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This paper explores the feasibility and scope of co-channel coexistence between a low data rate Orthogonal Frequency Division Multiplexing (OFDM) secondary network and a digital television (DTV) broadcast network operating in the same area. Since DTV receivers do not transmit, neither the presence nor the impact of interference from the secondary network on these DTV receivers can be known. Consequently, conventional spectrum sensing techniques cannot be employed effectively. This paper proposes to compensate the lack of feedback by combining the available information about population distribution in the region - ranging from coarse population density estimates to accurate geospatial maps - with better estimation of interference using a data-driven technique called Ordinary Kriging. Radio Environment Maps constructed using the Kriging method from field-reported observations from the secondary nodes are used to obtain better accuracy than the conventional statistical models. The estimates are then used to compute communication parameters for each secondary link in terms of power, number of active subcarriers, and active time slots, while keeping the average interference in the DTV network within threshold bounds. Thus, the viability of such co-channel license-exempt operation of secondary networks in the DTV black and grey spaces for low data rate applications in densely populated urban areas and higher data rate capabilities for sparsely populated rural areas is established. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communications is an important technology in vehicular ad hoc network (VANET) to support autonomous data exchange among vehicles. Multiple V2V communications modes have been investigated for VANET, including dedicated short-range communications (DSRC) based on IEEE 802.11p and LTE-V2X, which are suitable for different packet transmission cases. In order to fully exploit the strengths of various modes, hybrid V2V communications strategies are designed in this paper, where each vehicle is allowed to choose different modes for different kinds of transmissions in separate frequency bands for transmission throughput improvement. Furthermore, since it is usually impractical to decide all the vehicles’ communications strategies globally due to high computational complexity and heavy overhead to broadcast the decisions, we model the selection of hybrid V2V communications strategies for vehicles as an evolutionary game. A strategy selection algorithm is then proposed, where each vehicle can select its hybrid V2V communications strategy locally based on its evaluation of the payoffs for different strategies and limited signalling from the base station. Simulation results demonstrate that the proposed algorithm can converge to an asymptotic stable state, which can improve the transmission throughput of vehicles, and is robust to the slight evaluation errors of payoffs and the strategy mutations of a few vehicles. [ABSTRACT FROM AUTHOR]

Thanks to their ubiquitous coverage, Long-Term Evolution (LTE) networks are considered the most potential enabler for massive Machine-Type Communications (mMTC) service in fifth-generation (5G) context. LTE standard, however, was not designed for mMTC and scenarios where the massive Machine-Type Devices (MTDs) population try to access a network over a short period may overload the Random Access CHannel (RACH). Furthermore, there is no mechanism to prioritize urgent MTDs in such overload situation. The baseline Access Class Barring (B-ACB) scheme is thus adopted by the 3GPP to address both issues at a substantial cost of access delay. This paper follows a different approach and proposes a complete solution to the two main issues of cellular mMTC. We promote the use of a mechanism called Distributed Queueing (DQ), aided by a MAC-layer load estimation technique, to effectively resolve contentions between the MTDs to improve delay performance with minimal impacts to LTE access procedure and air interface. Then, by exploiting information related to congestion level from the DQ process, a dynamic access prioritization scheme can be realized without additional signaling overhead. Computer simulation under an mMTC-oriented traffic model shows that our framework outperforms the B-ACB in terms of both access delay and energy consumption when all devices are of equal importance. On the other hand, when devices of different priorities coexist, our framework with proper tuning also offers lower delay for all classes and lower overall energy consumption compared to both the baseline and a dynamic ACB solutions in massive bursty access scenarios. [ABSTRACT FROM AUTHOR]

Current regulations leave a few television (TV) white spaces in populated urban areas where spectrum shortage is mostly experienced. As TV set feedback becomes essential in the next generation terrestrial TV standard, an opportunistic TV spectrum sharing based on TV receiver activity information and transmit power control is proposed to exploit the underutilized active TV channels. Based on investigation of the spatial–spectral–temporal characteristics of TV receiver activities, analytical models are developed to capture the spatio-temporal distributions of available spectrum and corresponding capacity. The influence of multiple factors, such as feedback delay, spectrum handover overhead, ranking order, and distribution of TV channel popularity are discussed and modeled. The proposed power control mechanism is verified through experiments at representative campus and residential environments. Empirical data-based simulations and geographic analyses are conducted to evaluate the developed models and further profile the spectrum opportunities within a cell, across New York city (NYC) and other 273 cities in the United States. In NYC, the proposed solution provides a $3.8$ – $11.7$ -fold increase of average spectrum availability, and $2.5$ – $6.6$ -fold increase of capacity from current regulations. By investigating the feasibility and prospects of this approach, this paper intends to motivate further discussions in policy, business, and privacy aspects to reach its significant potential. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of energy consumption management of a large population of plug-in hybrid electric vehicles (PHEVs). A PHEV can be derived either on its gasoline or the battery modes while traveling, and can be either charged from or discharged to the grid while parking at home, respectively. This feature is the main novel consideration in our system model, besides taking into account the constraints like traveling time and distance, and the bounds on the charging power and state of charge of the battery. On the basis of the proposed model of PHEV, the optimal battery-gasoline energy management strategy is obtained in order to minimize the cost of energy and battery degradation for each user. Such a strategy can also compensate the peak load. Since the strategy of each PHEV affects other users’ cost through the electricity price, we model such an interaction as a game problem and we prove that there exist a unique Nash equilibrium point of the game. Then, due to the fact that the effect of a single PHEV of a large population on the other users’ costs is negligible, the theory of mean filed (MF) game is utilized to develop a decentralized control strategy for PHEVs. The lightweight information exchange of the proposed MF algorithm ensures a low computational cost and communication overhead. The proof of convergence for the proposed algorithm is given and it is shown that as the number of PHEVs goes to infinity the MF optimal strategy of the players converges to the unique Nash equilibrium point of the game. In simulation results, the effectiveness of the proposed method in terms of demand side management, peak shaving, convergence rate, and robustness is studied. [ABSTRACT FROM AUTHOR]

