The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers commonly used for these problems often lack scalability and fall into local optima. Moreover, security concerns about wireless data transfer arising from multi-factory manufacturing processes are garnering attention. To address these issues, this paper introduces an algorithm, TRL, which is a Transformer-based Reinforcement Learning for vehicle production planning problems. Furthermore, we propose a Transformer-based Federated Reinforcement Learning algorithm, named TFRL, tailored for large-scale manufacturing and secure wireless communication. Experimental results showcase the high performance and security of TFRL. It schedules 1000 orders in about 14 seconds and avoids exchanging plaintext during the interaction. Compared to Non-dominated Sorting Genetic Algorithm II(NSGA-II), the TFRL enhances computational speed by 95.12% and reduces constraint violation scores by 93.18%.

Employing ambient backscatter communication (AmBC) technology in Intelligent Transportation Systems (ITS) has emerged as an appealing solution to boost the awareness of crosswalks. However, the AmBC-based ITS is expected to face serious security threats due to the presence of malicious eavesdroppers. In this paper, we investigate the secure multi-antenna transmission in an AmBC-based ITS coexisting with a passive eavesdropper with jamming. Specifically, a cooperative jammer is placed in the system to deliberately disrupt the eavesdropper without affecting the legitimate receiver. In order to characterize the performance of the proposed scheme, new approximate closed-form expressions of secrecy outage probability (SOP) are derived by adopting the Gauss-Chebyshev quadrature. Additionally, the asymptotic behavior of SOP at the high signal-to-noise ratio (SNR) regime is also studied to provide more insights into the system design. We also derive the asymptotic SOP, when the number of transmit antennas tends to infinity. Monte Carlo simulations are provided to demonstrate the validity of our analytical results and to show that 1) the secrecy performance can be significantly improved by allocating part of the transmit power to perform cooperative jamming and 2) the optimal power allocation factor is related to the total transmit power.

The automotive manufacturing industry faces challenges in production planning, but current heuristic algorithms and solvers have limitations in scalability and local optima. Moreover, data security concerns are often overlooked. To address these issues, this paper introduces the FAG-Scheduler, a federated reinforcement learning approach integrating asynchronous advantage actor-critic, gated recurrent unit algorithms, and federated learning. By sharing model parameters instead of raw data, data security is ensured among participants. The FAG-Scheduler achieves optimal solutions in under 5 seconds and demonstrates high adaptability to other manufacturing contexts. It presents potential applications with significant improvements over conventional methods.

Transmission latency presents a significant challenge when operating remote equipment, such as a robotic arm. To address this, we developed a platform and conducted experiments to reduce transmission latency to near-zero levels. These experiments employed Long Short-Term Memory (LSTM) to anticipate future motion trends, leveraging both the controller's movement variables and Electromyography (EMG) data from the operator's arm muscles. Our findings indicate the potential to decrease transmission latency by approximately 500ms. Additionally, our research confirms a direct correlation between prediction accuracy and the brevity of prediction time, suggesting that shorter prediction times yield more accurate results when using EMG. In the context of video transmission for a remotely located robotic arm, we applied video prediction techniques using the Predictive Coding Network (PredNet) to counter network latency. Our results suggest that these predictive methods can effectively compensate for a latency period of 300ms, thereby highlighting their potential for reducing transmission latency in remote robotic operations.

In the rapidly evolving logistics industry, drones are becoming indispensable for automated delivery operations. As drone traffic escalates, formation flying is being explored to enhance operational control and increase drone density, thereby reducing the space they occupy. Drones typically rely on Global Navigation Satellite System (GNSS) positioning information for autonomous flight. However, civilian-grade GNSS devices are susceptible to spoofing via Software Defined Radio (SDR), posing significant challenges. In this study, we introduce a novel approach to detect GNSS spoofing by leveraging the multiple GNSS information available from each drone during formation flight. Our investigations, involving two GNSS receivers spoofed by an SDR, reveal that spoofing results in a calculated distance between two receivers that is smaller than the actual value. Capitalizing on this characteristic, we designed simulations of formation flights involving two and five drones. We also developed a GNSS spoofing detection method using the Long Short-Term Memory (LSTM) network. The performance of our spoof detection method was evaluated using simulation data. The results demonstrate that using multiple GNSS data from drones in formation flight significantly enhances performance, achieving an F1 score of 0.96 or higher. This study underscores the potential of our proposed method in improving the security and reliability of drone operations.

