver. Besides, FLRRS also significantly improves average delay and number of completed tasks, as compared to current routing algorithms.

Optimizing data transfers is critical for improving job performance in data-parallel frameworks. In the hybrid data center with both wired and wireless links, reconfigurable wireless links can provide additional bandwidth to speed up job execution. However, it requires the scheduler and transceivers to make joint decisions under coupled constraints. In this work, we identify that the joint job scheduling and bandwidth augmentation problem is a complex mixed integer nonlinear problem, which is not solvable by existing optimization methods. To address this bottleneck, we transform it into an equivalent problem based on the coupling of its heuristic bounds, the revised data transfer representation and non-linear constraints decoupling and reformulation, such that the optimal solution can be efficiently acquired by the Branch and Bound method. Based on the proposed method, the performance of job scheduling with and without bandwidth augmentation is studied. Experiments show that the performance gain depends on multiple factors, especially the data size. Compared with existing solutions, our method can averagely reduce the job completion time by up to 10% under the setting of production scenario.

Deterministic networking technologies play significant roles in Internet of Things, Augmented Reality and so on. In fact, the resource reservation policy is employed to guarantee the time-deterministic transmission of flows in deterministic networks. However, for flows with dynamic bandwidth demands, this policy will allocate bandwidths according to the peak volume in rush hours, and the link bandwidths may be just authorized but not fully utilized, which results in bandwidth overprovision and low resource utilization. Besides, due to the time-varying characteristics of both the network topology and link bandwidths, traditional static graph based routing schemes are inefficient for time-varying networks. Therefore, it is important to find a feasible way to improve the bandwidth utilization while guaranteeing the deterministic performance of flows. In this paper, we explore the deterministic routing problem for flows with dynamic bandwidth demands in stochastic time-varying networks (STN). First, we adopt the stochastic time-varying graph to characterize the dynamic attributes of networks, including the link bandwidth and topology, where random variables are utilized to depict the dynamicness of both dynamic bandwidth demands of flows and time-varying bandwidth resources of links. Then, we design an overcommitment rule, which allows us to design a bandwidth resource overcommitment shortest path algorithm in STN (ROSP-STN) to guarantee the delay of flows. Finally, we run numerical experiments to evaluate performance of the ROSP schme. Simulation shows that the proposed ROSP scheme can increase the bandwidth utilization by 24% and the transmission delay of flows can also be guaranteed into a deterministic range.

Deterministic networking technologies play significant roles in Internet of Things, Augmented Reality and so on. In fact, the resource reservation policy is employed to guarantee the time-deterministic transmission of flows in deterministic networks. However, for flows with dynamic bandwidth demands, this policy will allocate bandwidths according to the peak volume in rush hours, and the link bandwidths may be just authorized but not fully utilized, which results in bandwidth overprovision and low resource utilization. Besides, due to the time-varying characteristics of both the network topology and link bandwidths, traditional static graph based routing schemes are inefficient for time-varying networks. Therefore, it is important to find a feasible way to improve the bandwidth utilization while guaranteeing the deterministic performance of flows. In this paper, we explore the deterministic routing problem for flows with dynamic bandwidth demands in stochastic time-varying networks (STN). First, we adopt the stochastic time-varying graph to characterize the dynamic attributes of networks, including the link bandwidth and topology, where random variables are utilized to depict the dynamicness of both dynamic bandwidth demands of flows and time-varying bandwidth resources of links. Then, we design an overcommitment rule, which allows us to design a bandwidth resource overcommitment shortest path algorithm in STN (ROSP-STN) to guarantee the delay of flows. Finally, we run numerical experiments to evaluate performance of the ROSP schme. Simulation shows that the proposed ROSP scheme can increase the bandwidth utilization by 24% and the transmission delay of flows can also be guaranteed into a deterministic range.

Beam training is one of the kernel problems in Millimeter-Wave(mmWave) massive multiple-input multiple-output(MIMO) systems. The beam direction explicitly relies on user location and is implicitly related to channel state information(CSI). Based on this fact, we propose a deep neural network-based novel downlink beam prediction framework to reduce the beam training overhead while achieving higher reliability. Considering that the user location and CSI are two completely different types and dimensions of information, the proposed neural network adopts adjustable feature fusion learning(AFFL) to fuse the two kinds of information. To reduce the beam training overhead, only the user location and the CSI of a minimal number of antennas are taken as the network’s inputs. In addition, when fusing, the signal-to-noise ratio(SNR) is used to adaptively adjust the weights of the two inputs on beam prediction output. Finally, simulation results corroborate that the proposed AFFL-based framework can achieve superior performance and robustness than the strategy which solely uses CSI, especially under low SNR conditions.

Earth observation systems play important roles in many critical applications. The rapid increase of the number of satellites and their sensing capability, however, makes it challenging to send the massive amount of observed data back to the Earth. One promising direction to enhance the earth observation throughput is to use inter-satellite communication. Towards this, we identify two key design factors: 1) the capability to support on-demand scheduling of inter-satellite communication; and 2) the capability to co-optimize the scheduling of observation and transmission missions. For both, rigorous study is needed to determine whether they provide sufficient throughput gain to justify their additional complexity. Our work formulates a generic earth observation and transmission problem to study the maximum network throughput under different settings. By succinctly modeling the different constraints using a generalized time-varying graph representation, we can efficiently find the optimal scheduling solutions. We conduct an extensive study, which shows that using 40 relay satellites from the “starlink” constellation can increase the throughput of 10 sensing satellites from the “Gaofen” constellation by more than 400%. In particular, on-demand scheduling under heavy load and co-optimization of observation/transmission under light but time-critical load can improve the throughput by more than 180% and 100%, respectively.

In this paper, we investigate the maximum flow routing strategy with the service function chain (SFC) constraints in the space information networks (SINs), where a SFC consists of a specific ordered sequence of service functions, and the mission flow must go through these functions in a predefined order. The time-varying SIN is modeled by the time-expanded graph (TEG). We formulate the maximum flow routing strategy problem with the SFC constraints as a linear programming (LP) problem. Furthermore, for a large-scale SIN, as the complexity of solving the LP problem is still very high, we propose a novel low-complexity SFC-constrained graph theory based (SFC-GT) algorithm. Specifically, we formulate this problem as one special single commodity maximum flow problem, where this flow must satisfy the SFC constraints. We first define the SFC-constrained residual network and the SFC-constrained augmenting path. Afterwards, we iteratively search the SFC-constrained augmenting path and update the SFC-constrained residual network. Simulation results demonstrate our proposed SFC-GT algorithm can achieve near-optimal performance with much less complexity.

