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For several years, the completion time and the decoding delay problems in instantly decodable network coding (IDNC) were considered separately and were thought to act completely against each other. Recently, some works have aimed to balance the effects of these two important IDNC metrics, but none of them studied a further optimization of one by controlling the other. This paper investigates the effect of controlling the decoding delay to reduce the completion time below its currently best known solution in both perfect and imperfect feedback with persistent erasure channels. To solve the problem, the decoding-delay-dependent expressions of the users' and overall completion times are derived in the complete feedback scenario. Although using such expressions to find the optimal overall completion time is NP-hard, this paper proposes two novel heuristics that minimize the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. Afterward, this paper extends the study to the imperfect feedback scenario, in which uncertainties at the sender affect its ability to anticipate accurately the decoding delay increase at each user. This paper formulates the problem in such an environment and derives the expression of the minimum increase in the completion time. Simulation results show the performance of the proposed solutions and suggest that both heuristics achieve a lower mean completion time, as compared with the best known heuristics for completion time reduction in perfect and imperfect feedback. The gap in performance becomes more significant as the erasure of the channel increases. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a green spectrum and infrastructure leasing framework for sharing unused TV spectrum, in which the ownership of the network infrastructure and spectrum are decoupled, and each can be leased as a service on demand. An incumbent TV spectrum owner (TSO) can lease a share of the infrastructure from a network infrastructure owner (NIO) with a pay-per-use model, to provide new services to its end users. On the other hand, a TSO can rent a portion of its unused spectrum to the NIO for revenue such that the NIO can obtain access to the spectrum to serve its customers. This proposed framework achieves green communications through both resource sharing and energy saving. Our paper aims to analyze the interplay between TSO and NIO, and create a win–win situation to provide sufficient incentives for them to cooperate on this green communications. A multistage Stackelberg game is formulated, where TSO and NIO sequentially determine the total bandwidth supply for sharing, the infrastructure leasing price, the amount of infrastructure to lease, the spectrum leasing price, and the amount of spectrum to lease to maximize their utilities. The best strategies that will be taken by the TSO and NIO under various deployment scenarios are analyzed. Evaluation results show that the proposed green spectrum and infrastructure leasing framework is a promising scheme under which both TSO and NIO can benefit, and their utilities can be maximized in terms of user data throughput and revenue/payment, which provides sufficient incentives for them to cooperate. [ABSTRACT FROM PUBLISHER]

A method to locate the position of the target vehicle with the aid of infrared signal-direction discrimination for short-range vehicle-to-vehicle communications is proposed in this paper. With the use of two one-dimensional (1-D) signal-direction discriminators mounted on a vehicle with a lateral separation between them to measure the coming directions of the signal emitted from another target vehicle, the position of the target vehicle relative to this detecting vehicle can be located by triangulation. The aforementioned 1-D signal-direction discriminator is composed of two planar receiving modules forming a specific geometric structure, which leads to different responsivities of these individual receiving modules for a signal incident from a definite direction. The coming direction of the signal is determined by comparing the signal strengths received by these individual receiving modules. With a proposed hardware and software implementation, the position of the target vehicle can be accurately determined within a longitudinal range of 50 m. Owing to the restriction of the width of the vehicle, the separation of the signal-direction discriminators mounted on the vehicle cannot exceed 1.8 m. This leads to the consequence that in the remote region any small inaccuracy of the measured signal directions may cause very large position error. However, our measured results reveal a specific characteristic of the distribution of the measured positions for the target vehicle at a definite location. This allows us to design deliberate statistical algorithms to accurately extract the position of the target vehicle in the remote region. Using the simplest statistical algorithm, we are able to locate the position of the target vehicle with acceptable accuracy within a longitudinal range of 80 m. In this paper, we show the feasibility and applicability of this method. Furthermore, improvement is foreseeable with more deliberate design of hardware and software. [ABSTRACT FROM AUTHOR]

Pricing has been widely used to solve network optimization problems. In this paper, we study the case where the network service provider (NSP) has incomplete information about the users, such as the specific user type. Formulating the pricing of the monopolistic NSP and the flow control of the users as a two-level hierarchical game, we first obtain closed-form equilibrium solutions that jointly maximize the benefits of the monopolistic NSP and the users with heterogeneous quality-of-service (QoS) requirements. Then, based on the closed-form solutions, we consider a more general and practical multi-NSP and multiuser model. We further propose a novel one-sided job-hopping matching game to obtain stable NSP-user matching that can optimize network performance in a multi-NSP and multiuser scenario. Simulations demonstrate the effectiveness of the proposed game-theoretic approaches in both the monopolistic and multi-NSP cases. [ABSTRACT FROM AUTHOR]

Caching-enabled device-to-device (D2D) networks have the potential to make mobile users directly fetch requested files from nearby users, resulting in low network delay. In addition, user mobility can increase the communication chances among different users, and therefore, the network delay can be further effectively reduced by proper designing the caching strategy. In this paper, mobility-aware caching strategies in D2D networks are studied to minimize the network delay. Specifically, based on the inter-contact user mobility model, the expression of the average file delivery delay is analytically obtained. Considering the limited cache capacity, a delay minimization cache placement problem considering the user mobility is investigated. To optimally solve this nonlinear integer programming problem, we reformulate it as a multistage decision problem. According to the recursive relationship between adjacent stages, dynamic programming is adopted to obtain the optimal mobility-aware caching strategy stage-by-stage. Furthermore, to lower the complexity, we also demonstrate that the original problem can be recasted as a monotone submodular function maximization problem over a matroid constraint. Then, a low-complexity greedy mobility-aware caching strategy with $(1-1/e)$ -optimality performance guarantee is put forward. Numerical results show that, in the scenario with high user mobility, the file delivery delay can be reduced by 47% with our proposed mobility-aware caching strategy, as compared with the most popular caching. Furthermore, the superiority of the proposed caching strategy is verified by real-world data set. [ABSTRACT FROM AUTHOR]

Recently, millimeter-wave (mmWave) 5G localization has been shown to be to provide centimeter-level accuracy, lending itself to many location-aware applications, e.g., connected autonomous vehicles (CAVs). One assumption usually made in the investigation of localization methods is that the user equipment (UE), i.e., a CAV, and the base station (BS) are time synchronized. In this paper, we remove this assumption and investigate two two-way localization protocols: (i) a round-trip localization protocol (RLP), whereby the BS and UE exchange signals in two rounds of transmission and then localization is achieved using the signal received in the second round; (ii) a collaborative localization protocol (CLP), whereby localization is achieved using the signals received in the two rounds. We derive the position and orientation error bounds applying beamforming at both ends and compare them to the traditional one-way localization. Our results show that mmWave localization is mainly limited by the angular rather than the temporal estimation and that CLP significantly outperforms RLP. Our simulations also show that it is more beneficial to have more antennas at the BS than at the UE. [ABSTRACT FROM AUTHOR]

