As a powerful supplement to the ground network, the LEO satellite network can provide full-time communications with its high coverage, low latency, and the ability to overcome geographic conditions. However, satellites are directly exposed to the space environment, which are susceptible to artificial electromagnetic interference or natural phenomena in space such as sun transit, resulting in link failures and unreliable data transmission. To diminish the influence of electromagnetic interference, an anti-interference on-demand routing algorithm based on load prediction is proposed for LEO satellite networks (LPAO). Firstly, we introduce the adaptive extension area mechanism to control the signal overhead by restricting the RREQ packets’ flooding area, then according to the link communication quality and the estimated load state to determine which link to forward the RREQ packets, while excluding the overloaded and worse communication quality links from the requested area. By evaluating the performance of LPAO in NS3, the results demonstrate that LPAO is superior to other related schemes in terms of packet drop rate, average end-to-end delay, throughput, and traffic distribution index.

The LEO satellite network has received widespread attention because of its low latency, comprehensive coverage, and high capacity. Due to the uneven distribution of traffic, some satellite links are prone to congestion, so a load balancing algorithm is vital for improving network performance. In this paper, a load balancing routing algorithm with traffic preshunting (TPLB) is proposed. Through the traffic pre-shunting mechanism based on load prediction, it defers the congestion time in the network, and when congestion occurs in the satellite network, based on a load balancing mechanism and dynamic public queue mechanism, it alleviates congestion further and reduces packet drop rate. Besides, TPLB can handle the case of inter-plane ISLs failure when the satellite enters the polar region. The simulation results show that TPLB performs better in terms of packet drop rate, end-to-end delay, throughput, and traffic distribution.

In Unmanned Aeronautical Ad hoc Networks (UAANETs), it is challenging for routing protocols such as ad hoc on-demand distance vector (AODV) routing and improved protocols to provide reliable communications due to limited wireless resources and high mobility. This paper proposes a modified AODV protocol applying multi-agent Q-learning(Modified-QLAODV) based on mobility prediction and load sensing. This protocol is a distributed multi-agent reinforcement routing strategy, which employs reinforcement learning algorithm. It introduces mobility prediction and node relative load to infer link state on the purpose of enhancing robustness of UAANETs. In the learning strategy, mobility prediction based on Kalman filter algorithm combined with Gaussian filter algorithm, and load sensing will have impacts on Q-values. The destination obtains routes with different Q-values and returns the one with the maximum average Q-value to the source, which realizes to select the optimal route from a global view. The simulation results demonstrate that Modified-QLAODV can outperform the Q-learning routing protocol(QLAODV) and AODV on network performance in UAANETs.

The demands of video traffic transmission have grown rapidly in recent years. The emergence of converged network alleviates heavy burden on cellular network by introducing broadcasting. The transmission cost of contents with large demands can be effectively reduced. The content distribution strategy compromising between broadcasting and unicasting plays an important role in improving the performance of the converged network. To this end, we formulate the content delivery in a converged network by a game with three players, which are broadcast network service provider, unicast network service provider and a content provider. The cooperation without reward sharing among these players is formulated as a Stackelberg game. In this game, the two service providers are leaders to determine their transmission prices in a competitive way. The content provider is the follower to decide the content distribution strategy. Its Nash Equilibrium is derived consequently. In addition, we investigate the cooperation with reward sharing among the three players by introducing Shapley Value model. According to numerical results, it shows that the proposed Shapley Value based reward sharing scheme can improve the fairness of network cooperation. The gain of content provider using converged strategy over single mode transmission is validated as well.

Multiple Radio Access Technologies (Multi-RAT) make it possible to exploit the advantages of Heterogeneous networks (HetNets) resulting from a joint consideration of the networks as a whole. Users in HetNets can be served with a proper RAT to maximize the system-level utility. Especially, when user dynamics are considered, they can stay in a RAT or handover to another RAT with a transition probability depending on system configuration. By formulating these dynamics as a Markov chain model, the system-level utility is defined as a combinatorial object function. However, the combinatorial optimization is NP-hard, thus we can only use exhaustive search to obtain the optimum solution, which comes up with high computational complexity and is not practical. To this end, we use Markov approximation to obtain the approximate utility and transition probability. In addition, we propose a Count Down and Select (CDS) algorithm to implement the RAT selection. Numerical results validate the convergence of Markov approximation and the effectiveness of the CDS algorithm.