As a promising component of the beyond fifth gener-ation (B5G) and forthcoming sixth generation (6G), massive multi-ple input multiple output (massive MIMO)-based low earth orbit (LEO) communication is facing unprecedented serious doppler frequency shifts and delays due to its fast relative moving speed between the transmitters and receivers, which makes the accurate channel state information (CSI) hard to obtain. In order to solve this problem, we introduce an improved channel prediction method, named Prony-based spatial-delay domain (SDD-Prony) prediction, it not only achieves an accurate CSI acquisition for fast-speed relative motion massive MIMO-based LEO commu-nications, but also greatly reduces the computational complexity. Besides, we find that the prediction error of our method converges to zero when the number of antennas and bandwidth growing large, provided that only two sufficiently accurate channel samples are needed. The validness of our theoretical analysis is verified by numerical results, and the simulations also further demonstrate the effectiveness of our method.

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications, such as group recommender systems. As the distances between people have been greatly shortened, there has been more general demand for the provision of personalized services aimed at groups instead of individuals. The existing methods for capturing group-level preference features from individuals have mostly been established via aggregation and face two challenges: 1) secure data management workflows are absent and 2) implicit preference feedback is ignored. To tackle these current difficulties, this article proposes secure AIoT for implicit group recommendations (SAIoT-GRs). For the hardware module, a secure Internet of Things structure is developed as the bottom support platform. For the software module, a collaborative Bayesian network model and noncooperative game are introduced as algorithms. This secure AIoT architecture is able to maximize the advantages of the two modules. In addition, a large number of experiments are carried out to evaluate the performance of SAIoT-GR in terms of efficiency and robustness.

The Industrial Internet of Things (IIoT), a typical Internet of Things (IoT) application, integrates the global industrial system with other advanced computing, analysis, and sensing technologies through Internet connectivity. Due to the limited storage and computing capacity of edge and IIoT devices, data sensed and collected by these devices are usually stored in the cloud. Encryption is commonly used to ensure privacy and confidentiality of IIoT data. However, the key used for data encryption and decryption is usually directly stored and managed by users or third-party organizations, which has security and privacy implications. To address this potential security and privacy risk, we propose a Shamir threshold cryptography scheme for IIoT data protection using blockchain: STCChain. Specifically, in our solution, the edge gateway uses a symmetric key to encrypt the data uploaded by the IoT device and stores it in the cloud. The symmetric key is protected by a private key generated by the edge gateway. To prevent the loss of the private key and privacy leakage, we use a Shamir secret sharing algorithm to divide the private key, encrypt it, and publish it on the blockchain. We implement a prototype of STCChain using Xuperchain, and the results show that STCChain can effectively prevent attackers from stealing data as well as ensuring the security of the encryption key.

Sparse vector coding (SVC) is emerging as a key enabler for short-packet ultrareliable and low-latency communications (URLLCs), since it displays good block error rate (BLER) performance and can achieve low transmission latency. In this article, we propose an SVC-based sparse superimposed transmission (SVC-ST) coding scheme to further enhance the BLER performance of the SVC scheme. At the encoding side, a portion of transmission bits is represented by nonzero position indices of the sparse vector. The remaining bits are equally split into multiple streams and then mapped into the nonzero positions of sparse vector via quadrature amplitude modulation (QAM) with constellation rotation (CR). We afterward adopt the multipath matching pursuit-based soft decoding (MMP-SD) to recover the transmission packet. The BLER and bit error rate (BER) analyses of the SVC-ST scheme demonstrate the validity and rationality of our study. Moreover, we find from the simulation results that the proposed SVC-ST scheme outperforms SVC and its enhanced version (ESVC) schemes in terms of BLER and latency performance.

Computational social systems (CSSs) focus on topics such as modeling, simulation, analysis, and understanding of social systems from the quantitative and/or computational perspective. “Systems” can be man–man, man–machine, and machine–machine organizations and adversarial situations as well as social media structures and their dynamics[1],[2]. With the advance of the Internet of Things and communication technologies, various kinds of data from diverse areas can be acquired nowadays. As a result, CSSs are becoming ever more complex. Data-driven CSSs aim to conduct pre-competitive research on architectures and design, modeling, and analysis techniques for cyber-physical systems, with emphasis on making full use of big data and artificial intelligence. These applications include transportation systems, automation, security, smart buildings, smart cities, medical systems, energy generation and distribution, water distribution, agriculture, military systems, process control, asset management, and robotics[3],[4],[5]. However, due to the progressive transformation from host-centric networking to information-centric networking, CSSs pose fundamental challenges in multiple aspects, such as heterogeneous data generation, efficient data sensing and collection, real-time data processing, and greater request arrival rates. Thus, there is a great need for a powerful way that can deal with emerging issues in data-driven CSSs more efficiently and effectively in the age of big data.