Nowadays satellite networks are playing an increasing role in earth observation, global communication, etc. Many space missions require to deliver large amounts of data to the ground system for different purposes, and analyzing the maximum throughput of the given satellite network is a prerequisite for efficient data transmission. However, satellite networks possess the time-varying topologies, dynamic bandwidth and limited on-board energy, which restricts the end-to-end capacity and poses challenges to the analysis. In this paper, we utilize temporal graphs for better solving the end-to-end max-flow problem over energy-limited satellite networks. An energy time-expanded graph (eTEG) is constructed to accurately represent the restriction of on-board limited energy on data transmission capability. Furthermore, to maximize flow delivery and energy utilization, we proposed an eTEG-based max-flow routing algorithm with time-dependent residual network update rules. Simulation results are also presented to verify the efficacy of our algorithm.

In this article, we investigate control-aware energy-efficient transmission strategies for wireless control systems with short packets (WCSs), in which remote state estimation error, system stability, transmission energy consumption, and communication packets with finite-length coding are all taken into account. Specifically, we formulate the transmission strategy design problem as a multiobjective optimization problem, which minimizes the remote state estimation error and transmission energy consumption simultaneously under the constraints of system stability and short packet communications. To solve the multiobjective optimization problem, we first introduce a novel objective function that encapsulates two different objective functions into a single one by using weight parameters, and further prove that the solution of the new stochastic optimization problem is a nondominated solution of the original one. Moreover, to solve the new stochastic optimization, we propose a dynamic control-aware energy-efficient transmission (DCET) algorithm that pushes the objective cost close to the optimal with a tradeoff in virtual queue backlogs for constraints. In particular, to tackle the nonconvexity constraint due to short packet communications, we introduce an additional constraint, with which the optimization problem is convex. Finally, simulation results verified the superiority of our proposed transmission strategy as compared with schemes of TDMA and Aloha-RAM multi access.

In this paper, we examine the acquisition of uplink and downlink channel parameters for the massive multiple-input multiple-output (MIMO) networks in the high mobility scene. We firstly formulate the time domain massive MIMO signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including angle, delay, Doppler frequency, and channel gain for each physical scattering path. Then, we fully exploit the angle, delay and Doppler reciprocity between uplink and downlink and reconstruct angles, delays, and Doppler frequencies for the downlink massive channels at the base station. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

The application of non-orthogonal multiple access (NOMA) in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems can enhance spectral efficiency. In this paper, we propose an angle-domain NOMA scheme over the multi-cell mmWave massive MIMO networks. This scheme is optimized through both user scheduling and precoders/decoders design to maximize the system sum rate, where the precoders are decomposed into outer and inner ones. We construct the outer precoders with the help of the users' spatial angle information, i.e., beam signatures, and propose two design strategies for both inner precoders and decoders, i.e., joint optimization of precoders/decoders (JOPD) and cooperative NOMA (C-NOMA). Specifically, in JOPD, the precoders/decoders are obtained through maximizing a nonconvex function subject to the users' quality-of-service (QoS) constraints, where an alternate optimization algorithm based on the constrained concave-convex procedure is proposed for its solutions. In C-NOMA, we adopt interference alignment to cooperatively serve the cell-edge users and achieve simplified yet effective precoders/decoders. Furthermore, we optimize C-NOMA through power allocation. Afterwards, user scheduling algorithms are proposed for both JOPD and C-NOMA. Extensive simulations verify that the proposed schemes exhibit improved performance in terms of both sum rate and users' QoS compared to that of existing mmWave NOMA schemes.

In this paper, we consider a mission-critical control system, where a dynamic plant is monitored by a mobile device (MD), and the monitored signal is transmitted to a remote controller via heterogeneous cellular and Wi-Fi networks. We propose an optimal control-aware machine-to-machine (M2M) transmission strategy for mission-critical control applications, in which control performance is measured by remote estimation error and system stability while limited by bandwidth cost. Specifically, the problem of minimizing estimation error, subject to the constraints of cellular usage costs and system stability, is formulated as an infinite-horizon constrained Markov decision process (CMDP), where the MD has options to transmit through Wi-Fi or cellular, or to stay idle. We solve the problem by utilizing the Lagrange multiplier approach, and prove that the optimal strategy is a randomized mixture of two threshold structure strategies. Furthermore, to estimate the structured optimal strategy, we present an algorithm called simultaneous perturbation stochastic approximation (SPSA), in which the complexity is O(IAI) lower than a non-structured one with IAI being the number of the actions.

This paper proposes a Bayesian downlink channel estimation framework for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to estimate the model parameters of the sparse virtual channel. The expectation maximization (EM) algorithm is employed to reduce complexity. Specifically, the factor graph and the general approximate message passing (GAMP) algorithms are used to compute the desired posterior statistics in the expectation step, so that high-dimensional integrals over the marginal distributions can be avoided. The non-zero supporting vector of the virtual channel is then obtained from channel statistics by a k-means clustering algorithm. After that, the reduced dimensional GAMP-based scheme is designed to make the full use of the channel temporal correlation so as to enhance the virtual channel tracking accuracy. Finally, the efficacy of the proposed framework is demonstrated through simulations.

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.

In this paper, we propose an angle-domain NOMA transmission scheme over multicell massive MIMO systems, where multiple users' signal can be superposed to be served by the same spatially angle-domain beams. Then, we carefully consider the performance degradation resulting from the severe inter-cell interference, and formulate an optimization problem in terms of jointly optimizing precoders and decoders (JOPD) to seek an optimal transmission policy with quality of service (QoS) requirements of both cell-edge users and cell-center users, which consequently is a maximization of a nonconvex function with nonconvex constraints. To solve this challenging problem, we invoke the constrained concave convex procedure (CCCP) method to optimize precoders with fixed decoders, while the decoders can be readily optimized with obtained precoders. Consequently, we propose an alternating optimization algorithm based on CCCP (AoCCCP) to jointly optimize precoders and decoders and then obtain a suboptimal solution of the prime problem. Simulation results verify that the proposed scheme exhibits significant performance gain in terms of sum rate as well as QoS guarantee.