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500–2250 r/min) and indicated mean effective pressures (IMEPs) (3–4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $\lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual. [ABSTRACT FROM PUBLISHER]

The application of the cross entropy (CE) method to tone injection (TI) can make the peak-to-average power ratio (PAPR) of the orthogonal frequency division multiplexing (OFDM) signals reduced efficiently with great complexity savings. However, as a population-based search method, the CE method requires large samples to find a high-quality solution, which means higher complexity and more time consuming concerning convergence. To reduce the complexity while achieving promising PAPR performance, an improved CE based TI scheme is proposed in this paper. First, a structured extended constellation for rectangular quadrature amplitude modulation is designed to provide more freedom degrees. With proper parameter selection, the proposed extended constellation can offer more design flexibilities and achieve a better tradeoff between freedom degree and power increase. Then, by analyzing the impact of the subcarrier candidate population on the conventional CE based TI scheme, we derive an explicit expression for the determination of the size of the subcarrier candidate population. And, based on the prior information carried by the clipping signal, proper candidate subcarriers are selected. Finally, an improved CE based TI scheme is proposed, which can efficiently enhance the solution quality and greatly accelerate the convergence speed. Simulation results demonstrate that the improved CE based TI scheme achieves great improvements on the performance of PAPR, bit error rate, power increase and complexity compared with the conventional CE based TI scheme. [ABSTRACT FROM PUBLISHER]

Nodal voltages and feeder currents are acquired by running load flows for electric vehicle (EV) charging purposes. The impact of the charging is analyzed by either visually inspecting the voltages and currents or calculating appropriate impact indices. Optimal charging, on the other hand, is performed by incorporating the voltages and currents in charging strategies. Though the impact indices are frequently used for assessing the impact, their usage for optimizing charging has been minimal. Therefore, this paper develops probabilistic impact indices for nodal voltage violations and feeder overloading, which can be applied not only for investigating the impact but also for optimizing charging. Also, aggregated EV load demand of a large EV population is modeled by using a new combined state of charge (SOC)-based charging strategy in time series, for assessing the applicability of the developed impact indices for practical purposes. The combined SOC dynamically integrates arrival and departure of EV and delivered energy. The numerical results show that the proposed impact indices in conjunction with the SOC-based charging have numerous advantages over the conventional charging strategies aided by the load flows. [ABSTRACT FROM AUTHOR]

Limiting the number of retransmissions in practical random access systems such as S-ALOHA and IEEE 802.11 systems has been used, since it was often considered as a way of preventing the system from being congested, by dropping packets in retransmission or letting users know network congestion after the maximum of retransmissions. Therefore, it can be expected that the smaller the maximum of retransmissions is allowed, the smaller the backlog size gets with a higher packet dropping probability. This paper explores how such a retransmission limit alters the shape of the stability and throughput regions of S-ALOHA systems. To do this, we first consider an S-ALOHA system, where two users with infinite queue length have different packet arrival rate and retransmission probability, which is called asymmetric user. Then, we obtain those results of the systems with $N$ symmetric users. [ABSTRACT FROM PUBLISHER]

Fast-charging stations are connected to the electric grid and can fully charge an electric vehicle (EV) in less than half an hour. The capacity and location of the charging stations bring the costs to the electric grid operator, as well as to the station owner and EV user. A zonal approach has been proposed in this paper to determine the optimal place and capacity of fast-charging stations. Station development cost and the expected costs incurred by the EV user and the grid operator due to EV charging have also been included in the proposed approach. The geographic characteristics associated with the electric substations, urban roads, and city zones have also been considered in the proposed approach. EV user behavior is also considered to determine the expected charging demand and the expected EV user cost. The expected cost associated with extra grid loss due to EV charging has also been calculated by ac power flow using hourly electric grid load scenarios. The problem is formulated as a mixed-integer nonlinear (MINLP) problem to minimize the total expected EV charging costs. The problem is solved using the genetic algorithm technique to determine the optimal location and capacity of charging stations. The proposed approach has been applied to study charging station development for the northwest area of Tehran, Iran, and the robustness of the proposed approach has been evaluated. [ABSTRACT FROM PUBLISHER]