With the development of heterogeneous networks, small cell is introduced into LTE-A networks to satisfy the demand of user equipments (UEs) with various traffic. Due to the existence of small cells and multi-traffic UEs, traditional mobility load balance (MLB) schemes have not been suitable for the current LTE-A network. By reconfiguring the handover thresholds on the basis of cell load, these algorithms are envisioned to cope with the contradiction between large network capacity demand and shortage spectrum resources. However, the existed algorithms are always focusing on the single traffic which are not efficient for the scenario with non-uniform traffic distribution in small cells. In this paper, we propose a multi-traffic load balance (MTLB) algorithm to balance the traffic load and improve the network capacity with an appropriate handover procedure. In this MTLB algorithm, a novel cell selection method will be executed to improve the quality of service (QoS) of UEs. Moreover, the adaptive adjustment method of threshold and TTT (time to triggering) will be introduced to enhance load balance and reduce the call drop rate. Meanwhile, the simulation results strongly verify that the proposed MTLB algorithm can achieve a good resource utilization and the network throughput can also be improved.

Network throughput and packet delay are both important performance metrics in Ad-Hoc networks. While uncorrelated mobility has been proved to be able to increase the network capacity at the cost of delay, the influence of correlated mobility on network performance has not been well studied. In correlated mobility model, nodes in the same group are constrained to lie in a disc area, whose center moves uniformly according to the i.i.d. model; however, it is still an open issue how to evaluate the performance of capacity and delay tradeoff in hybrid Ad-Hoc networks. In this paper, we propose a theoretical framework to examine such issue when relay nodes are under correlated mobility and static nodes are acting as transmitters and receivers. Simulation results shows that our algorithm provides better throughput delay tradeoff than i.i.d. model.

In this paper, we study the resource allocation in a device-to-device (D2D) communication underlaying green cellular network, where the base station (BS) is powered by sustainable energy. Our objective is to enhance the network sustainability and efficiency by introducing power control and cooperative communication. Specifically, we propose optimal power adaptation schemes to maximize the network efficiency under two practical power constraints. We then take the dynamics of the charging and discharging processes of the energy buffer into consideration to ensure the network sustainability. To this end, the energy buffer is modeled as a G/D/1 queue where the input energy has a general distribution. Power allocation schemes are proposed based on the statistics of the energy buffer to enhance the network efficiency and sustainability. Both theoretical analysis and numerical results demonstrate that our proposed power allocation schemes can improve the network throughput drastically while maintaining the network sustainability at a certain level.

The problem of jointly estimating clock skew and offset for wireless sensor networks (WSNs) in a pairwise broadcast synchronization (PBS) protocol is considered. The random part of the delay is supposed to be an exponential random variable. We consider two estimators, i.e., joint maximum-likelihood estimator (JMLE) and generalized ML-like estimator (GMLLE) proposed by Leng and Wu . For both estimators, the corresponding algorithms are explicitly derived and presented. For the GMLLE, the corresponding performance bound based on the reduced set of observations is derived and the optimal value of a user-defined parameter is identified accordingly. At last, analytical results are corroborated by numerical experiments. We observe that: (i) JMLE usually outperforms GMLLE at the cost of larger computational complexity; (ii) JMLE, while achieving the same estimation accuracy as that of the LP method presented in , enjoys significantly lower computational complexity than that of the latter.

Cooperative communication techniques exploit diversity gain to improve the network throughput based on cooperation of multiple data transmissions. In wireless local area networks (WLAN), the whole network throughput can be increased by letting high-rate stations relay traffic from low-rate stations. However, such kind of strategy may lead to a shorter network lifetime due to additional energy consumption. In this paper, we propose a MAC protocol, namely Lifetime Extended Cooperative MAC protocol (LECMAC), to maximize both the network lifetime and throughput. In our MAC protocol, a dynamic score is assigned to each station for evaluating its availability and performance when acting as a relay station. Based on the score of each station, a scoring scheme is designed to determine whether a relay station is needed or not, and which station is the best choice for relaying traffic in the packet transmission. Extensive analysis and simulation results demonstrate that the throughput can be increased significantly with the extended network lifetime by the proposed LECMAC.

Recent advances in green energy technologies have boosted the deployment of green wireless communication networks. Unlike traditional energy sources, green energy, e.g., solar, wind, and hydro, etc, is sustainable and eco-friendly in nature. However, the capacity and availability of green energy highly depend on the weather and location, which may lead to violation of users' Quality of Service (QoS) requirements. In this paper, we jointly consider the power allocation and access point (AP) placement problems in green wireless cooperative communication networks. Specifically, the network scenario includes several cooperative users served by an AP. The objective aims at maximizing the overall throughput by jointly allocating transmitting power and deploying the green APs, i.e., APs powered by green energy, subject to the harvested energy constraint. To this end, a closed form power allocation scheme and an AP placement metric are proposed. Our theoretical analysis shows that the overall throughput can be dramatically improved with our proposed scheme. Moreover, extensive simulation results demonstrate that our scheme approaches the optimal solution and significantly outperforms non-cooperative transmission under a variety of network scenarios.