Security is a critical element to the existing Internet of Things (IoT) deployment, where any user may actively or passively attack the content sharing of others reusing the same channel. As most smart devices are carried by human, we may leverage their owners’ social trust to avoid being intercepted by untrusted users, which conforms to the Social Internet of Things (SIoT) paradigm. In this paper, we propose a secure content sharing (SCS) scheme to strike the trade-off between security and quality of experience (QoE) by exploring the social trust. Firstly, to dynamically extract the social trust, the random walk strategy is employed for prediction based on the proposed “User-Content-Social Group” graph which models users’ preference over time. Given the social trust value, we propose a hierarchical game model to decouple the optimization problem into two sub-problems: user pairing and channel selection. More specifically, the user pairing sub-problem is formulated as a matching sub-game with peer effect, and the embedded rotation-swap matching algorithm can accommodate the dynamics caused by mutual interference. The second sub-problem can be formulated as a secure channel selection sub-game with the directed hypergraph being game space, which is proved to be an exact potential game. Then, we design an uncoupled-user concurrent learning algorithm (UUCL) to search for the optimal pure Nash equilibrium, and thereby the global optimum of this sub-game is achieved. Finally, simulation results generated on realistic social dataset verify that our proposed scheme can notably enhance the security without sacrificing users’ QoE. [ABSTRACT FROM AUTHOR]

In the field of indoor pedestrian navigation (IPN), the orientation information of a pedestrian is often obtained by means of strap-down inertial navigation system (SINS). To deal with the problem of divergence in SINS based orientation estimates, additional orientation sensors, such as a camera, are needed to provide external orientation observations, resulting in increased cost and complexity of system. Although a low-cost magnetometer (or compass) can be used, it is significantly affected by geomagnetic disturbances indoors. Besides, the magnetometer can only give the heading observation which is insufficient to correct orientation errors in all three directions. In this paper, we propose a novel IPN method based on shoe-mounted micro-electro-mechanical systems inertial measurement unit and ultra-wideband. The biggest advantage of this method is able to obtain high-precision position and orientation estimates at low cost. In addition, in the proposed method, the data fusion is implemented by a quaternion Kalman filter which does not involve any complex linearization and hence the system complexity is reduced. Experimental results show that a decimeter level position accuracy is achieved and the orientation drifts can be limited to 0.066 radians in indoor environments. [ABSTRACT FROM AUTHOR]

Forthcoming 5G New Radio (NR) systems will be asked to handle a huge number of devices accessing or delivering “resource-hungry” and high-quality services. In view of this, the new 5G Radio Access Technology (RAT) aims to support, in next releases, Multimedia Broadcast/Multicast Service Single Frequency Network (MBSFN) to enable the simultaneous delivery of the same content to a set of users covered by different cells. According to MBSFN, all cells belonging to the same MBSFN Area are synchronized in time and the MBSFN transmission occurs over the same radio resources. In such a way, the same content flow is delivered by several cells to all the receivers in the MBSFN Area. A further means to enhance the network coverage and provide high data rate and low latency in future 5G-enabled MBSFN networks is Device-to-Device (D2D) connectivity. Along these lines, in this paper we propose a D2D-aided MBSFN Area Formation (D2D-MAF) algorithm to dynamically create MBSFN Areas with the aim to improve the system aggregate data rate while satisfying all user requests. The proposed D2D-MAF foresees that users could receive the service through either MBSFN, or D2D, or unicast transmissions. Performance evaluation results, carried out under a wide range of conditions, testify to the high effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Deploying small cells in cellular networks, as a technique for capacity and coverage enhancement, is an indispensable characteristic of future cellular networks. In this paper, two novel online approaches for enabling energy trading in multitier cellular networks with noncooperative energy-harvesting base stations (BSs) are proposed. The goal is to minimize the nonrenewable energy consumption in a multitier cellular network with an arbitrary number of tiers. In the first approach, a decentralized energy trading framework is established in which BSs are stimulated to compensate their energy shortage with the extra harvested energy from other BSs rather than using the nonrenewable energy. Matching theory is used to assign BSs with energy deficit to the BSs with extra harvested energy. In the second approach, which is centralized, BSs with extra harvested energy and BSs with energy deficit enter a double auction for energy trading. The centralized approach also motivates the BSs with deficient energy to use other BSs extra harvested energy and satisfies a number of properties including truthfulness, individual rationalities, and budget balance. Both approaches achieve Nash equilibrium and motivate noncooperative BSs to share their extra harvested energy. The extra harvested energy is exchanged by the smart grid. We show that the amount of information exchanged in the network to enable BSs to trade energy is reduced in the centralized algorithm compared to the decentralized algorithm at the expense of using a control center. Simulation results verify that the proposed approaches reduce the nonrenewable energy consumption conspicuously. Furthermore, by applying the proposed approaches, BSs gain more profit, and consequently, their utility functions enhance. [ABSTRACT FROM AUTHOR]

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users, as a large number of applications demand for more comprehensive knowledge on primary network behaviors in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship among primary nodes by introducing a Bayesian network (BN)-based framework. How to efficiently learn such a BN structure is a long-standing issue that is not fully understood even in the statistical learning community. To address such an issue in CR, this paper proposes a BN structure learning scheme consisting of a concise directional dependence checking function and a regular BN graph, which achieves significantly lower computational complexity compared with existing approaches. With this result, cognitive users could efficiently understand the statistical behavior patterns in the primary networks, such that more efficient cognitive protocols could be designed across different network layers. [ABSTRACT FROM PUBLISHER]

Mobile crowdsensing systems aim to provide various novel applications by employing pervasive smartphones. A key factor to enable such systems is substantial participation of normal smartphone users, which requires effective incentive mechanisms. In this paper, we investigate incentive mechanisms for online scenarios, where users arrive and interact with a task requester in a random order, and they have preferences (e.g., photographing) or limits (e.g., travel distance) over the sensing tasks. In existing online mechanisms, the task requester has limited power in assigning tasks to the selected users, i.e., it has to pay for all of the tasks specified by the selected users, although some of these tasks are of little value. To accommodate this, we investigate a more flexible setting, where the requester can actively assign most valuable tasks to the selected users. We design two online incentive mechanisms motivated by a sampling-accepting process and weighted maximum matching. We prove that the designed mechanisms achieve computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, and constant competitiveness. By carrying out extensive experiments on two real-world geographical datasets, we demonstrate the practical applicability of the proposed mechanisms. [ABSTRACT FROM PUBLISHER]