As the typical delay tolerant networks, the satellite networks possess the intermittent connections, the large-scale time delays and the time-varying topologies. Obviously, such features seriously affect the delivery of mission data with certain requirements on the traffic or latency. To decrease the delivery time, existing relay-based contact graph routing (CGR) method selects the earliest reachable path to forward mission data. However, this method cannot guarantee the transmission of a large amount data and wastes the rare contact opportunities. Therefore, in this paper, we focus on the transmission QoS problem of multiple missions over the satellite networks, and design a QoS support routing strategy to achieve multiple flow-maximizing paths with acceptable delivery delays. Especially, with the storage time aggregated graph (STAG), we construct an on-demand mission model to depict both the network dynamic characteristics and the different mission requirements, and then reduce the QoS support problem as a graph-based maximum flow problem. To solve this problem, one STAG-based multiple flow-maximizing routing scheme is proposed to ensure the mission QoS and match the rare network resources, which has low computation complexity. Finally, compared with CGR, simulation results demonstrate the proposed scheme can achieve a higher mission completion rate and resource utilization rate.

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO systems. In this paper, with the help of the virtual channel representation, we apply such property to both time-division duplex and frequency-division duplex systems, where the time-varying channel scenarios are considered. First, we formulate the dynamic massive MIMO channel as one sparse signal model. Then, an expectation maximization-based sparse Bayesian learning framework is developed to learn the model parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch-Tung-Striebel smoother are utilized to track the model parameters of the uplink (UL) spatial sparse channel in the expectation step. During the maximization step, a fixed-point theorem-based algorithm and a low-complex searching method are constructed to recover the temporal varying characteristics and the spatial signatures, respectively. With the angle reciprocity, we recover the downlink (DL) model parameters from the UL ones. After that, the KF with the reduced dimension is adopt to fully exploit the channel temporal correlations to enhance the DL/UL virtual channel tracking accuracy. A monitoring scheme is also designed to detect the change of model parameters and trigger the relearning process. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.

In this paper, the sum rate of opportunistic interference alignment (OIA) is analyzed in multiple-input-multiple-output interfering broadcast channels. The alignment metric upon which users are scheduled is based on the chordal distance between certain interfering subspaces at each receiver, and the closed-form expressions for the rates of the scheduled users are derived. Furthermore, we show that for a system in which each user has$N$receive antennas and the$j$th base station transmits$d_{j}$data streams, where$\sum _{j=1}^{I}d_{j}={N}+1$and${N}\ge 2$, the rate for each user can be approximated by the mean of a Gumbel random variable. Further analysis reveals that if the number of users in cell$i$scales as$\rho ^{\alpha }$, where$\rho $is the normalized transmit power and$\alpha \in [{0,1}]$, then cell$i$can achieve$\alpha d_{i}$degrees of freedom. The simulation results confirm the validity of the theoretical analysis and the accuracy of the approximation. Thus, the sum rate analysis provided herein is an effective performance evaluation method for multi-cell OIA.

With the capability of bidirectional communications on a single frequency band, the full-duplex (FD) operation can potentially double the spectral efficiency in physical layer. In network layer, nevertheless, it may cause severe mutual interference to the system. In this paper, we exploit interference alignment (IA) to address the interference in small cell networks, where some of the base stations simultaneously serve both uplink and downlink users on the same frequency via FD. Under such scenario, we first derive the feasibility condition for IA from Bezout's theorem and find that IA can be feasible only if a certain size constraint of the network is satisfied. On this basis, we then propose two clustering methods, i.e., minimized spectrum consumption clustering (MSCC) and minimized interference leakage clustering (MILC), both of which can perfectly eliminate the intra-cluster interference with IA. The difference between them is that MSCC aims at minimizing the number of clusters through allocating orthogonal resource blocks (RBs) for each cluster to avert inter-cluster interference, while MILC tries to minimize the aggregated inter-cluster interference with all clusters sharing the same RB. Extensive simulations verify that MSCC can achieve higher system sum rate, but MILC works better in terms of spectral efficiency.

Task planning plays a vital role in the application of the earth observation satellites (EOS) as it can effectively reduce the task execution delay and the system energy consumption. However, it is a classic NP-hard problem. In this paper, we propose a graph-based scheduling algorithm to match the to-be-observed tasks with the sensor resources for the multi-satellite multi-task scenario. Specially, we construct a collision avoidance clustering graph (CACG) to model the relationship between tasks and resources, where the concept of collision set is creatively constructed to characterize the conflict relationships among tasks. Through analyzing which nodes can be a clique in the graph, we can get the aggregated tasks. As such a CACG-based algorithm is proposed to achieve satellite task scheduling, where three criteria,i.e.,the N-priority criterion, the T-priority criterion and the collision avoidance rule, are proposed to enhance the percentage of the completed task and decrease the delays. Finally, we demonstrate the effectiveness of the proposed schemes through simulations.

In this paper, we investigate a spatially sparse code multiplexing (SCM) transmission scheme for the massive multiple-input multiple-output (MIMO) networks to enhance the access connectivity. We construct a non- orthogonal transmission policy over both power and angle domains to fully utilize the limited angle-domain degree of freedom (DoF). Firstly, the mapping structure in the angle domain and the detection method are presented. Then, we formulate an optimization problem to seek an optimal transmission policy for the proposed SCM framework, where both the design of the mapping matrix and the power allocation are concerned. To simplify the non-convex problem, we solve the problem with three steps. During the first step, we allocate different angle-domain beams for users to obtain a sparse mapping matrix; during the second step, the prime optimization is transformed as a convex power allocation problem. Finally, we pursue a suboptimal transmission strategy for the multiple clusters with iterative power allocation. Simulation results verify that the SCM scheme exhibits significant performance gain in terms of sum rate.

In this paper, we study the distributed caching scheme in dense small cell networks, that determines which files each SBS should cache locally. In particular, we consider that the appropriate caching policy would produce more Coordinated Multiple Points Joint Transmission (CoMP-JT) opportunities so as to reduce wireless access resource consumption. Our caching scheme minimizes the wireless resource consumption while satisfying the caching capacity limit. Then we formulate the optimal caching problem into a submodular function subject to matroid constraints, which can be solved by an efficient algorithm. Finally we conduct extensive simulations to demonstrate that our proposed distributed caching scheme with considering the impact of CoMP-JT is effective for reducing wireless resource consumption.

OBSS (overlapping basic service set) interference management is a major issue for designing the next generation WLAN with the dense deployment. DSC (dynamic sensitivity control) was proposed to increase the multiple concurrent transmissions to increase throughput, but it would introduce more hidden terminal problems. In this paper, we explore FFR (fractional frequency reuse) to coordinate interference between neighboring BSSs (basic service set). We study how to apply FFR in the existing WLAN system, and present the user identification approach and sub-channel allocation method. Afterwards, based on NS3 simulator platform, we conducted extensive simulations to evaluate the performance of FFR-based interference coordination scheme in WLAN with dense deployment. Our simulation results show that our proposed interference coordination scheme not only can effectively improve system throughput, but also provides fairness for each user.