Sensor network localization based on connectivity can be modeled as a nonconvex optimization problem. However, current models only consider the convex constraints, i.e., connections among the nodes. The proposed method considers not only the connection constraints but the disconnection constraints, which are nonconvex in nature, as well. It is argued that the connectivity-based localization problem should be represented as an optimization problem with both convex and nonconvex constraints. In this paper, an algorithm combining a modified differential evolution (DE) algorithm and heuristics is presented for the situation in which the communication range value is unknown. The developed algorithm has a new crossover procedure, with refined procedures to produce a new generation of individuals/candidates. A “single node treatment” procedure is also designed for the search procedure to formulate a new set of coordinate locations to jump out from the local minimum. The final solution can reach the most suitable configuration of the unknown nodes (nodes without knowing their location) because all the information on the constraints has been used. Simulation results have shown that better solutions can be obtained when compared with other convex-constraint methods. The proposed method also gets better results than other general nonconvex optimization methods. [ABSTRACT FROM AUTHOR]

Online social networks have become increasingly popular. One of the interesting applications is the friend locator, in which the application server informs a user through a mobile device if some of his/her listed friends are close enough in terms of geographical locations. However, in such services, it is challenging to protect the privacy of the individual users. Previous solutions for the friend locator do not guarantee a high level of privacy and do not maintain efficiency. In this paper, we propose a dual-resolution system structure to guarantee both strong privacy and efficiency. Additionally, we use the polygon decomposition method to achieve both accuracy and flexibility. To be more specific, in the coarse resolution level, each regular mobile user uploads his/her encrypted coarse location information to a central server periodically for comparisons. If a regular mobile user is found to be in the same grid block as an active mobile user, then the friend locator procedure with a higher resolution level will be conducted. Finally, through numerical analysis and simulations, we show that the proposed system design and algorithm can achieve high privacy, efficiency, accuracy, and flexibility. [ABSTRACT FROM PUBLISHER]

Several studies have shown the importance of realistic micromobility and macromobility modeling in vehicular ad hoc networks (VANETs). At the macroscopic level, most researchers focus on a detailed and accurate description of road topology. However, a key factor often overlooked is a spatiotemporal configuration of vehicular traffic. This factor greatly influences network topology and topology variations. Indeed, vehicle distribution has high spatial and temporal diversity that depends on the time of the day and place attraction. This diversity impacts the quality of radio links and, thus, network topology. In this paper, we propose a new mobility model for vehicular networks in urban and suburban environments. To reproduce realistic network topology and topological changes, the model uses real static and dynamic data on the environment. The data concern particularly the topographic and socioeconomic characteristics of infrastructures and the spatiotemporal population distribution. We validate our model by comparing the simulation results with real data derived from individual displacement survey. We also present statistics on network topology, which show the interest of taking into account the spatiotemporal mobility variation. [ABSTRACT FROM PUBLISHER]

In relay-assisted cellular networks, when and how to cooperate for potential relay nodes is an important issue, particularly from the energy-aware perspective. Moreover, addressing the dynamic cooperative partner-selection problem with incomplete private information is a challenging issue, particularly in the competitive and changing environments. In this paper, we model the energy-aware dynamic competition of cooperative partner selection as an evolutionary game. Specifically, the replicator dynamics are applied to model the adaptation of strategic interactions while considering the dynamic nature in time dependence, and the evolutionarily stable strategy (ESS) is referred to as the robust solution of the proposed game. Moreover, we prove the uniqueness of the resulting ESS, guarantee the convergence to the ESS, and analyze the convergence behavior under the replicator dynamics from different perspectives. In practical terms, we further develop a cooperative partner-selection evolutionary algorithm based on the Q-learning approach, which only requires local knowledge for the potential relay nodes. In addition, we extend our observation by studying the dynamic cooperative partner selection with delay and discuss the properties of the resulting ESS. [ABSTRACT FROM AUTHOR]

In wireless multicast systems, cooperative multicast, in which successful users help to relay received packets to unsuccessful users, has been shown to be effective in combating channel fading and improving system performance. However, this mechanism requires the users' voluntary contributions, which cannot be guaranteed since users are selfish and only care about their own performance. Furthermore, users may have heterogeneous costs (which are their private information) to forward packets, and they may lie about their costs if cheating can improve their utilities. To address these problems, in this paper, we model the interaction among users in the wireless multicast system as a multiseller multibuyer payment-based game, where users pay to receive relay service and get paid if they forward packets to others. A simplified case with homogeneous users that have the same cost to forward packets is investigated first. Then, for the case with heterogeneous users, to encourage users to tell their true costs, we use the second-price sealed-bid auction, which is a truth-telling auction, since bidding the true cost is a weakly dominant strategy. To analyze the multiseller multibuyer payment-based game, we observe that under different selected prices, the game can converge to different equilibria, resulting in different user free-riding probabilities and system throughput. We also study the price selection problem and derive the optimal price that maximizes the system throughput. Simulation results show the effectiveness of our proposed mechanism. [ABSTRACT FROM PUBLISHER]