With the proliferation of electric vehicles (EVs), private charging pile (PCP) sharing networks are likely to be an integral part of future smart cities, especially in places with limited public charging infrastructure. However, there are a number of operational challenges associated with the deployment of PCPs in such a shared and untrusted environment. For example, how do we formulate efficient PCP sharing strategies in PCP sharing networks, while also taking into consideration the dynamic charging behaviors of EVs? Therefore, in this paper, we propose an energy blockchain-based secure PCP sharing scheme (BBC) for PCP sharing networks. First, an energy blockchain-based framework is designed for PCP sharing networks to facilitate energy sharing services for EVs and PCPs, using both distributed ledgers and cryptocurrency. Then, we devise a reputation-based secure PCP sharing algorithm to improve consensus efficiency with smaller signature sizes. In addition, a distributed reputation mechanism is constructed to assess the trustworthiness of consensus nodes in blockchain, based on ratings, behaviors, and fading. We also model the interactions among EVs and cooperative PCPs as a joint coalition-matching game, and obtain the optimal strategies of PCPs and EVs by analyzing the Nash-stable coalitional structure and stable many-to-one matching pairs. Extensive simulations and the real-world implementation demonstrate that the proposed approach improves the utility of EV users and renewable energy efficiency in PCP sharing networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the secrecy performance of a full-duplex (FD) non-orthogonal multiple access (NOMA) relay system under the presence of multiple non-colluding eavesdroppers over Rayleigh fading channels. Furthermore, to improve legitimate link's capacity, the partial relay selection (PRS) scheme is applied to choose the best relay. To evaluate the secrecy performance, we derive the closed-form expressions of the secure outage probability (SOP) of each user, the overall SOP and the sum effective secrecy throughput (EST) of the considered FD-NOMA relay system. We compare the SOP of the FD-NOMA relay system with that of the half-duplex (HD)-NOMA system and the SOPs of FD-NOMA relay system in the cases that PRS and multiple-relay (MR) schemes are employed at multiple relays. Monte-Carlo simulations verify the correctness of all derived mathematical expressions. Numerical results show that thanks to utilizing FD relays and PRS scheme, the considered FD-NOMA relay system has superior secrecy performance than other systems. Furthermore, increasing the number of FD relays, enhancing their self-interference cancellation capability, and selecting a suitable transmission power help to reduce the SOP of the considered FD-NOMA relay system. Meanwhile, the EST rapidly increases with the equivocation rate of the eavesdropping channel then gradually reaches the saturated maximum value. [ABSTRACT FROM AUTHOR]

In this paper, we consider a unified approach to model wireless channels by the mixture of Gaussian (MoG) distribution. The proposed approach provides an accurate approximation for the envelope and the signal-to-noise ratio (SNR) distributions of wireless fading channels. Simulation results have shown that the proposed model can accurately characterize multipath and composite fading channels. We utilize the well-known expectation–maximization (EM) algorithm to estimate the parameters of the MoG distribution and further utilize the Bayesian information criterion (BIC) to determine the number of mixture components automatically. We employ the Kullback–Leibler (KL) divergence and the mean-square-error (MSE) criteria to demonstrate that the proposed distribution provides both high accuracy and low computational complexity. Additionally, we provide closed-form expressions or approximations for several performance metrics used in wireless communication systems, including the moment generating function (MGF), the raw moments, the amount of fading (AF), the outage probability, the average channel capacity, and the probability of energy detection for cognitive radio (CR). Numerical analysis and Monte Carlo simulation results are presented to corroborate the analytical results. [ABSTRACT FROM PUBLISHER]

Delay-/disruption-tolerant networking is recognized by the National Aeronautics and Space Administration as the only candidate network communication technology that approaches the level of maturity required for deep-space vehicle communications. Extensive performance evaluation and development of efficient data transmission mechanisms for its main protocol, bundle protocol (BP), are needed. In this paper, a new transmission approach based on “proactive” retransmission is proposed for BP for highly efficient data delivery in deep-space communications characterized by a high rate of data loss, long signal propagation delay, and highly asymmetric channel rate, given ample transmission bandwidth. The objective is to ensure successful and reliable deep-space file transfer within a single communication round-trip interval. The expected total number of transmission attempts needed for successful delivery of a file is first calculated based on the available space channel quality and the configured bundle size. Then, the time-out interval for custodial retransmission is set according to the expected number of transmission attempts required, such that all these attempts will be undertaken within the interval of a single round-trip. The performance evaluations based on both theoretical modeling and file transfer experiments indicate that the proposed proactive retransmission approach achieves the objective and has significant performance advantages over the existing reactive retransmission regardless of the transmission conditions. [ABSTRACT FROM AUTHOR]

Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is particularly important because it serves as the predecessor of the full duplex transmission technology, which has been identified as one of the candidate technologies for the 5th-generation (5G) networks. In this paper, we study the performance of the synchronous dynamic TDD from a media access control layer viewpoint, which has been widely adopted in the existing 4G systems. Furthermore, we analyze the coverage probability and the area spectral efficiency in the DL and UL of dense SCNs considering the synchronous dynamic TDD transmissions, and the performance impact of dynamic TDD transmissions on the ASE in the DL and UL of dense SCNs is discussed. Moreover, the performance impact of interference cancellation is also explored. Our analytical results shed new light on the performance of dynamic TDD in future synchronous 5G networks. [ABSTRACT FROM AUTHOR]

Spectrum trading with secondary users (SUs) for monetary gains improves the inefficient spectrum utilization of primary users (PUs) in the cognitive radio networks (CRNs). It is an efficient way of dynamic spectrum sharing that enhances accessing opportunities of SUs and escalates revenue of PUs. In the spectrum trading market, bidding privacy is a serious concern for SUs, since untrustworthy PUs or spectrum traders may leverage bidding information to manipulate the trading or leak SU's bids to other SU competitors for illegal gains. Current centralized privacy preserving spectrum trading designs may incur extra infrastructure deployment, miss many instantaneous spectrum accessing opportunities, and have scalability issues. In this paper, we propose a privacy-preserving semidistributed spectrum trading scheme for multiradio multichannel CRNs, which has joint consideration of spectrum reuse, SUs’ bidding privacy preservation, and PUs’ revenue maximization. We use the conflict graph to characterize SUs’ interference relationships, and jointly employ the Paillier Cryptosystem and dynamic matching to conduct the spectrum trading in a semidistributed manner. Through security, complexity and performance analysis, we show that the proposed scheme 1) effectively preserves the privacy of SUs’ bidding values, and 2) notably increases the PUs’ revenue and improves spectrum utilization with limited overhead. [ABSTRACT FROM AUTHOR]