Small cell network is a promising approach to improve network capacity due to the high link quality and spectrum utility efficiency. Nevertheless, the bandwidth enjoyed by small cell networks is limited by the wireless backhaul capacity and the serious interference from neighbor cells. The backhaul resource needed can be reduced by caching in small cell base stations (SBSs). On the other hand, the inter-cell interference can be mitigated by Coordinated Multi-Point-Joint Transmission (CoMP-JT) which allows multiple SBSs transmit concurrently. Unfortunately, CoMP-JT requires more backhaul resources and caching capacity. This paper jointly considers the transmission mode selection and the caching scheme to minimize wireless backhaul resources consumption. Different from existing works, our work identifies the optimal caching placement while satisfying the spectral efficiency. Our simulation results show that our proposed caching scheme can effectively reduce the backhaul resources consumption.

As a revolutionary wireless transmission strategy, interference alignment (IA) can improve the capacity of cell-edge users. However, the acquisition of the global channel state information for IA leads to unacceptable overhead in the massive MIMO systems. To tackle this problem, in this paper, we propose an IA and soft-space-reuse (IA-SSR)-based cooperative transmission scheme under the two-stage precoding framework. Specifically, the cell-center and the cell-edge users are separately treated to fully exploit the spatial degrees of freedoms. Then, the optimal power allocation policy is developed to maximize the sum-capacity of the network. Next, a low-cost channel estimator is designed for the proposed IA-SSR framework. Some practical issues in IA-SSR implementation are also discussed. Finally, plenty of numerical results are presented to show the efficiency of the proposed algorithm.

Spectrum trading creates more accessing opportunities for secondary users (SUs), and economically benefits the primary users (PUs). Compared with centralized spectrum trading designs, e.g., spectrum auction, distributed spectrum trading captures instantaneous spectrum trading opportunities better over large geographical regions without incurring extra infrastructure deployment and has no network scalability issues. However, the existing distributed spectrum trading designs have limited concern regarding spectrum reuse. Considering spatial reuse, in this paper, we propose a novel distributed frequency reuse-based opportunistic spectrum trading (D-FROST) scheme, which can further improve spectrum utilization, provide more accessing opportunities for SUs, and increase the revenues of PUs. In this paper, we employ conflict graph to characterize the SUs' co-channel and radio interferences, and mathematically formulate a centralized PUs' revenue maximization problem under multiple wireless transmission constraints. Due to the NP-hardness to solve the problem and the non-existence of centralized trading entity, we develop the D-FROST algorithms based on matching with evolving preferences, and prove its stability. Through extensive simulations, we show that the proposed D-FROST algorithm is superior to other distributed spectrum trading algorithms without considering spectrum reuse, yields results close to the centralized optimal one, and is effective in increasing PUs' revenue and improving spectrum utilization.

The multi-commodity flow problem plays an important role in network optimization, routing, and service scheduling. With the network partitioning and the intermittent connectivity, the commodity flows in delay tolerant networks (DTNs) are time-dependent, which is very different from that over the static networks. As an NP-hard problem, existing works can only obtain sub-optimal results on maximizing the multi-commodity flow of dynamic networks. To overcome these bottlenecks, in this paper, we propose a graph-based algorithm to solve the maximum two-commodity flow problem over the DTNs. Through analyzing the relationship between the two commodities, we propose a maximum two-commodity flow theorem to simplify the coupling two-commodity flow problem as the two single-commodity flow ones. Then, with the help of the storage time aggregated graph (STAG) (a DTN model with less memory), we construct a pair of flow graphs to describe the reduced two single-commodity flows (addition flow and subtraction flow), and design the corresponding flow calculation methods. Moreover, we design a STAG-based dynamic combined flow algorithm to maximize the two-commodity flow. Finally, the computational complexity of the proposed algorithm is analyzed, and its efficacy has also been demonstrated through an illustrative example and numerical simulations.

With deployment of small cells, cellular multicast is gaining attractions as it can efficiently deliver large amounts of multimedia data to cellular users. Evolved Multimedia Broadcast Multicast Service (eMBMS) is an efficient broadcast/multicast mechanism to deliver shared content from LTE network to multiple users. It introduces Multimedia Broadcast multicast service Single Frequency Network (MBSFN) for the video sharing among users to guarantee the Quality of Experience (QoE) of users. In MBSFN, each Small cell Base Station (SBS) should determine the appropriate Modulation and Coding Scheme (MCS) for each physical Resource Block (RB), so as to simultaneously maximize the multicast throughput and satisfy the users' experience requirements. However, existing works did not consider the effect of limited wireless backhaul capacity on the multicast throughput. In this paper, we first demonstrate that limited backhaul capacity would affect the system throughput. Then we formulate the problem of MBSFN-based multicast resource allocation with the constraint of limited wireless backhaul capacity. To solve the problem, we develop a near-optimal algorithm. Finally, we conduct extensive simulations to demonstrate that the limited wireless backhaul capacity is important when identifying the optimal multicast resource allocation solution.

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO system. In this paper, we exploit such low-rank property through virtual channel representation (VCR) under the time-varying channel scenario. Firstly, we reformulate the dynamic massive MIMO channel as one sparse signal model through VCR. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to estimate the statistical parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch-Tung-Striebel smoother (RTSS) are applied to track the posterior statistics of the angle domain sparse channel in the expectation step, while a fixed-point theorem based algorithm and a low-complexity searching algorithm are separately developed to recover the temporal varying characteristics and the spatial signatures in the maximization step. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

As one typical delay tolerant network (DTN), the satellite networks possess the intermittent connections, the large-scale time delays and time- varying topologies. Such features seriously affect the delivery of data with certain constraints about the traffic or the latency. In this paper, we design one QoS support routing strategy for the satellite networks to achieve multiple flow-maximize paths with acceptable delivery delays. In order to satisfy the demands of different missions, the storage time aggregated graph (STAG) is utilized to construct an on-demand mission flow model with some temporal constraints. Moreover, the proposed flow model carefully depicts the relation between the time-varying topologies and the features of different missions. Specially, one flow-maximizing routing scheme with shortest path is proposed for matching the rare contacts, which has low computation complexity. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

In addition to comforting passengers' journey, the modern railway system is responsible to support a variety of on-board Internet services to meet the passenger's demands on seamless service provisioning. In order to provide wireless access to the train, one idea attracting increasing attention is to deploy a series of track-side access points (TAPs) with high-speed data rates along the rail lines dedicated to the broadband mobile service provisioning on board. Due to the heavy data traffic flushing into the base stations (BSs) of the cellular networks, TAPs act as a complement to the BSs in data delivery. In this paper, we focus on the TAP association problem for service provisioning in a heterogeneous wireless railway network, where the TAP and BS coexist by applying a queueing game theoretic approach. Specifically, we present comprehensive theoretical analysis of the delay performance on the circumstances of partially observed, totally unobserved, and totally observed state of the system. Moreover, based on the considered payoff model and the derived association delay time, the passenger's equilibrium strategies on association behaviors, i.e., whether to associate with a TAP or not, are studied. Finally, performance evaluations and discussions are provided to illustrate our proposed passenger-TAP association scheme for the heterogeneous wireless railway communication system.