Energy efficiency is the core issue in wireless sensor network's scheduling algorithm design, which calls up wide attention from researchers. However, few of the previous works notice the constraints on sensors caused by limited buffer size. It challenges the system's meeting deadlines performance. To alleviate this concern, we propose a scheduling policy: the least energy and free storage remaining first, which extends wireless sensor network's lifetime and provides a high real-time service's quality. To make our strategy more practical, we consider the user's non-cooperative behavior out of self-interests. It increases the system's packet lost in buffer overflow and reduces the energy efficiency. Based on a mixed strategy game model, we show that the non-cooperative game converges to an inefficient Nash Equilibrium, where the spectrum resource is significantly wasted. In order to eliminate user's selfish manner, we design a punishment scheme via a repeated game, which considers the time-variant character of network and can detect non-cooperative action precisely and rapidly.

Cognitive radio is promising in improving spectrum efficiency by enabling unlicensed users to access to the licensed spectrum. Spectrum auction is perceived as a potential way to realize it. Primary users (PUs) act as sellers selling unused spectrum bands and secondary users (SUs) act as buyers who intend to get spectrum bands from PUs. In situations where multiple PUs and SUs exist, double auction is a paradigm to assign spectrum. Efficiency and economic robustness are considered two essential properties in the model. Previous work often employ bid-independent uniform pricing to maintain economic robust at the substantial cost of efficiency. In this paper, we investigate the tradeoff between efficiency and robustness. We propose DIPA, a DIscriminatory Pricing double Auction for spectrum, in which bidders are charged of varying prices for the same item they purchase. We demonstrate that DIPA is robust and improves efficiency largely over the previous design.

Vehicular Ad Hoc Networks (VANETs) with base stations are called hybrid VANET, where base stations are deployed to improve the throughput capacity. In this paper, we study the multicast throughput capacity for hybrid wireless VANET with a directional antenna on each vehicle and the end-to-end delay is constrained. In the hybrid VANET, there are n mobile vehicles (or nodes) distributed in a unit area with m strategically deployed base stations connected using high-bandwidth wire links. There are n_s multicast sessions and each multicast session has one source which transmits identical data to its associated p destinations. We investigate the multicast throughput capacity for two mobility models with two mobility scales, respectively, while each vehicular node is equipped with a directional antenna and with a tolerant delay D. That is, a source node transmits to its p destinations only with the help of normal nodes within D consecutive time slots. Otherwise, the transmission will be performed with in the infrastructure mode, i.e., with the help of base stations. We demonstrate that the one dimensional i.i.d. slow mobility pattern catch the main feature of VANETs. And we find that the multicast throughput capacity of the hybrid wireless VANET greatly depends on the delay constraint D, the number of base stations m, and the beamwidth of directional antenna θ. In the order of magnitude, we obtain the closed form of the multicast throughput capacity of the hybrid directional VANET, where the impact of D, m and θ on the multicast throughput capacity is analyzed. Moreover, we derive the lower bound of the muticast throughput using a similar raptor coding approach.

In this paper, we define an ad hoc network where multiple sources transmit packets to one destination as Converge-Cast network. We will study the capacity delay tradeoffs assuming that n wireless nodes are deployed in a unit square. For each session (the session is a dataflow from k different source nodes to 1 destination node), k nodes are randomly selected as active sources and each transmits one packet to a particular destination node, which is also randomly selected. We first consider the stationary case, where capacity is mainly discussed and delay is entirely dependent on the average number of hops. We find that the per-node capacity is Θ (1/√(n log n)) (given nonnegative functions f(n) and g(n): f(n) = O(g(n)) means there exist positive constants c and m such that f(n) ≤ cg(n) for all n ≥ m; f(n)= Ω (g(n)) means there exist positive constants c and m such that f(n) ≥ cg(n) for all n ≥ m; f(n) = Θ (g(n)) means that both f(n) = Ω (g(n)) and f(n) = O(g(n)) hold), which is the same as that of unicast, presented in (Gupta and Kumar, 2000). Then, node mobility is introduced to increase network capacity, for which our study is performed in two steps. The first step is to establish the delay in single-session transmission. We find that the delay is Θ (n log k) under 1-hop strategy, and Θ (n log k/m) under 2-hop redundant strategy, where m denotes the number of replicas for each packet. The second step is to find delay and capacity in multisession transmission. We reveal that the per-node capacity and delay for 2-hop nonredundancy strategy are Θ (1) and Θ (n log k), respectively. The optimal delay is Θ (√(n log k)+k) with redundancy, corresponding to a capacity of Θ (√((1/n log k) + (k/n log k)). Therefore, we obtain that the capacity delay tradeoff satisfies delay/rate ≥ Θ (n log k) for both strategies.

