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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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