Deadly diseases and terrorist attacks are greatly threatening human safety, which challenges global security. To address this issue, urban surveillance systems are being applied at a rapid pace with mature but inefficient solutions in large scale networks. When a surveillance network is managing the data generated from multiple edge nodes, it is easy to create congestions due to concentrated data traffic and inefficient data delivery mechanism. In parallel, 5G technology, cope with explosive mobile data traffic growth and massive device connections, can realize a true “Internet of Everything” and build the social and economical digital transformation. In this paper, in the context of 5G technology, we propose an Information-Centric Networking (ICN) surveillance system based on our designed Suspicious Object Network System (SONS) over the concept of next-generation networking. In this solution, the edge nodes in the network distribute the computing and data storage requirements. We first describe the current surveillance issues and our proposed system architecture. Then we use simulation to verify and evaluate the system performance between legacy all-to-one centralized surveillance system and ICN based decentralized surveillance system.

Sparse vector coding (SVC) is emerging as a feasible solution for short packet transmission, which can provide better reliability and lower latency. In this article, a uniquely decomposable constellation group (UDCG)-based SVC called UDCG-SVC is proposed. The important idea of UDCG-SVC is to map the bits into the nonzero elements of the sparse vector by using the sub-constellations in the UDCG. After random spreading, the resource blocks carry the unique decomposable superimposed constellations, which ensures that the minimum Euclidean distance (MED) of the superimposed constellation is large. This results in an improved block error rate (BLER) performance of SVC. Numerical results demonstrate the advanced BLER simulation of UDCG-SVC, and the proposed UDCG-SVC scheme is better than constellation rotation-based SVC (CR-SVC) scheme.

The pandemic of the COVID-19 [1] has reawakened people that viruses are still the greatest threat to human society. Quarantining the patients and tracking close contacts has been used for hundreds of years in the battle between humans and the plague, which are still useful today. In the information society, we can employ information communications technology (ICT) to suppress the spread of epidemics and lower the epidemic curve. By using spatiotemporal information, we can trace the trajectories of patients and their close contacts. However, spatiotemporal information also involves personal privacy, and it has become a topic of concern about whether people's privacy should be sacrificed for epidemic control. In this paper, we propose a close contact tracing solution based on crypto-spatiotemporal information (CSI). First, the solution encrypts spatiotemporal information to protect personal privacy. Then, it uses a blockchain platform to realize the proof of CSI and uses Intel SGX [2] based trusted execution environment [3] to perform close contact judgment. Finally, it can trace close contacts while protecting personal privacy. The evaluation results indicate that the advantages and efficiency of the proposed scheme are significant.

In this paper, we propose a novel two-dimensional (2D) non-stationary geometry-based stochastic model (GBSM) for wideband multiple-input multiple-output (MIMO) base station-to-vehicle (B2V) channels. The proposed model combines one-ring and multiple ellipses with time-variant parameters, which can capture the channel non-stationary characteristics more precisely. The corresponding stochastic simulation model is then developed with finite number of effective scatterers. In addition, the birth-death process is applied to determine the number of ellipses in the proposed model at different time instants. Afterwards, the time-variant parameters and time-variant space cross-correlation functions (CCFs) are derived and analyzed. The impact of different parameters on the space CCFs such as vehicle traffic density (VTD) is investigated. Numerical results illustrate that the simulation model has great agreement with the reference model at different time instants, which indicates the correctness of our derivations.

For the reason of public security, there are many surveillance actives taken places in either open areas or small areas. During an outburst of public eventuality, it is necessary to surveil public individuals. Based on the great number of populations nowadays, it is critical to propose efficient and secured data delivery network for surveillance networks. There are many different methods to surveil and secure an area, such as cameras and radio wave scanners. The modern surveillance networks are usually based on video content deliveries which consumes much network bandwidth and data security performance. Because it needs to efficiently deliver and protect the data generated by different devices. We propose a content-oriented IoT surveillance network, currently under development, which aims to identify dangerous individuals with various sensors and track the individuals through areas. This paper describes the concept of simulated passive imaging and identifying for conceal objects, various sensors association for person tracking and its traffic volume reduction. The data security in the network uses trust verification concept from blockchain technology. There are simulation and experiment to prove the work valid.

In this article, we leverage Network Function Virtualization(NFV) and Multi-Access Edge Computing (MEC) technologies, proposing a system which integrates ICN (Information-Centric Network) with CDN (Content Delivery Network) to provide an efficient content delivery service. The proposed system combines the dynamic CDN slicing concept with the NDN(Named Data Network) based ICN slicing concept to avoid core network congestion. A dynamic CDN slice is deployed to cache content at optimal locations depending on the nature of the content and the geographical distributions of potential viewers. Virtual cache servers, along with supporting virtual transcoders, are placed across a cloud belonging to multiple-administrative domains, forming a CDN slice. The ICN slice is, in turn, used for the regional distribution of content, leveraging the name-based access and the autonomic in-network content caching. This enables the delivery of content from nearby network nodes,avoiding the duplicate transfer of content and also ensuring shorter response times. Our experiments demonstrate that integrated ICN/CDN is better than traditional CDN in almost all aspects, including service scalability, reliability, and quality of service.