Relay stations are introduced into cellular systems to extend the coverage of the cell, improve the throughput and outage probability of the system. However, present resource-allocation algorithms for traditional cellular system are not directly applicable to the system with relay stations. In this paper, we propose an adaptive resource-allocation algorithm for orthogonal frequency-division multiple-access (OFDMA) cellular system in which relay stations participate in the channel allocation. This algorithm adjusts the frame structure in time domain adaptively according to the channel state and the real-time system throughput. Simulation results show that the proposed algorithm has higher throughput and lower outage probability.

This paper studies the problem of determining locations of Secondary Access Points (SAPs) which belong to two competitive service providers in a certain region. SUs connect to the SAP according to their preferences. A key feature of our modeling approach is that it permits SAPs to set discriminatory powers for SUs, that is, SUs in different locations will be scheduled with different transmission powers. The profit maximizing SAPs compete with each other by setting locations and delivered power schedules to attract SUs, taking into account the impact of the revenue obtained and the power cost incurred. To study this competitive situation, an appropriate extensive form game is defined. The game can be viewed as two-staged. The selection of transmission powers by the SAPs can be investigated for each pair of locations. Then, the problem of location choice can be analyzed, anticipating what transmission powers will be chosen. We make an important observation: the Nash equilibrium of the game exists and the global revenue maximum strategy is a Nash equilibrium strategy. An example demonstrating how to place SAPs in a linear region is presented.

The traditional spectrum auctions require a central auctioneer. Then, the secondary users (SUs) can bid for spectrum in multiple auction or sealed auction way. In this paper, we address the problem of distributed spectrum sharing in the cognitive networks where multiple owners sell their spare bands to multiple SUs. Each SU equips multi-interface/multi-radio, so that SU can buy spare bands from multiple owners. On the other hand, each owner can sell its spare bands to several SUs. There are two questions to be addressed for such an environment: the first one is how to select bands/the owners for each SU; the second one is how to decide the competitive prices for the multiple owners and multiple SUs. We propose a two-sided multi-band market game theoretic framework to jointly consider the benefits of all SUs and owners. The equilibrium concept in such games named core. The outcomes in the core of the game cannot be improved upon by any subset of players. These outcomes correspond exactly to the price-lists that competitively balance the benefits of all SUs and owners. We show that the core in our model is always non-empty. The measurement of price is set to discrete value. Subsequently, the core of the game is defined as discrete core. The Dynamic Multi-band Sharing (DMS) Algorithm is proposed to converge to the discrete core of the game. With small measurement of price, the algorithm can achieve the optimal performance compared with centralized one in terms of total profit of the system.

In this paper we exploit a setting for cognitive radio networks by utilizing cooperation from secondary users (SUs) to assist the transmissions of operators' primary users (PUs). On the other hand, SUs can share the spare spectrum of operators. Such a scenario can be viewed as a market where multiple operators trade their spare spectrum for the assistance of SUs, and multiple SUs trade the transmission energy for access opportunities from operators. We model the system using transferable payoff coalitional game theory. An outcome of a coalitional game is a specification of the coalition that forms and the joint action it takes. We show that the optimum joint action strategy can be obtained as a solution of convex optimization problem. Then, based on dual technique, we show that there is an operating point that maximizes the sum utility over the operators and SUs while providing each player a share such that no subset of operators and SUs has an incentive to break away from the brand coalition.