Interference alignment (IA) can remarkably improve the spectral efficiency of dense small cell networks (SCNs) underlaying a macrocell, but its feasibility condition and implementation complexity are restricted by the number of small cell equipments (SUEs). Moreover, the SUEs performing IA may have unsatisfactory quality of service (QoS) requirements as IA only eliminates interference while neglecting the gain of desired signals. In this paper, we propose a centralized efficient subchannel allocation scheme based on IA with similarity clustering in dense SCNs underlaying a macrocell, which aims at maximizing the number of QoS guaranteed SUEs performing IA. The corresponding problem is formulated as a combinatorial optimization problem which is NP-hard. So a low-complexity solution is proposed which includes three phases: similarity clustering for SUEs through graph partitioning, further adjustments of cluster sizes to make IA feasible in each cluster, and subchannel allocation for the formed clusters, each of which is performed with a notably reduced computational complexity. Moreover, the proposed solution greatly reduces the signaling overhead incurred by channel state information estimation. Numerical results show that the proposed solution not only outperforms other related schemes, but also achieves a performance close to the optimal solution.

Vehicular ad hoc network (VANET) is able to facilitate data exchange among vehicles and provides diverse data services. Intuitively, end-to-end backlog and delay bounds are considered significant metrics to evaluate the quality of service in VANETs. In order to analyze how the multi-hop transmission impacts the delay performance, we model the multi-hop service process into a virtualized single service in a min-plus convolution form. To obtain multi-hop end-to-end backlog and delay bound, we consider the stochastic network traffic characteristics and the highly dynamic channel environment under the static priority, first in first out, and earliest deadline first scheduling policies by applying the martingale theory. The IEEE 802.11p enhanced distributed channel access mechanism is also adopted to analyze the access performance in the MAC sub-layer. With three kinds of real wireless data traces, i.e., VoIP, gaming, and UDP, we verify our algorithm by considering the double Nakagami-m fading channel model among vehicles. From the simulation results, we can see that the supermartingale end-to-end backlog and delay bound are remarkably tight to the real simulation results when compared with the existing standard bounds. The effect of the number of vehicles on the highway on the end-to-end backlog and delay performance is also investigated.

A multi-hop multi-AP multi-channel cooperation scheme, referred to as MHACWlan, is proposed for the purpose of improving the efficiency and performance of wireless local area network (WLAN) in dense scenarios, which is the top concern of HEW (High Efficiency WLAN), a IEEE 802.11 study group created in 2013. The proposed MHACWlan provides an effective way to solve the performance anomaly problem, interference problem, and the load imbalance problem in dense WLAN. In MHACWlan, we considers two-hop association to the APs for the wireless STAs with poor single-hop links to improve the throughput, assigning different channels for the two-hop links to reduce the interference and increase the throughput, and associating with more than one AP so as to balance the load. The mathematical analysis and simulation results demonstrate the performance benefits of MHACWlan compared to the traditional WLAN.

Recently, high-speed train is rapidly developed as a popular public transportation to carry passengers and goods with low cost and energy consumption. How to provide passengers's broadband mobile communication services efficiently, such as voice over IP (VoIP) or other multimedia services, is receiving more and more attention nowadays. To fulfill the passenger's demand, we consider a heterogeneous network (HetNet) structure consisting track-side access points (TAPs) and cellular networks for the high-speed rail communication system (HRCS). End-to-end delay is one of the most important quality of service (QoS) indicators to evaluate the HetNet performance. Therefore, this paper investigates the joint end-to-end delay of VoIP and multimedia services in this HetNet architecture. Intermittent connectivity of TAPs and scheduling of multiple on-demand services are considered. In order to obtain the theoretic value of queueing delay bounds, we utilize the martingale theory by analyzing the Markov arrival processes. By combing the arrival-martingale and service- martingale concepts, the theoretic delay bounds under the first in first out (FIFO) scheduling scenario are obtained. For the simulation, we use three kinds of real wireless data traces, VoIP, gaming and UDP to evaluate our algorithm by using Nakagami fading channel and LTE fading channel. From the results we can see that the martingale end-to- end delay bounds are tight to the real data trace simulation results.

In this paper, we study the propagation of deadline- constrained content over wireless cellular network, in which the base station transmits a content to a certain set of users. The lifetime of a content is consumed in two aspects: waiting in the queue and transmitting. In our system, the base station first transmits the content to users with a certain data rate, such that some users may not directly receive the content. Afterwards, the users who obtained the content would forward the content to the other users. Due to the deadline constraint of each content, we formulate the scheduling problem by using Markov Decision Processing (MDP) with the objective of maximizing the throughput of the whole system. We propose an algorithm based on value iteration. Extensive simulation results are provided to demonstrate that our scheduling algorithm can efficiently improve system throughput.

This paper studies the interference management of D2D communications in cellular networks. We consider the underlay D2D communication, such that the signal quality of cellular user would be affected by D2D users. We explore the application of network coding to mitigate interference. In our proposed interference management scheme, the helper nodes overhears the signals from cellular users, and then, code the received packets and sends to the base station. We design the transmission policy for the helper node, and also describe how to select the helper nodes. Our simulation results show that the proposed interference management can effectively improve the spectrum efficiency and increase system throughput.

In this paper, we consider a disruption tolerant network (DTN) which enables data transmission with intermittent connectivity and in which instantaneous end-to-end path between a source and destination may not exist. We explore routing problems in such networks in consideration of limited storage for each intermediate node. A graph model called the storage enhanced time-varying graph (STVG) is presented, which fits the dynamic topology characteristics and time- varying parameters of DTN. We transform the nodes with finite buffer in DTN into links with limited bandwidth in STVG. Then, a Shortest Path Computation with Flow Guaranteed (SPFG) algorithm is proposed to select a path with minimum overall cost (delay), meanwhile, to guarantee the transfer of a certain flow. Simulation results show that the proposed algorithm based on the STVG model can obtain better performance in delay and delivery ratio (which can be improved to more than 90%) as compared with existing routing algorithms without considering the buffer constraints.