Non-orthogonal multiple access (NOMA), millimeter wave (mmWave), and massive multiple-input-multiple-output (MIMO) have been emerging as key technologies for fifth generation mobile communications. However, less studies have been done on combining the three technologies into the converged systems. In addition, how many capacity improvements can be achieved via this combination remains unclear. In this paper, we provide an in-depth capacity analysis for the integrated NOMA-mmWave-massive-MIMO systems. First, a simplified mmWave channel model is introduced by extending the uniform random single-path model with angle of arrival. Afterward, we divide the capacity analysis into the low signal to noise ratio (SNR) and high-SNR regimes based on the dominant factors of signal to interference plus noise ratio. In the noise-dominated low-SNR regime, the capacity analysis is derived by the deterministic equivalent method with the Stieltjes–Shannon transform. In contrast, the statistic and eigenvalue distribution tools are invoked for the capacity analysis in the interference-dominated high-SNR regime. The exact capacity expression and the low-complexity asymptotic capacity expression are derived based on the probability distribution function of the channel eigenvalue. Finally, simulation results validate the theoretical analysis and demonstrate that significant capacity improvements can be achieved by the integrated NOMA-mmWave-massive-MIMO systems.

The non-regenerative massive multi-input-multi-output (MIMO) non-orthogonal multiple access (NOMA) relay systems are introduced in this paper. The NOMA is invoked with a superposition coding technique at the transmitter and successive interference cancellation (SIC) technique at the receiver. In addition, a maximum mean square error-SIC receiver design is adopted. With the aid of deterministic equivalent and matrix analysis tools, a closed-form expression of the signal to interference plus noise ratio (SINR) is derived. To characterize the performance of the considered systems, closed-form expressions of the capacity and sum rate are further obtained based on the derived SINR expression. Insights from the derived analytical results demonstrate that the ratio between the transmitter antenna number and the relay number is a dominate factor of the system performance. Afterward, the correctness of the derived expressions are verified by the Monte Carlo simulations with numerical results. Simulation results also illustrate that: 1) the transmitter antenna, averaged power value, and user number display the positive correlations on the capacity and sum rate performances, whereas the relay number displays a negative correlation on the performance and 2) the combined massive-MIMO-NOMA scheme is capable of achieving higher capacity performance compared with the conventional MIMO-NOMA, relay-assisted NOMA, and massive-MIMO orthogonal multiple access (OMA) scheme.

A Pseudo Double Scattering Channel (PDSC) Matrix assumption is proposed here for the downlink Non- Orthogonal Multiple Access (NOMA) within the massive Multi-Input Multi-Output (MIMO) systems. Afterwards, the outage probability analysis of such a system is investigated. That is, with the aid of random matrix and statistics theories, the Cumulative Probability Distribution (CDF) and also the outage probability performance are addressed. After that, the mathematics derivations obtained here are verified through numerical simulation results, wherein we further find out that with antenna number increasing, the system outage probability performance is reduced.

To further enhance the energy efficiency (EE) performance of fifth generation (5G) Internet of Things systems, an integrated structure is proposed in this paper. That is, other than prior studies that separately study the wireless and wired parts, the wireless and wired parts are holistically combined together to comprehensively optimize the EE of the whole system. The integrated system structure is introduced beforehand with the proposed unified control center components for better deployment of the select-and-sleep mechanism. In addition, in the wireless part, one cellular partition zooming (CPZ) mechanism is proposed. In contrast, in the wired part, a precaching mechanism is introduced. With these proposals, the proposed system EE performance is investigated. Comprehensive computer-based simulation results demonstrate that the proposed schemes display better EE performance. This is due to the fact that system power consumption is further reduced with these schemes as compared to the prior work.

In order to provide high-quality and highly reliable video delivery services for mobile users, especially train passengers, we propose a proactive content caching scheme that uses transportation systems. In our system, we place content servers with cache capability [e.g., content centric networking/named data networking (CCN/NDN)] in every train and station. Video segments encapsulated by MPEG-Dynamic Adaptive Streaming over HTTP (MPEG-DASH) are distributed and pre-cached by the station servers before the trains arrive at the stations. The trains receive content via high-speed wireless transport, such as wireless LANs or millimeter waves, when they stop at the stations. We developed prototype systems based on hypertext transfer protocol and CCN/NDN protocol, evaluate their performance through two field experiments that uses actual trains, and compare with traditional video streaming over cellular networks. Such evaluations indicate that our system can achieve high-quality video delivery without interruption for up to 50 users simultaneously.