In this paper, we study the relay assignment problem in cooperative wireless networks with self-interested nodes. Such systems should be organized from the point of view of efficiency, stable and providing consistent incentives to all nodes. We propose a cooperation mechanism which includes the cooperative relationship formation stage and cooperation maintenance stage. The cooperative relationship among the nodes can be modeled as an exchange market game (a special coalitional game) where nodes trade transmission power between each other to get diversity gain. The exchange games have a basic assumption that each node conforms to trade agreement. So that, each agent has the option to trade its good in order to get a better one. In such game, strict core is considered as individual rational, Pareto optimal and relationship-stable solution. A Cooperation Cycle Formation (CCF) algorithm is proposed to get the strict core solution. But, in networks, some deviated nodes may break the cooperation agreement to get more utility gain. Such deviated behaviors in the cooperation cycles can totally destroyed the cooperation relationship. However, the date transmissions in networks have the repeated element (e.g., the data of each user are transmitted in many time slots). Hence, based on the cooperative cycle formed by CCF, we introduce a repeated game model for cooperation maintenance in the second stage. A Dynamic Punishment and Recover (DPR) mechanism is proposed to punish the deviated behaviors and recover cooperation.

Peer-to-peer (P2P) networks are very popular for large-scale data sharing in today's internet. It is naturally envisioned that the file sharing would also be an important application for civil mobile ad hoc networks in the future. The authors propose the cooperative diversity download methods utilising the wireless medium and common file sources in wireless P2P networks. The designed protocols including multi-source opportunistic direct downloading (MODD) and multi-source opportunistic decode-and-forward downloading (MODFD) can adaptively work in different file distribution scenarios. Both of them rely on the cooperation between the sources or receivers and the proposed distributed source-selection schemes. For the situation with one receiver and multi-source, MODD selects ‘best’ source with most favourite channel condition from available m sources. In the situation with multiple receivers and sources, the multiple receivers could form the cooperative pairs to receive the data from the selected source based on decode-and-forward transmission strategy. Compared with MODD, MODFD saves the source-selection time and provides incentives for the cooperation between the peers. A distributed collision solving scheme for the selection schemes is also proposed. The information theoretic analysis of outage probability shows that the proposed downloading methods can both obtain m times diversity gain.

In this paper, we address the incentive-based relay-selection problem over multi-source and multi-relay wireless networks. A two-side market game approach is employed to jointly consider the benefits of all sources and relays. The equilibrium concept in such games is called core. The outcomes in the core of the game cannot be improved upon by any subset of players. These outcomes correspond exactly to the price-lists that competitively balance the benefits of all sources and relays. When the price assumes only discrete values, the core of the game is defined as discrete core. The Distributed Source-Relay Assignment (DSRA) algorithm is proposed for competitive price adjustment and converges to the discrete core of the game. With small enough measurement of price, the algorithm can achieve the optimal performance compared with centralized one in terms of total profit of the system.

Based on the effective contention access and perfect scheduling transmission, an enhanced multiple access protocol - UPMA++ protocol is proposed, which integrates the advantages of random multiple access and fixed allocation multiple access protocols. It allocates the resource dynamically according to the factual traffic requirement, thus reducing the source waste. This protocol adjusts the length of contention access period and polling period effectively to resolve the conflicts between active stations and transmission stations, which active station expects to access the channel quickly and transmission station wants to transmit packet as soon as possible. By analysis, the optimal contention access period length for the given max polling period length is found, and the performance evaluations show UPMA++ protocol has a high throughput (up to 96%), low average message delay and small average message dropping probability.

The effect of mobility on the critical transmitting range in wireless ad hoc networks is discussed in this paper. A new parameter, pause probability P/sub pause/, which is the function of the minimum velocity V/sub min/, the maximum velocity V/sub max/ and the pause time t/sub pause/ of random waypoint (RWP) mobility model, is defined. Simulation results show that the effect of RWP parameter on the critical transmitting range is determined by P/sub pause/ that is, if P/sub pause/ smaller than 0.5, the critical transmitting range of mobile network is about 10 percent larger than that of stationary one, else it equals that of stationary network approximately. If the values of t/sub pause/, V/sub min/ and V/sub max/ are given, their effect on the critical transmitting range is determined since P/sub pause/ is the function of them.

A multiple access control (MAC) protocol - user adaptive scheduling multiple access (UASMA) protocol is proposed in this paper, which can perfectly schedule the packet transmission according to the exact number of active mobile terminals (MTs) determined by self-organizing algorithm, and adjust the number of packets sending by one node in one frame properly. UASMA protocol employs an especial frame structure, which makes it possible to allocate channel resource to uplink and downlink traffic according o their respective service requirements. And the concept of referenced-frame-length is proposed, which can ensure the frame length various in a certain range, and consequently increase the utilization of the channel greatly. Meanwhile, UASMA protocol uses an efficient collision resolution algorithm to guarantee that active MTs can access the channel rapidly. Finally, the performance of UASMA protocol is evaluated by simulation and compared with UPMA protocol, the simulation results show that the former has better performance than the latter.