The exponential growth of services demands further increase of spectral efficiency which drives the next generation wireless access networks towards deploying femtocells with frequency reuse. However, the interference will be severe especially in dense deployment scenarios. The fair resource allocation problem by joint consideration of sub‐channel assignment and interference alignment (IA) is more complicated than the traditional problem. First, not all the users are appropriate for IA since the number of participant users is limited by the feasibility constraint and the interference power levels are different for the path loss. Second, IA can increase the degrees‐of‐freedoms while occupy additional signal dimensions of participant users, hence more sub‐channels are needed by IA compared with the non‐participants when each user transmits the same number of streams. This study models the fair resource allocation problem as an optimisation problem, which is a non‐deterministic polynomial‐time (NP)‐hard. To solve it with low complexity, the authors propose a graph‐based scheme to give the approximate solution, where the selection criteria of IA group are based on the influence of IA on the interference graph. Simulation results show that the authors scheme can approximate the optimal solution in a small network and improve the fairness in dense deployment scenarios.

This study concerns joint channel and power allocation scheme for multi‐user orthogonal frequency division multiple access system. The author's highlight is margin adaptive (MA) resource allocation problem namely minimising the total transmit power of users with rate requirement constraints. MA is generally provable NP‐hard; the typical methods are either to relax and round, or to fix the transmission mode of users (e.g. modulation and coding). Differently, they reorganise MA problem with only power variables left and design a novel relaxation scheme to enable the convexity. The polynomial‐time algorithm‐interior‐point method‐is employed to solve the relaxation problem and the theoretical complexity is further presented. Simulation results demonstrate that the author's scheme can provide high energy efficiency compared with the existing methods, 100% relative error bounds with respect to the optimum in most cases, and low computational complexity.

In this paper, we examine the channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading scenario, where the distributed space-time coding (DSTC) is adopted at relay nodes. Different from most existing works, our target is to estimate and track the individual channels of each relay hop instead of the composite channels. To reduce the number of the channel parameters to be estimated, we apply the polynomial basis-expansion-model (P-BEM) and convert the problem to estimating the channel coefficient-vectors (called in-BEM-CVs) of each relay hop. With the aid of the autoregressive (AR) model, we formulate the dynamic state space for the in-BEM-CV estimation. Specifically, we adopt the unscented Kalman filter (UKF) to track the in-BEM-CV dynamic variations in an forward manner, and utilize the unscented Rauch-Tung-Striebel smoother (URTSS) to smooth the UKF's estimations in an backward manner. To make the study complete, we also derive Bayesian Cramér lower bounds (BCRBs) for the in-BEM-CV estimation. Finally, numerical results are provided to corroborate the proposed studies.

In this paper, we present a new tracking algorithm based on time-multiplexed-superimposed training (TMST) scheme for the individual channels in amplify-and-forward oneway relay network (OWRN) under time-varying flat fading scenario. Due to the large number of unknowns, we apply the the polynomial basis-expansion-model (P-BEM) to approximate the channel vector of each individual hop by a coefficient-vector with much smaller size, called in-BEM-CV here. Then tracking the individual channel is converted to tracking the corresponding in-BEM-CVs. With the aid of Jakes model, we developed an auto-regressive (AR) process for the in-BEM-CVs and derive the hidden Markov model (HMM) for the in-BEM-CV tracking problem. A particle filtering (PF)-based algorithm to dynamically track the in-BEM-CVs is then designed. Finally, numerical results are presented to evaluate the proposed algorithms.

Energy is the impediment to various applications of battery-powered wireless sensor networks (WSNs). Beyond the battery constraint of sensors/aggregation and forwarding nodes (AFNs), the major energy consumption of WSNs is from the longdistance multi-hop transmissions from the sensors/AFNs to the sink. To address these issues, in this paper, we employ a wireless charging vehicle (WCV) to travel inside WSNs to replenish the energy of sensors/AFNs, and cut long-distance transmissions into short-distance ones. Different from prior works, we let the WCV not only recharge the AFNs selectively, but also collect data from chosen AFNs and bring collected data back to the sink. The chosen AFNs play as virtual sinks, and nearby AFNs can use short-distance transmissions to deliver their traffic to the chosen AFNs. We formulate this problem into an energy replenishment optimization with joint consideration of sensed data delivery, flow routing, wireless power transfer, etc. Since the formulated problem is mixed integer nonlinear programming which is NP-hard to solve, we also develop a heuristic algorithm for feasible solutions. Through simulations, we show that the solution of the proposed algorithm is close to the optimal one and the energy replenishment is optimized while data delivery guaranteed.

In this paper, we examine the pilot-based channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading scenario. Different from most existing works, our target is to estimate and track the individual channel of each relay hop instead of the composite channel. To reduce the number of the channel parameters to be estimated, we apply the polynomial basis-expansion-model (P-BEM) and convert the problem to estimating the channel coefficient-vector (called in-BEM-CV) of each relay hop. With the aid of the autoregressive (AR) model, we formulate the dynamic state space for the pilot-based in-BEM-CV estimation. Specifically, we adopt the unscented Kaiman filter (UKF) to track the in-BEM-CV dynamic variations in an online manner, and utilize the unscented Rauch-Tung-Striebel smoother (URTSS) to smooth the UKF's estimations in an offline manner. Finally, numerical results are provided to corroborate the proposed studies.

In cooperative cognitive radio networks (CCRNs), through cooperating with primary transmissions, secondary users (SUs) could access the spectrum resource when primary users (PUs) are transmitting. The existing schemes in CCRNs allocate the spectrum resource only to the cooperative relay SU. However, this may lead to the waste of spectrum resource, especially when the relay SU has light traffic load or poor channel condition. To better utilize the spectrum among all SUs in a secondary network, we design a spectrum resource utilization maximization scheme with joint consideration of relay selection and spectrum scheduling problems. With the goal to maximize the throughput of the secondary network, our scheme allocates spectrum among all SUs according to the diversity of secondary traffic load and the channel conditions. Besides, considering that the SUs are always energy limited, we also formulate the energy constraint for each SU to avoid the energy consumption exceeding the total available energy. Moreover, we study the resource allocation problem from long-term view under dynamic network setting, and design an online algorithm to solve it. Through extensive simulations, we show that the proposed scheme outperforms the existing schemes in terms of secondary network throughput.