In order to address the energy flaw and enhance the energy efficiency of Information Centric Networking (ICN) Architecture, this research proposes an innovated optimal ICN model with adaptive power consumption ability based on the popularity of the content to adapt content router link rate to its real-time utilization for optimizing the ICN router operating power value and based on the interest traffic to the server to select the optimal working mode for the server/content provider. With this dynamic control/adaptive mechanism, the proposed architecture can provide higher reliability with dynamic energy efficiency consumption and better cost-effectiveness for the future ICN infrastructure system to reduce electricity usage, cost and greenhouse gases, then solve global warming issues.

Massive Multiple-Input Multiple-Output (Massive MIMO) has been realized as a promising technology element for 5G wireless mobile communications, in which Spectral Efficiency (SE) and Energy Efficiency (EE) are two critical issues. Prior estimates have indicated that 57% energy consumption of cellular system comes from the operator, mostly used to feed the base station (BS). Yet previously, the User Equipment(UE) is focused on while studying the EE issue instead of BS. In this case, in this paper, an EE scheme that focuses on the optimization of BS energy consumption is proposed. Apart from the previous studies, which divides the coverage area by circuit section, the coverage area is divided by fan section with the help of Propagation theory for zoom in or zoom out. In the proposal, transmission model and parameters related to EE is deduced first. Afterwards, the Cellular Partition Zooming (CPZ) scheme is proposed where the BS can zoom in to maintain the coverage area or zoom out to save the energy. Comprehensive simulation results demonstrate that CPZ presents better EE performance with negligible impact on the transmission rate.

Despite the numerous benefits brought by device‐to‐device (D2D) communications, the introduction of D2D into cellular networks poses many new challenges in the resource allocation design because of the co‐channel interference caused by spectrum reuse and limited battery life of user equipment's (UEs). Most of the previous studies mainly focus on how to maximise the spectral efficiency and ignore the energy consumption of UEs. In this study, the authors study how to maximise each UE's Energy Efficiency (EE) in an interference‐limited environment subject to its specific quality of service and maximum transmission power constraints. The authors model the resource allocation problem as a non‐cooperative game, in which each player is self‐interested and wants to maximise its own EE. A distributed interference‐aware energy‐efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. The authors prove that the optimal solution obtained by the proposed algorithm is the Nash equilibrium of the non‐cooperative game. The authors also analyse the tradeoff between EE and SE and derive closed‐form expressions for EE and SE gaps.

Providing robust content delivery along with efficient wireless resource usage is important for next generation wireless networks. To achieve this, we propose a proactive content caching scheme utilizing transportation systems, especially trains. In our system, we place content servers with CCN capability to every train and station. Segments of video contents are pre-cached by the station servers before trains arrive at stations. Trains receive the contents via high-speed wireless transport while they stop at the stations. We develop a prototype system based on IP and CCN Hybrid protocols. We evaluate its performance by field experiment and compare with traditional CDN scenarios using cellular networks. Evaluations conclude that our system can achieve high-speed and high-reliable video delivery without freezing.

The introduction of device-to-device (D2D) into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of user equipments (UEs). In this paper, we propose a distributed interference-aware energy-efficient resource allocation algorithm to maximize each UE's energy efficiency (EE) subject to its specific quality of service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. The formulated EE maximization problem is a non-convex problem and is transformed into a convex optimization problem by exploiting the properties of the nonlinear fractional programming. An iterative optimization algorithm is proposed and verified through computer simulations.

In this paper, we consider the application scenario where multiple Base Stations (BSs) cooperate to transmit signals to a mobile terminal in the same frequency and any of the cooperative BSs is allowed to enter into sleeping mode to save energy. The mobile terminal employs the Joint Maximum Likelihood Sequence Estimation (JMLSE) based Joint Signal Detection (JSD) to simultaneously detect multiple co-channel signals and judge whether a cooperative BS is active or not. The detection error probability of JSD is analyzed in this paper. For the case of BS sleeping, the error probability is computed based on a tentative modulation scheme which incorporates the M constellation points of conventional M-QAM and the additional constellation point 0. For the case of BS cooperation, the error probability bounds are derived based on a genie-aided receiver, and a new Tighter Lower Bound (TLB) is derived by replacing the genie with a less generous one. Simulation results have verified that the computed error probability can provide a rapid and accurate estimation of the Symbol Error Rate (SER) performance.

In the long term evolution (LTE), Relay node architectures included in order to increase the coverage and system capacity. But designed in the LTE-A and release 12, the Relay node architectures supported mobility. An LTE system based on the network-controlled hard handoff and handoff reuses existing user equipment. In this paper, we propose an adaptive handoff hysteresis scheme with a simplified cost function considering some dominant factors for Mobile Relay such as the load difference target cells, the velocity, and the service type. The performances of the traditional handoff scheme and the proposal are analyzed and compared in details.