The sensors in water surface sensor networks are sparsely deployed, which means the distance between sensors is generally larger than the sensors’ communication radius. So, it is difficult for sensors to aggregate their data by multihop routing. If water surface mobile sink is to be used to collect the sensors’ data, its path forming method is a key issue. In this paper, the Voronoi diagram and dominating set theory are introduced, and a path forming method called path forming Voronoi diagram dominating set (PFVD) for water surface mobile sink is proposed. First, the water surface sensor network structure is converted into a Voronoi diagram and the adjacent sensors’ data can be collected at the generated Voronoi vertexes position. Second, the dominating set theory is used to compute the minimum set of Voronoi vertexes that can gather all the sensors’ data. Finally, based on the minimum set, the final path for mobile sink is formed. Theoretical analysis and extensive experiments demonstrate that PFVD has the advantages of short path and balanced energy consumption in different scale networks. [ABSTRACT FROM AUTHOR]

Mobile social networks have greatly strengthened people's online social interactions, generating massive volume of mobile data traffic, and bringing remarkable revenues to the wireless service providers. Meanwhile, the potential revenue growth is restrained by the network capacity of physical communication infrastructure and also challenged by the competition among service providers. In this paper, we study the pricing strategies of multiple service providers in a competitive data service market, where mobile users’ data consumption behaviors are influenced by two effects: the positive network effect and the congestion effect. To analyze the strategic interactions between mobile users and service providers, we devise a two-stage Stackelberg game consisting a pricing game in Stage I and a usage game in Stage II, respectively. In particular, for the usage game, we characterize the equilibrium solution and establish its uniqueness. For the pricing game, our analysis indicates that a mixed-strategy equilibrium solution is guaranteed for the scenario with rational providers as well as the scenario with providers of bounded rationality. We further develop a distributed learning algorithm for finding a mixed-strategy equilibrium solution in the second scenario. Our numerical results provide insights into how positive the network effect and the congestion effect would impact the system performance, and demonstrate that the bounded rational behavior incurs degradation to service providers’ revenues. [ABSTRACT FROM AUTHOR]

Ever-increasing demand for high-speed internet connections motivates cellular operators to upgrade their incumbent networks to the next-generation networks. In this correspondence paper, we study how an operator dynamically manages the budget flow during network upgrade to maximize the final-stage profit. Considering the time discount effect, we propose dynamic deployment policies for the next-generation network that trade off revenues and costs at each stage, and decide the optimal network expansion for the operator. We show that the time discount factor can have opposite effects to boost or hinder the network deployment, which depends on the depreciation of both the revenues and the costs. Furthermore, we extend our model and analysis to a market with more than one operator, where operators under radio access networks (RANs) sharing agreement jointly deploy the next-generation networks, and share the deployment and operational costs. A well-coordinated network upgrade under RAN sharing will speed up the deployment process and lead to a wider next-generation network coverage. [ABSTRACT FROM AUTHOR]

As a response to the challenge of data traffic explosion in wireless networks, content caching in device-to-device (D2D) communication networks has emerged as a promising solution. However, in practical deployment, D2D content caching has its own problems. In particular, not all of the user devices are willing to share the content with others due to numerous concerns, such as security, battery life, and social relationship. In this paper, we consider the factor of social relationship in the deployment of D2D content caching. First, we apply stochastic geometry theory to derive an analytical expression of downloading performance for the D2D caching network. Specifically, a social relationship model with respect to the physical distance is adopted in our analysis to obtain the average download delay performance using random and deterministic caching strategies. Second, to achieve a better performance in more practical and specific scenarios, we develop a socially aware distributed caching strategy based on a decentralized learning automaton, to optimize the cache placement operation in D2D networks. Different from the existing caching schemes, the proposed algorithm not only considers the file request probability and the closeness of devices as measured by their physical distance but also takes into account the social relationship between D2D users. Our simulation results show that the proposed algorithm can converge quickly and outperforms the random and deterministic caching strategies. With these results, our work sheds insights on the design of D2D caching in the practical deployment of 5G networks. [ABSTRACT FROM PUBLISHER]

In view of the emergence and rapid development of the Internet of Vehicles (IoV) and cloud computing, intelligent transport systems are beneficial in terms of enhancing the quality and interactivity of urban transportation services, reducing costs and resource wastage, and improving the traffic management capability. Efficient traffic management relies on the accurate and prompt acquisition as well as diffusion of traffic information. To achieve this, research is mostly focused on optimizing the mobility models and communication performance. However, considering the escalating scale of IoV networks, the interconnection of heterogeneous smart vehicles plays a critical role in enhancing the efficiency of traffic information collection and diffusion. In this paper, we commence by establishing a weighted and undirected graph model for IoV sensing networks and verify its time-invariant complex characteristics relying on a real-world taxi GPS dataset. Moreover, we propose an IoV-aided local traffic information collection architecture, a sink node selection scheme for the information influx, and an optimal traffic information transmission model. Our simulation results and theoretical analysis show the efficiency and feasibility of our proposed models. [ABSTRACT FROM PUBLISHER]

In this paper, an efficient transmission scheme, termed the joint antenna selection and data exchange (AS-DE) scheme, is proposed for a two-way amplify-and-forward relaying network, where two single-antenna source terminals exchange information via a multiantenna relay station. For the proposed scheme, the best antenna at the relay for each source terminal is first selected separately, following the max–max scheme. Then, from the set of the previously selected antennas, either one antenna is selected, in a similar fashion as well-known max–min and max–sum schemes, or two antennas exchange their respective received signals, which are then coded, amplified, and broadcasted to the source and destination terminals. Tight lower and upper bounds on the outage probability (OP) for the proposed scheme have been derived assuming independent and identically distributed Rayleigh fading channels. Furthermore, our analysis reveals that the proposed joint AS-DE scheme can achieve full diversity. Finally, it is shown that under the same resource constraints, i.e., in terms of the number of the utilized time slots and transmit power, the proposed joint AS-DE scheme outperforms the max–min, the max–sum, and the max–max schemes. Extensive numerical results accompanied with computer simulations, are further provided to validate the developed analytical results. [ABSTRACT FROM PUBLISHER]