Typical interference avoidance methods in HetNets are arranging orthogonal resources between macrocell and small cells such as in frequency domain in inter-cell interference coordination (ICIC) or time domain in enhanced ICIC (eICIC). However, wireless channels experience time-frequency fading appealing for a two dimensional resource allocation scheme. By optimizing the resource pattern based on variable channel conditions, plus the introduction of group lasso term, our scheme exploits channel variations in both frequency and time domains and as well avoid interference. By grouping the resources in our optimization model, our scheme is flexible and robust against heavy bursty traffic. It is also a lightweight (in terms of coordination overhead) and completely distributed approach, making it suitable for practical implementation. Simulation results show the effectiveness of our proposed method compared with sole frequency domain and time domain approaches.

Interference alignment (IA) is an emerging interference management approach which fully exploits the potential spatial bandwidth in wireless networks, achieving optimal DOF. However, perfect IA is almost unaccessible for 3-user complex constant interference channel without symbol extension. For this, an efficient phase based imperfect interference alignment scheme is proposed in this paper, which uses a deviation compensation factor (DCA) to measure how much the practical channel coefficients deviate from perfect IA feasibility condition. The DCA is allocated to receivers based on a Minimum Interference Leakage (MIL) criterion. Simulation results shows that the MIL based DCA, Max-SINR and TDMA algorithms perform better in different SNR regions, and the proposed scheme can be improved with proper switch between different strategies.

This study analyses the performance of a roadside two‐way relaying system in which a roadside access point (AP) and a vehicle exchange messages with the aid of amplify‐and‐forward mobile relay (MR) based on partial relay selection. It has been shown that AP‐MR‐vehicle communications may experience severer channel fading than conventional cellular communications. Mixed Nakagami ‐m and ‘double’ Nakagami ‐m fading is adopted to provide a realistic description of the involved AP‐MR‐vehicle channels. In this scenario, a tight closed‐form lower bound and high signal‐to‐noise ratio approximate expression for the system outage probability are derived. By applying these results, the authors obtain the diversity and the coding gains and the average symbol error rate for the considered system. In particular, the optimum number of relays is provided, providing valuable guidelines for practical system design. It is shown that when the number of relays is greater than the optimum number, no performance gain would be further achieved. The simulation results highlight the authors theoretical analysis.

In this study, the authors study the downlink resource allocation optimisation in femtocell networks, to maximise the number of guaranteed users whose data rate requirements are fully met. The spectral access of femtocell networks is based on orthogonal frequency division multiple access. In their work, two challenges are solved. The first is the intractable inter‐cell interference coordination brought about by the transmission delay in backhaul connections, and the second is the incorporation of physical interference model into problem formulations. To solve these problems, the authors propose a novel two‐level resource allocation scheme, implemented in both radio resource management controller and femtocell base stations, based on the maximisation of the number of guaranteed users. Notably, their proposed scheme is efficient and requires low overhead. Simulation results show that the proposed scheme offers significant performance improvement in both the percentage of guaranteed users and spectrum spatial reuse over existing methods proposed in the literature.

Cooperative cognitive radio networks (CCRNs) enable secondary users (SUs) to access primary resource by cooperation with active primary users (PUs). For the cooperation-generated resource, existing schemes in CCRNs allocate the resource only to the relay SUs. However, this may lead to inefficient spectrum utilization, when the relay SUs have poor channel condition or little traffic load for their own secondary transmissions. In this paper, considering user diversity in secondary networks, we focus on network-level throughput optimization for secondary networks, by allowing all SUs to optimally share the cooperation-generated period. Besides, considering the energy constraint on SUs, we formulate the resource allocation problem from long-term perspective, to reflect the time-varying change of user diversity in channel condition, traffic load and energy amount. We present an online SUM scheme to solve the long-term optimization problem. Although a mixed-integer and non-convex problem is involved in the SUM scheme, we transform the problem into multiple convex subproblems, and then optimally solve it with low computational complexity. Extensive simulations show that the proposed SUM scheme significantly outperforms the existing schemes.

Channel allocation has become a significant challenge in multi-channel Ad-Hoc networks due to the fast growing of wireless applications and the limitation of spectrum resource. In this paper, we assume that the demand of each user is different and alterable, which has been known by all the others. We study the problem of demand-aware channel allocation (DaChA) from a non-cooperative strategic game theoretic view. We show that the game would converge to a Nash Equilibrium (NE), but the NE may be sub-optimal. To avoid this possible situation, we propose a novel mechanism which assigns a prior channel to each communication link. If deviating from the prior channel, we design a charging scheme which would induce players to converge to a unique NE. We analyze the Pareto-Optimality (PO) of the game and work out that the unique NE must be Pareto optimal. The simulation results demonstrate that the mechanism is efficient.

With the development of multi-interface terminals, a host can connect to the Internet simultaneously by multiple access technologies. Under multi-access technology, a multi-path transmission can obtain high throughput, increased available bandwidth and enhanced reliability. However, the multi-path transmission with multi-access technology also has the problems that the packet re-ordering is unavoidable, and the fast retransmission is unnecessarily requested. Considering the stochastically varying transmission delay, the problems above may eventually result in a degradation of throughput. As a result, in this paper, we focus on the analysis of buffer overflow probability problem which is influenced by the transmission interval. First, we utilize Reinforcement Learning method to estimate the stochastic delay of end-to-end paths. Then, we discuss problems of re-sequencing buffer occupancy distribution and the overflow probability. In this paper, we model the stochastic delay as a continuous random variable, and then, discuss its mean value and variance. Simulation result shows that the re-sequencing buffer overflow probability is influenced by the transmission intervals and the variance of stochastic delay.

Femtocells have emerged as an effective solution to enhance indoor coverage and improve system performance in cellular networks. However, the inter-cell interference (ICI) caused by the unplanned nature of femtocells considerably leads to the degradation in throughput of users with guaranteed performance (GP). Meanwhile, the existing resource allocation algorithms bring about high complexity and large overhead. By tracking channel variation as well as arrival and departure of users, we propose a hybrid centralized/distributed resource allocation algorithm to maximize the number of GP users, with lower complexity and smaller overhead. Simulation results show that the proposed algorithm can significantly improve the system performance compared to the existing algorithms such as Q-FCRA.

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.