In this paper, we present a new Wi-Fi roaming and selection scheme as a solution for macro-cell traffic offloading. The idea is to take advantage of the Access Network Discovery and Selection Function (ANDSF) policies and the IEEE 802.11k standard for Wi-Fi access point (AP) selection process. The ANDSF can provide the list of candidate APs with preferred selection policies. However, due to the fact that ANDSF's selection policies are static and are not based on the real- time network condition, it cannot be considered as a solution for traffic steering and offloading issue in heterogeneous network. In our proposed scheme for Wi-Fi offloading, we address this issue by taking into account the real-time traffic load of Wi-Fi AP as one criterion for selection. We simulate and verify the proposed scheme on NCTUNS 6.0. The simulation result shows that our scheme can effectively offload the data traffic from cellular network to Wi-Fi and improve the overall network throughput.

The extensive deployment of femtocells introduces co-channel interference to the macro cell-edge users (MCEUs), which degrades the throughput improvement of the total network. In this paper, we consider the scenario in which the femtocell base station (FBS) allows the hybrid access of MCEUs on the condition that the MCEUs rent the power resource from it. However, the FBS is power limited. If almost all of its power is used to serve the MCEUs and its original femtocell users (FUEs), when newly authoritized FUEs are switched on, there will be little power left to serve these newly authoritized FUEs. In this situation, the FBS can select one MCEU as a relay to coordinate its information transmission to the FUE. To reward the cooperation of the MCEUs, the FBS will agree to support the service of MCEUs without charging. Stackelberg game is used to find the optimal power and price value for this procedure. An MCEU relay selection criterion is proposed for the FBS. The optimal power that the MCEU can obtain from the FBS and the power that the MCEU can obtain from the FBS and the power that the MCEU can share with the FUEs are obtained. The simulation results show that the throughputs of both the FUEs and the MCEUs are improved.

This paper aims at improving the demodulation performances of Four-element Single Sideband signals by proposing a new receiver structure involving an Inter-Signal Interference cancellation filter. The filter makes use of the estimated symbols generated by the receiver to approximate the interference between multiplexed signals inherent to the use of low complexity Hilbert Transform filters at the transmitter. Simulation results show that the new receiver structure performs better than the one formerly proposed in early works on this topic.

Peer-to-Peer file sharing, computing and communication services are widely used by internet community and thoroughly studied by academia. However, main utilization of such services is in the wired networks due to several significant issues in the wireless world like connectivity, bandwidth, battery, unwillingness of cellular operators, etc. In the recent years, offering femtocell access points to subscribers became very promising business model for cellular operator. It also opens space for a variety of new services at the users premises. In this paper, we propose novel architecture for mobile peer-to-peer file sharing based on the femtocell deployment and describe a role of the femtocell in such scenario. We also describe traditional architecture and previously proposed solutions for mobile P2P. We evaluate these approaches and clearly show that the femtocell plays significant role and moves mobile P2P services into reality. We also discuss several current issues which can be easily solved by this architecture.

This paper proposes a study of performances of the Orthogonal Four Single Sideband elements modulation previously proposed by Ohta [1]. Realistic parameters such as receiver noise and various channel effects are introduced in the derivation of the modulation. Transmission of two QPSK signals multiplexed in one 4-SSB (Single Sideband) signal over OFDM (Orthogonal frequency-division multiplexing) is considered and a demodulation process exploiting a turbo equalizer is presented. Computer simulations are used to verify the performances of the proposed modulation scheme. Results show that 4-SSB QPSK signal over OFDM can achieve performances close to QPSK-OFDM scheme while carrying twice more information, provided perfect channel equalization. However, we also show that in a non-ideal equalization case, performances of 4-SSB QPSK OFDM become highly sensitive to channel effects.

The wireless communication system is evolving towards a ubiquitous wireless network in which multiple-radio devices can roam anytime and anywhere across different access networks. Minimal latency and low packet loss ratio during handover (HO) are the indispensable requirements to reduce service disruption and support real-time services. However, minimizing service disruption is a challenging issue to multiple access networks since HO latency is much higher than that of homogeneous networks. Link layer trigger or layer 2 trigger has been proposed to address this issue. Nevertheless, the precise timing and definitive criteria for initiating L2 trigger which can greatly affect the handover performance is not specified. This paper considers the issue of how to timely initiate handover decision from the link layer by using adaptive filtering algorithm to predict forthcoming received signal. Particularly, we propose Recursive Least Squares (RLS) as the predictive method for layer 2 triggering and compare its performance with various conventional filtering techniques. A simulation in which the proposed predictive algorithm is implemented to a handover scenario from Wi-Fi to WIMAX is also presented. Our numerical analysis and simulation results show that the proposed algorithm can achieve better performance than that of the other algorithms and can be used to enhance the handover performance in heterogeneous wireless networks.