In contrast to the traditional assumption of the independent and identically distributed fading channels, this paper studies the achievable rate of the multiuser two-way full-duplex (FD) relay system under the independent and nonidentically distributed (i.ni.d.) fading channels, which leads to the different selection probabilities and different weighted rates of different user pairs contributing to the overall system achievable rate as well. To be specific, we first derive the user pair selection probabilities of the max–min scheduling scheme for the i.ni.d. channels. Then, based on the selection probabilities, we solve the hard problem of the weighted rate derivation of each pair. Finally, according to the total probability theorem, the general expressions of the system achievable rate are obtained. In addition, theoretical evaluations and numerical simulations are conducted. The results verify the accuracy of the theoretical analysis and reveal the superiority of the FD mode especially within the low transmitting power regime. [ABSTRACT FROM PUBLISHER]

This paper proposes an efficient (semi-closed-form) zero-forcing (ZF) precoder design for the weighted sum-rate maximization problem under per-antenna power constraint (PAPC). Existing approaches for this problem are based on either interior-point methods that do not favorably scale with the problem size or subgradient methods that are widely known to converge slowly. To address these shortcomings, our proposed method is derived from three elements: minimax duality, alternating optimization (AO), and successive convex approximation (SCA). Specifically, the minimax duality is invoked to transform the considered problem into an equivalent minimax problem, for which we then recruit AO and SCA to find a saddle point, which enables us to take advantages of closed-form expressions and hence achieve fast convergence rate. Moreover, the complexity of the proposed method scales linearly with the number of users, compared to cubically for the standard interior-point methods. We provide an analytical proof for the convergence of the proposed method and numerical results to demonstrate its superior performance over existing approaches. Our proposed method offers a powerful tool to characterize the achievable rate region of ZF schemes under PAPC for massive multiple-input multiple-output. [ABSTRACT FROM PUBLISHER]

Accurate and sufficient location information is the prerequisite for most wireless sensor networks (WSNs) applications. Existing range-based localization approaches often suffer from incomplete and corrupted range measurements. Recently, some matrix completion-based localization approaches have been proposed, which only take into account Gaussian noise and outlier noise when modeling the range measurements. However, in some real-world applications, the inevitable structural noise usually degrades the localization accuracy and prevents the outlier recognition drastically. To address these challenges, we propose a noise-tolerant localization via multi-norms regularized matrix completion (LMRMC) approach in this paper. Leveraging the intrinsic low-rank property of euclidean distance matrix (EDM), the reconstruction problem of true underlying EDM is formulated as a multi-norms regularized matrix completion model, where the outlier noise and structural noise are explicitly sifted by $L_1$-norm and L_{1,2}-norm, respectively, while the Gaussian noise is implicitly smoothed by employing the well-known alternating direction method of multiplier optimization method. To the best of our knowledge, this is the first scheme being able to efficiently recover the unknown range measurements under the coexistence of Gaussian noise, outlier noise, and structural noise. Extensive experiments validate the superiority of our proposed LMRMC approach, outperforming the state-of-the-art localization approaches with regard to the localization accuracy. Besides, LMRMC can also achieve an accurate detection of both outlier noise and structural noise, making it promising for further nodes fault diagnosis and topology control in WSNs. [ABSTRACT FROM AUTHOR]

Cooperative multicast serves as an efficient communication paradigm for supporting multicast-intensive applications in mobile ad hoc networks (MANETs). Available studies on cooperative multicast in MANETs mainly focus on either the full cooperation or the noncooperation, which fail to capture the more general cooperation behaviors among destination nodes. To address this issue, this paper proposes a general cooperative multicast scheme CM( $f,g, p,\tau$) with replication factor $f$, multicast fanout $g$, cooperative probability $p$ , and packet lifetime $\tau$. With this scheme, a packet from source node will be replicated to at most $f$ distinct relay nodes, which forward the packet to its $g$ destination nodes, and with probability $p$ a destination node helps to forward the packet. Here, the packet has the lifetime of $\tau$ time slots. The scheme is flexible and general, and it covers the full cooperation ($p = 1$) and the noncooperation ($p = 0$ ) as special cases. A Markov chain theoretical model is further developed to depict the packet delivery process under the new scheme and help us to conduct analytical study on the corresponding expected packet delivery probability and packet delivery cost. Finally, extensive simulation and numerical results are provided for discussions. [ABSTRACT FROM AUTHOR]

The anticipated explosion in the total data traffic load will impose to mobile network operators (MNOs) the necessity to densify their networks to provide coverage. At the same time, since MNOs plan their networks according to their high-peak traffic load, base station (BS) underutilization during the low traffic hours raises the issue of unnecessary power consumption and excessive cost. In this paper, we plan to study the energy and cost efficiency of a heterogeneous network (HetNet) that is a cooperation result of many MNOs. Each MNO is owner of a HetNet, composed of eNodeBs and small cells and they cooperate by sharing their infrastructure and by switching off a part of it. BS type and traffic load constitute switching off criteria and a roaming-cost-based user association scheme is used to roam traffic to neighboring BSs. We assess the cost alterations created by the possible MNO coalitions and we propose a bankruptcy game to allocate the obtained cost to the cooperative MNOs and to motivate them thus to maintain their sharing agreement instead of following a non-cooperative tactic. The bankruptcy game uses Shapley Value to portray each MNO's contribution to cost savings. The MNOs’ satisfaction from their payoffs (i.e., the allocated cost) and the overall fairness of the method are evaluated. According to the extracted results, the proposed switching off scheme achieves significant improvement of energy efficiency for the studied network, while the proposed bankruptcy game achieves a balanced and satisfactory cost allocation for different MNO traffic loads. [ABSTRACT FROM AUTHOR]

In wireless networks, retransmissions are used by senders to recover lost packets. The conventional retransmission schemes usually retransmit only one lost packet each time, resulting in a low retransmission efficiency. However, using broadcasting nature of a wireless channel with network coding (NC), we can recover more than one lost packet per transmission. Different from single-sender multiple-receiver wireless networks as considered in the existing works, this paper explores the ways to use NC for retransmissions in multiple-sender multiple-receiver scenarios. In addition, this paper also aims to investigate the advantages and shortcomings of using NC in retransmissions. In order to maximize the gain and minimize the impact in NC retransmissions, a probabilistic network coding retransmission (PNCR) scheme is proposed, where a buyer/seller game-theoretic approach is adopted to optimize the performance. The experimental results confirm the effectiveness of the PNCR scheme. [ABSTRACT FROM PUBLISHER]