The emerging Machine-to-machine (M2M) communications are posing serious challenges to cellular network systems. When a high number of M2M devices try to send data almost simultaneously, the air interface congestion would result in significant quality of service degradation in conventional Humanto- Human (H2H) communications. To solve this problem, an uplink intercell coordination admission control and scheduling scheme (ACSS) in a CDMA multicell environment is proposed in this paper, which makes the best of M2M delay tolerant feature. Delay tolerant M2M connections in the home cell and neighbor cells can be interrupted to release resource for H2H services and reestablished later. The final M2M set to be scheduled is determined by the maximum throughput criterion and residual tolerant delay criterion. Simulation results have shown that with ACSS the H2H blocking probability can be guaranteed and M2M transmission failures can be reduced, achieving better system performance and higher resource utilization.

By enabling opportunistic secondary users' (SUs) usage of licensed spectrum, cognitive radio (CR) technology notably improves spectrum utilization. However, the fundamental multicast throughput optimization problem in cognitive radio networks (CRNs) is still under-explored. Considering spectrum availability and sharing fairness, in this paper, we propose a cross-layer approach to maximize the multicast throughput in multi-hop CRNs. We introduce a new service provider, called secondary service provider (SSP), to harvest the available spectrum and allocate the collected bands among SUs. The SSP also guides the transmissions of multicast CR sessions w.r.t. their contention and spectrum sharing fairness. Leveraging the proposed palmier structure for the multicast session and the multi-radio multi-band multicast (M3) conflict graph, we mathematically characterize the multicast flow routing and link scheduling, respectively. Based on the proportional fairness model, we formulate the multicast maximization problem under multiple cross-layer constraints in CRNs, and provide near-optimal solutions. Through simulations, we show that the performance of the proposed scheme is much better than that of schemes without cross-layer consideration.

The integration of cellular and wireless local area networks (WLAN) has drawn much attention due to their complementary characteristics. Cellular supports wider coverage and better capability to mobile user but with lower bandwidth whereas WLAN have higher bandwidth and lower cost but is effective only in small areas. Based on the combination of users' mobility and network load, this paper proposes an adaptive network selection scheme, called as DMMT (Dynamic Match of Mobility Threshold) scheme. By considering the distribution of the whole users' movement character and arrival rate, we can get the Mobility Threshold (MT) from the solution of optimization problem, which minimize the global network blocking probability. Then we use MT to differentiate mobile users to access different network. To obtain the parameters in the optimization problem, we use the prediction of arrival and mobility characteristics. The heterogeneous networks adjust the MT to adapt to the variation of environment. Simulation results show that the proposed DMMT scheme can reduce call blocking and handoff probabilities compared to the referenced schemes.

Channel assignment is a significant challenge due to scarcity of number of channels available in multi-channel multi-radio Ad Hoc networks. Meanwhile, traffic allocation is a problem worthy of research especially in the situation that different traffic demands for different users. In this paper, we formulate the problem of joint channel and traffic assignment as a static non-cooperative game with charging scheme, taking into account the different individual demand constraints, the number of channels, and the number of radios. To some extent, the charging scheme alleviates the unfairness problem. We show the existence of Nash Equilibrium (NE) and derive the sufficient and necessary conditions to be NE. Then, the uniqueness of the Nash Equilibrium is demonstrated under reasonable conditions. Finally, we extend the algorithm to multiple collision domains. The simulation results illustrates the algorithm is efficient and has a better performance.

In a cognitive radio network, primary service providers (PSPs) set prices for the vacant licensed bands and sell/lease them for pecuniary gains while the secondary service provider (SSP) can buy/rent the bands and support opportunistic spectrum accessing (OSA). However, due to the unpredictable activities of primary services, the SSP may suffer the monetary risk or failure to meet the traffic demands from the secondary users (SUs). It is challenging for the SSP to measure the risk for OSA, to choose the bands to use, and to split the traffic on the band-mix, when there are multiple vacant bands from PSPs. In this paper, we first introduce the X loss, an intuitive measurement for the risk for OSA. Although the X loss is attractively simple, it underestimates the potential risk for OSA and is mathematically not subadditive, which makes it difficult to support the band-mix selection for traffic splitting. To overcome this problem, we propose a more suitable risk measurement, which is subadditive and consistent with the SSP's perception of the risk. Based on the proposed risk metric, we formulate the band-mix selection problem as an optimization problem and solve it by linear programming.

Spectrum auction is an enabling approach to drastically improving the spectrum utilization to satisfy the ever increasing service demands in wireless networks. However, the behaviors of the untrustworthy auctioneer (i.e., the frauds of the untrustworthy auctioneer and the bid-rigging between the greedy bidders and the insincere auctioneer) pose significant design challenges. In this paper, we propose a secure combinatorial spectrum auction (SCSA) by using homomorphic encryption to deal with the untrustworthy auctioneer. SCSA computes and reveals the results of spectrum auction while the actual bidding values are kept confidential. By taking frequency reuse and interference constraints into consideration, we also incorporate a corresponding procedure to implement the combinatorial spectrum auction. It has been shown that SCSA can effectively thwart the back-room dealing without much performance degradation.

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.

In wireless networks, interference between nodes is an important factor which affects the performance of communications. In this paper, we propose an interference avoidance routing protocol (IARP) that chooses a route with fewer collisions and show that the new routing algorithm is stable. The idea is to collect the busyness information about nodes, and use this to determine the next hop based on the backpressure routing algorithm. From the simulation results, we observe that IARP can achieve better performance in terms of network delay than the pure backpressure routing protocol.

Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary service providers (PSPs) set prices for the vacant licensed bands of primary users (PUs) and sell/lease them for pecuniary gains, and the secondary service provider (SSP) can buy/rent the bands and support the secondary users (SUs) for their opportunistic spectrum accessing (OSA) when primary services are not active. However, due to the unpredictable activities of primary services, the SSP may suffer the monetary risk or failure to satisfy the traffic demands from the SUs. It is challenging for the SSP to measure the risk for OSA, to choose the bands to access, and to split the overall traffic on the band-mix, when there are multiple vacant bands and uncertain spectrum supply from PSPs. To address the concerns of the SSP, in this paper, we first introduce the X loss, an intuitive measurement to evaluate the risk for OSA at a given confidence level. Although the X loss is attractively simple, it theoretically underestimate the potential risk for OSA. Meanwhile, the X loss requires strong assumptions to support band-mix selection for traffic splitting, i.e., the primary services of different bands must satisfy normal distribution, which is not necessarily true in practice. To overcome the weakness of the X loss, we further propose a more suitable risk measurement, the expected X loss, which is theoretically subadditive and practically consistent with the SSP's perception of risk for OSA. Based on the proposed measurement, we formulate the band-mix selection problem for traffic splitting into an optimization problem and solve it by linear programming.