In the previous work, a single antenna interference cancellation (SAIC) algorithm named least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) has been proposed. However, LMS-BJMLSE requires a long training sequence (TS) for channel estimation, which reduces the transmission efficiency. In another work, in order to solve this problem, a subcarrier identification and interpolation algorithm was proposed, in which the slowly converging subcarriers are identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired channel estimate. However, this correlation relationship was only found based on simulation results and no clear mathematical proof was given. The performance of the algorithm was only evaluated for the case of single interference. In this paper, the mathematical proof of the correlation relationship between MSE and MSD is given. Furthermore, we generalize LMS-BJMLSE from single antenna to receiver diversity, which is shown to provide a huge improvement over single antenna. The performance of LMS-BJMLSE is also evaluated for the case of dual interference.

In orthogonal frequency division multiplexing (OFDM) based cellular systems, co-channel interference (CCI) from adjacent interfering base stations (BSs) would greatly degrade the bit error rate (BER) performance of cell-border users. In the previous work, a blind single antenna interference cancellation (SAIC) algorithm named least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) has been proposed. The proposed LMS-BJMLSE algorithm is blind with respect to interfering signals and neither the training sequence (TS) nor pilot signal from interferers is needed. However, the conventional LMS-BJMLSE requires a long training sequence (TS) for channel estimation. In this paper, we propose a TS reduction scheme in which the subcarriers are divided into small groups based on the coherence bandwidth, and the slowest converging subcarrier in each group is identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired signal. The identified subcarrier's channel estimate is replaced by the interpolation result using the adjacent subcarriers' channel estimates. Simulation results demonstrate that the proposed algorithm could reduce the required TS length by 80%.

Mobile Sensor Networks (MSN) are used for network load balancing, prolonging network lifetime, and improving network coverage by monitoring critical areas where manual sensor deployment cannot be performed. Addressing the problem of how to achieve maximum network coverage and network uniformity, after deploying sensors in critical areas randomly, is of significant importance recently. In this paper, we design an energy efficient distributed self-deployment algorithm, which is based on the diffusion of mobile sensors in the Region of Interest (ROI). Mobile sensors are diffused from denser sensors area to lesser or uncovered area in ROI, on the basis of localized information. Our algorithm considers ROI with the absence as well as presence of obstacles. Resemblance in the numerical and simulation analysis confirms the concreteness of our algorithm.

In orthogonal frequency division multiplexing (OFDM) based cellular systems, co-channel interference (CCI) would greatly degrade the bit error rate (BER) performance of cell-border users. Joint maximum likelihood sequence estimation (JMLSE) based single antenna interference cancellation (SAIC) algorithms have been under intense research. In this paper, both the upper and lower error probability bounds for JMLSE are extended to MQAM-OFDM systems based on a genie-aided receiver. The derived upper and lower bounds are valid for any MQAM and an arbitrary number of interferers. A tighter lower bound is derived by replacing the genie with a less generous one. We prove that this new lower bound is much tighter compared to the conventional lower bound.

This paper describes the velocity adaptive vertical handoff algorithm on multi-frequency band wireless systems (V-MFVHO: Velocity adaptive Multi Frequency Vertical HandOff) in order to achieve the homogeneous handoff decision strategy. WiMAX Forum has published three licensed spectrum profiles: 2.3 GHz, 2.5 GHz and 3.5GHz. These frequency bands operated by WiMAX coexist on the same service area. In heterogeneous wireless networks, the problem is complicated since there are many differences cell coverage due to the frequency band, traffic conditions, data rate, and moving speed of MS (Mobile Station). This paper clarifies the design a scheme for selecting the candidate cells to satisfy the QoS requirements from each MS using the estimation of the location and the velocity of each MS by back-propagation neural network and the traffic and RSSI (Received Signal Strength Indicator) conditions of MS for each cell allocated for different frequency bands.

We study a method of cognitive interference management in user-deployed 3G femtocell networks. Each femtocell cognitively recognizes an interference signature from the network environment and opportunistically allocates the proper channel patterns for interference minimization. Simulation results show great channel SINR improvement by implementing our opportunistic channel scheduler. The approach offers a novel view of autonomous spectrum management based on cognitive interference recognition and adaptive channel management.

In orthogonal frequency division multiplexing (OFDM) based cellular systems, co-channel interference (CCI) from adjacent interfering base stations (BSs) operating and coexisting with the desired BS in the same frequency channel would greatly degrade the bit error rate (BER) performance of mobile receivers which are near to cell borders. CCI cancellation algorithms by using only one receiving antenna named single antenna interference cancellation (SAIC) have been under intense research. However, most of these algorithms require additional information from interfering signals. Furthermore, the orthogonality of a asynchronous interfering signal is destroyed and therefore, conventional SAIC algorithms cannot be applied. In this paper, we propose a blind SAIC algorithm named least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) for asynchronous interference cancellation. The proposed LMS-BJMLSE algorithm is blind with respect to interfering signals and neither the training sequence (TS) nor pilot signal from interfering signals is needed. Its complexity is independent of channel length compared to the conventional time domain JMLSE algorithm. Simulation results show that the proposed LMS-BJMLSE algorithm could greatly improve the BER performance in both synchronous and asynchronous interference conditions.