Edge intelligence is an emerging concept referring to processes in which data are collected and analyzed and insights are delivered close to where the data are captured in a network using a selection of advanced intelligent technologies. As a promising solution to solve the problems of insufficient computing capacity and transmission latency, the edge intelligence-empowered Internet of Vehicles (IoV) is being widely investigated in both academia and industry. However, data sharing security in edge intelligent IoV is a challenge that should be solved with priority. Although attribute-based encryption (ABE) is capable of addressing this challenge, many time-consuming modular exponential operations and bilinear pair operations as well as serial computing cause ABE to have a slow decryption speed. Consequently, it cannot address the response time requirement of edge intelligent IoV. Given this problem, an ABE model with parallel outsourced decryption for edge intelligent IoV, called ABEM-POD, is proposed. It includes a generic parallel outsourced decryption method for ABE based on Spark and MapReduce. This method is applicable to all ABE schemes with a tree access structure and can be applied to edge intelligent IoV. Any ABE scheme based on the proposed model not only supports parallel outsourced decryption but also has the same security as the original scheme. In this paper, ABEM-POD has been applied to three representative ABE schemes, and the experiments show that the proposed ABEM-POD is efficient and easy to use. This approach can significantly improve the speed of outsourced decryption to address the response time requirement for edge intelligent IoV. [ABSTRACT FROM AUTHOR]

This paper proposes a demand response method to reduce the long-term charging cost of single plug-in electric vehicles (PEV) while overcoming obstacles such as the stochastic nature of the user's driving behaviour, traffic condition, energy usage, and energy price. The problem is formulated as a Markov Decision Process (MDP) with an unknown transition probability matrix and solved using deep reinforcement learning (RL) techniques. The proposed method does not require any initial data on the PEV driver's behaviour and shows improvement on learning speed when compared to a pure model-free reinforcement learning method. A combination of model-based and model-free learning methods called Dyna-Q reinforcement learning is utilized in our strategy. Every time a real experience is obtained, the model is updated, and the RL agent will learn from both the real experience and “imagined” experiences from the model. Due to the vast amount of state space, a table-lookup method is impractical, and a value approximation method using deep neural networks is employed for estimating the long-term expected reward of all state-action pairs. An average of historical price and a long short-term memory (LSTM) network are used to predict future price. Simulation results demonstrate the effectiveness of this approach and its ability to reach an optimal policy quicker while avoiding state of charge (SOC) depletion during trips when compared to existing PEV charging schemes. [ABSTRACT FROM AUTHOR]

While the Poisson point process (PPP) has been widely employed to model the user distribution in many network design problems, an existing challenge is that it often reveals inaccuracy in small-cell networks. In this paper, instead of employing PPP, we capture the randomness of user equipment (UE) by collecting many their realizations. Specifically, we focus on the millimeter-wave (mmWave) base station (BS) deployment problem in an urban geometry, based on the application of a scenario sampling approach, previously introduced for large-scale optimization, to quantitatively sample a portion of the UE realizations. Motivated by the scenario sampling, a reduced-scale mmWave BS deployment problem is formulated, whose optimal solution is attained by the proposed low-complexity iterative search algorithm. A required number of samples that guarantee a specified majority of the link quality constraints is analyzed. Simulation results verify the scenario sampling theory and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study an intelligent omni-surface (IOS)-assisted downlink communication system, where the link quality of a mobile user (MU) can be improved with a proper IOS phase shift design. Unlike the intelligent reflecting surface (IRS) in most existing works that only forwards the signals in a reflective way, the IOS is capable to forward the received signals to the MU in either a reflective or a transmissive manner, thereby enhancing the wireless coverage. We formulate an IOS phase shift optimization problem to maximize the downlink spectral efficiency (SE) of the MU. The optimal phase shift of the IOS is analysed, and a branch-and-bound based algorithm is proposed to design the IOS phase shift in a finite set. Simulation results show that the IOS-assisted system can extend the coverage significantly when compared to the IRS-assisted system with only reflective signals. [ABSTRACT FROM AUTHOR]

The transmit time-switching (transmit-TS) approach, under which the energy and information are transferred over different fractions of a time slot has proved its supremacy over the power splitting (PS) approach of simultaneous wireless information and power transfer, where PS splits the power of the received signal for energy harvesting and information decoding. For integrating data and energy transfer, this paper develops new classes of beamforming that are suitable for improper Gaussian signaling which is capable of network throughput improvements while maintaining high computational efficiency in its design. [ABSTRACT FROM AUTHOR]

Most existing trip planning work ignores the issue of planning detailed travel routes between points of interest, leaving the task to online map services or commercial Global Poisioning System (GPS) navigators. However, such a service or navigator cannot meet the diverse requirements of users. Particularity, in this paper, we aim at planning travel routes that not only minimize the distance but provide high-quality sceneries along the way as well. To this end, we propose a novel two-phase framework to plan travel routes efficiently in a large road network considering multiple criteria, i.e., both the quality of the scenic view and the travel distance. In the first phase, we enrich the edges and assign a proper scenic view score for each of them by extracting relevant information from heterogeneous digital footprints of geotagged images and check-ins. In the second phase, on the top of the enriched road network, given users’ trip queries, we employ the concept of skyline operator and propose a memetic algorithm (MA) to discover a set of equally optimal routes with diverse travel distances for users to pick from. Finally, to validate the efficiency and effectiveness of the proposed framework, we conduct extensive experiments on three real-world datasets from the Bay Area in the city of San Francisco, CA, USA, which contain a road network with 3771 nodes and 5940 edges crawled from OpenStreetMap, more than 31 000 geotagged images generated by 1571 Flickr users in one year; and 110 214 check-ins left by 15 680 Foursquare users in six months. Results demonstrate that the MA yields high-quality solutions that are reasonably close to the optima but within desirable computation time and considerably better than the baseline solutions obtained by genetic algorithms. [ABSTRACT FROM PUBLISHER]

This paper investigates the secure communication issue for both static and dynamic cognitive radio networks (CRNs), where multiple nonaltruistic primary users (PUs), secondary users (SUs), and eavesdroppers exist. The design objective is to provide secure communications for PUs and, meanwhile, to ease the starvation of transmission opportunities for SUs. To achieve this goal, we propose a barter-like trading model to incentivize the cooperation among nonaltruistic users. Specifically, PUs leverage the assistance of SUs in the form of cooperative relaying or friendly jamming and, in return, yield certain licensed spectrum accessing time to the aided SUs. We propose a truthful nonmonetary double auction framework (FONDA) toward secure communications for static CRN where PUs and SUs interact in a single round. Then, we extend our framework to d-FONDA for dynamic CRN, where SUs who have patience (tolerant of traffic delay) arrive and leave the network dynamically. We prove that both FONDA and d-FONDA preserve nice economic properties, including truthfulness, individual rationality, and budget balance. Simulation results reveal that the proposed frameworks provide substantial performance gains compared with the baseline scheme and suffer acceptable performance degradation over ideal schemes. [ABSTRACT FROM PUBLISHER]

Applying data buffers at relay nodes significantly improves the outage performance in relay networks, but the performance gain is often at the price of long packet delays. In this paper, a novel relay selection scheme with significantly reduced packet delay is proposed. The outage probability and average packet delay of the proposed scheme under different channel scenarios are analyzed. Simulation results are also given to verify the analysis. The analytical and simulation results show that, compared with non-buffer-aided relay selection schemes, the proposed scheme has not only significant gain in outage performance but also similar average packet delay when the channel signal-to-noise ratio (SNR) is high enough, making it an attractive scheme in practice. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network (CRN) when there exist multiple primary channels. Our work focuses on a scenario in which each secondary user (SU) has the freedom to decide whether to participate in cooperative spectrum sensing; if not, the SU becomes a free rider. Such a mechanism can conserve the energy for spectrum sensing at a risk of sacrificing the spectrum sensing performance. To overcome this problem, we address the following two questions: “Which action (contributing to spectrum sensing or not) should be taken?” and “which channel should be sensed?” We model our framework as an evolutionary game in which each SU makes its decision based on its utility history and takes an action more frequently if it brings a relatively higher utility. We also develop a coalition formation algorithm based on the channel status, where each SU always chooses the coalition that brings the most information regarding the status of the corresponding channel. The simulation results demonstrate that the proposed scheme can guarantee the detection probability at a low false alarm rate. The results also indicate that our algorithm can satisfy different requirements by carefully tuning the system parameters. [ABSTRACT FROM PUBLISHER]

The mobile ticket dispenser system (MTDS) allows customers to remotely draw tickets for service orders anywhere through a mobile handset. In our previous work, the MTDS was applied to a restaurant scenario in which both clerks and customers are patient, i.e., once a mobile ticketing (MT) customer remotely draws the ticket, his request can be served by the clerk when the clerk is available, regardless of when the customer arrives at the restaurant. In this paper, the MTDS is applied to a post office scenario in which the customers are patient, but the clerk is impatient since the original ticket drawn by the MT customer may be invalid if he/she does not arrive at the post office before his/her turn. In this case, the behavior of the MT customer is the same as the so-called in-person ticketing customer who needs to draw a ticket in person when he/she arrives at the service counter. We propose an analytical model to derive the probability that an MT customer misses his/her turn when he/she arrives at the post office. A discrete-event simulation model is developed to investigate the performance of the predicted time adjustment mechanism for the MTDS. We also use real data collected at a post office to observe the queuing behavior. Our study provides guidelines for arranging the time for an MT customer to arrive at the MTDS server. [ABSTRACT FROM PUBLISHER]

With the ongoing development and improvement of deep learning technology, autonomous vehicles (AVs) have made tremendous progress in recent years. Despite its great potential, AV supported by deep learning technology still faces numerous security threats, which prevent AV from being putting into large-scale practice. Aiming at this challenging situation, in this paper, we would like to exploit two attacks against deep learning algorithms in traffic sign recognition system by leveraging particle swarm optimization. Specifically, we first exploit the PAPSO (poisoning attack with particle swarm optimization) which focuses on the training process of the deep learning algorithms in the traffic sign recognition system, i.e., the attacker injects crafted samples into the training dataset, causing a reduction in classification accuracy of the traffic sign recognition system. Then, we also explore the EAPSO (evasion attack with particle swarm optimization) which on the other hand focuses on the interference process of the deep learning algorithms, i.e., the attacker adds some hardly perceptible perturbations to the targeted test sample, leading to a misclassification on it. Extensive experiments are conducted to shed light on the effectiveness of our attacks, and some corresponding defense strategies are also presented. [ABSTRACT FROM AUTHOR]

In this paper, we study the reconfigurable intelligent surface (RIS) based downlink multi-user system where a multi-antenna base station (BS) sends signals to various users assisted by the RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only the large-scale fading gain is required at the BS and a limited number of phase shifts can be realized by the finite-sized RIS. To maximize the sum rate, we propose a hybrid beamforming scheme where the continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively. An iterative algorithm is designed for beamforming and theoretical analysis is provided to evaluate how the size of RIS influences the achievable rate. Simulation results show that the RIS-based system can achieve a good sum-rate performance by setting a reasonable size of RIS and a small number of discrete phase shifts. [ABSTRACT FROM AUTHOR]

In recent years, traffic flow controlling and monitoring have become important issues in intelligent transportation systems (ITSs). For example, vehicle flow management can positively benefit the ITSs from accurate road vehicle detection and counting surveillance systems. In this paper, a high-accuracy vehicle detection mechanism with robust and adaptive vehicle mask training is proposed to effectively detect vehicles in nighttime scenarios. First, a preprocessing step is applied to segment vehicle lamps and filter out reflections. Next, the similarity and symmetric degree of light components are classified into vehicle lamp candidates. A simple fog lamp elimination method is proposed for adapting the proposed method to various road conditions. Then, the mask is trained by a combination of color distribution and weighting information, and it is utilized in the first stage of vehicle detection. The experimental results verify that the proposed method is effective and adaptive for the detection of vehicles at night. [ABSTRACT FROM PUBLISHER]

This paper investigates distributed beamforming (DBF) designs for two-way relay networks (TWRNs), where two terminal nodes exchange information through a set of amplify-and-forward (AF) relays. We assume individual relay power constraints and study two important design problems, namely, the max–min fairness (MMF) problem and the weighted sum-rate maximization (WSRM) problem. For the MMF problem, unlike the existing works that usually solve the problem by a bisection method, we propose an efficient optimal solution by solving one convex second-order cone program (SOCP) only. For the WSRM problem, we develop an efficient iterative algorithm based on SOCP reformulation and the successive convex approximation (SCA) technique. For both the MMF and WSRM designs, we further propose a distributed implementation framework where each of the relays can independently compute its beamforming weight using its local channel state information (CSI) and some common parameters broadcasted by a control center. Simulation results are presented to demonstrate the performance advantages of the proposed solutions. [ABSTRACT FROM PUBLISHER]

The existing indoor localization approaches based on single fingerprints, such as received signal strength (RSS) and channel impulse response, are rather susceptible to the changing environment, multipath, and nonline-of-sight. It is well known that indoor localization can obtain higher positioning accuracy than the single-fingerprint-based methods by fusing multiple information sources (fingerprints/fingerprint functions). However, the existing fusion methods cannot fully exploit the intrinsic complementarity among multiple information sources and thus show lower accuracy. In this paper, we propose an accurate WiFi localization approach by Fusing A Group Of fingerprinTs (WiFi-FAGOT) via a global fusion profile (GFP). WiFi-FAGOT first constructs a WiFi-based GrOup Of Fingerprints (GOOF) in the offline phase, which consists of RSS, signal strength difference, and hyperbolic location fingerprint. Then, instead of direct localization by using the WiFi-based GOOF, we design multiple classifiers by training each fingerprint in the WiFi-based GOOF, namely GOOF classifiers. To fully leverage the intrinsic complementarity among different kinds of fingerprints, we propose a GFP construction algorithm by minimizing the average positioning error over the space of all GOOF classifiers. Finally, in the online phase, we derive a grid-dependent matching algorithm, namely, optimal classifier selection, to intelligently choose a fusion profile in the GFP for more accurate localization. Experimental results demonstrate that WiFi-FAGOT performs better than other systems in real complex indoor environments. [ABSTRACT FROM AUTHOR]

Locally most powerful invariant test (LMPIT) for sphericity of Gaussian vectors has been derived by Ramírez et al. Nevertheless, the decision threshold of the LMPIT is not accurate and its detection performance has not yet been addressed. In this paper, the LMPIT is performed for target detection in multiple-input multiple-output (MIMO) radar, and its theoretical decision threshold as well as detection probability are accurately determined. Utilizing asymptotic expansion approach, we calculate the asymptotic null distribution as a function of central chi-square distributions, resulting in precise closed-form formula for thresholding. On the other hand, the nonnull distribution is approximated by weighted sum of noncentral chi-square distributions and Gamma distribution for close and far hypotheses, respectively. This enables us to derive a closed-form formula to precisely evaluate the detection power of the LMPIT. Numerical results demonstrate that our theoretical computations are very accurate in determining the decision threshold and predicting the behaviors of the LMPIT. Moreover, the superiority of the LMPIT for MIMO radar target detection over state-of-the-art methods is demonstrated for spatially colored but temporarily white noise. [ABSTRACT FROM AUTHOR]

This paper proposes a new scheme to secure the transmissions in an untrusted decode-and-forward (DF) relaying network. A legitimate source node, Alice, sends her data to a legitimate destination node, Bob, with the aid of an untrusted DF relay node, Charlie. To secure the transmissions from Charlie during relaying time slots, each data codeword is secured using a secret-key codeword that has been previously shared between Alice and Bob during the perfectly secured time slots (i.e., when the channel secrecy rate is positive). The secret-key bits exchanged between Alice and Bob are stored in a finite-length buffer and are used to secure data transmission whenever needed. We model the secret-key buffer as a queueing system and analyze its Markov chain. Our numerical results show the gains of our proposed scheme relative to benchmarks. Moreover, the proposed scheme achieves an upper bound on the secure throughput. [ABSTRACT FROM AUTHOR]

Named data networking (NDN) uses the content name instead of its location within the interest message to locate the content within the network. An interest is broadcasted by the content seeking node over the wireless channel with specific content name and waits for the data message containing the actual content. The broadcast region of an interest is restricted by using the time-to-live (TTL) field of the message. Record of every pending interest is stored in the pending interest table (PIT) for a specific duration, called PIT entry lifetime (PEL). The record is purged when a node either receives the data message or the PEL expires. The size of the PIT has a significant impact on the name prefix search during the interest or data message forwarding process. Additionally, the interest and/or data message loss in the vehicular network is very frequent, which results in a significant PIT size depending on the PEL. Vanilla NDN uses the fixed PEL of $4\text{ s}$ , and the very recent work considers the constant parameters to control the PEL, which fails to reduce the PIT size. Hence, in this paper, we propose a hop limit based adaptive PEL (LAPEL) scheme, which adaptively estimates the PEL at interest forwarding vehicle. LAPEL estimates PEL based on the lower layer parameters and has no constant factor that makes it more adaptive than the previously proposed schemes. Simulation results show that LAPEL has $52\%$ less PEL, reduces $8.4\%$ the PIT size, and achieves $4.5\%$ more interest satisfaction ratio, for any simulated TTL and interest rates. [ABSTRACT FROM AUTHOR]

Opportunistic network coding (ONC) has shown high potential in enhancing the quality-of-experience for the clients of cellular networks using their previously downloaded files. In this paper, we study the problem of offloading clients from the macrocell base station (MBS) with the help of femtocaches (FCs) and ONC. We formulate this MBS offloading problem as an optimization problem over an ONC graph, and prove that it is nondeterministic polynomial-time (NP)-hard. Thus, we propose an ONC-broadcast offloading scheme, which utilizes separate ONC graphs at the MBS and FCs in addition to uncoded broadcasting, to offload the clients from the MBS. We analyze the performance of the ONC-broadcast offloading scheme and show that it is asymptotically optimal using random graph theory. Since even this ONC-broadcast offloading scheme is still NP-hard to implement, we devise an efficient heuristic to simplify the implementation. We show that the proposed heuristic reduces the worst case complexity of implementing the ONC-broadcast offloading scheme from an exponential to a quadratic function of the total number of vertices in the FC ONC graph. Simulation results show that, despite its low complexity, the proposed heuristic achieves similar MBS offloading performance to the ONC-broadcast offloading scheme. [ABSTRACT FROM AUTHOR]

Statistical signal transmission (SST) is an emerging orthogonal frequency-division multiplexing (OFDM) technology that can carry additional information over conventional OFDM signal streams without extra bandwidth cost. This additional information can be exploited to carry control information, location/speed data, or cooperative schedule signals, etc., to improve the performance of wireless communications. However, existing detection methods used in this technique work-based mainly on time-invariant channels (i.e., channel state is quasi-static within a detection period), and cannot work well in diverse channel variations caused by different user velocities. In this paper, we propose a mobility adaptive detection method for SST. First, we analyze the influence of nonstatic channels on SST detection and identify a negative factor, called cyclic autocorrelation function whitening effect. Correspondingly, we probe the feasibility of antiwhitening concept and carry out relevant experiments for verification purpose. Based on this, a complete version of mobility adaptive detection method is proposed. Numerical results manifest that the proposed method outperforms the existing ones in high velocity scenarios. In addition, the properties of the proposed method are investigated, based on which we show a specific application for SST to reveal the potential for SST to be used in 5G wireless communications. [ABSTRACT FROM AUTHOR]