In anycast routing the packet is routed to anyone of the anycast group members. Anycast allows supporting many services and can significantly improve network performance of ad hoc networks. Unicast protocols have been extended to support anycast routing, however, these protocols consider solely the shortest path routing. The protocols based on a theory of potential fields have been proposed to improve packet delivery ratio in large mesh networks by routing to area with higher density of nodes instead of the shortest path routing. In this paper, we consider the density of nodes through count of routes. We propose simple anycast protocol which utilizes count of routes to the anycast group member as a routing metric and evaluate its performance.

Due to limited frequency resources, the co-channel interference (CCI) from adjacent femto cells operating and coexisting with conventional macro cells in the same frequency channel would greatly degrade the bit error rate (BER) performance of mobile terminals. CCI cancellation algorithms for the 2G GSM and the 3G CDMA systems have been under intense research. However, since orthogonal frequency division multiplexing (OFDM) has been adopted in the next generation mobile communication systems, dealing with CCI is more complex in OFDM than in GSM or CDMA because it requires dynamic power control and frequency allocation with advanced coordination among base stations. In this paper, we propose a single antenna interference cancellation (SAIC) algorithm named power adaptive joint maximum a posterior (PA-JMAP) for OFDM systems. The proposed PA-JMAP algorithm jointly decodes the desired and interfering data by using a joint trellis, which defines all possible combinations of each base station's encoder trellis. Signal to interference ratio (SIR) estimated by a sequentially updated estimator is used to track power changes of the received signal. Simulation results show that the proposed PA-JMAP decoder could greatly improve the BER performance for OFDM systems even under severe CCI conditions when compared to a conventional viterbi decoder (CVD).

For wireless multicast applications like multimedia conferencing, voice over IP and video/audio streaming which require reliable transmission of packets within short delivery delay, adding forward error correction (FEC) can enhance packet error rate (PER) performance under low channel conditions, but cannot guarantee a reliable transmission. However, for applications like file transfer, distributed computing, chat and whiteboard sharing, reliability is crucial to the performance of the application. Reliable FEC or hybrid automatic repeat request (H-ARQ) can provide reliability, and can be deployed at different levels of the protocol stack. In this paper, we compare the performance of a reliable packet-level FEC protocol and a reliable byte-level FEC protocol through simulation and analytical analysis. Our result show that byte-level FEC can deliver a higher reliability performance in terms of throughput and delivery ratios.

For end-to-end wireless multicast applications like multimedia conferencing, voice over IP and video/audio streaming applications which require reliable transmission of frames within short delivery delay, adding MAC layer H-ARQ (hybrid automatic repeat request) can enhance reliability and delivery delay. However, IEEE802.11 does not support reliable multicast due to its inability to exchange ACKs with multiple recipients. In this paper, we propose a protocol that enhances WLAN reliability performances by using adaptive FEC (forward error correction) while maintaining an optimal throughput performance.

Routing in ad-hoc networks is challengeable due to the mobility of the nodes. Location-based routing protocols, unlike other protocols which rely on flooding, excel in network scalability. Further more, new location-based routing protocols, like BLR[1] , IGF[2], & CBF[3] have been proposed, with the feature of being beaconless, which improve more in terms of scalability and battery consumption. Such beaconless routing protocols can work efficiently in dense network area. However, these protocols' algorithms have no immunity from routing into sparse area. In this article, historical signal strength has been added as a factor into the BLR algorithm, which improves in avoidance of routing into sparse area, and consequently improves the global routing efficiency.

This paper reports experimental results regarding radio propagation, transfer, reflection, and scattering characteristics through tempered glass, plasterboard, plywood, plastic tile, and aluminum plate in an anechoic chamber environment at 2.45, 3.5 GHz, 5 GHz, and 10 GHz frequency bands. The time domain measurement method is used for accurate measurement, while the measurement accuracy of the electric wave is 7.5 cm obtained by sweeping 2 GHz bandwidth at the center of the measurement frequency. Simulation results show that fundamental experimental data were obtained for the analysis of actual radio environments, and that the measured data are reliable. The data will be useful for propagation analyses such as ray tracing.

IEEE 802.11b standard provides 1, 2, 5.5, 11 Mbps data rates. These data rates can be made possible by using different modulation techniques: DBPSK, DQPSK, CCK5.5 and CCK11 respectively. Rate adaptation is the process of dynamically selecting a proper modulation scheme depending on channel conditions in order to improve total throughput. Current rate adaptation protocols deal with unicast links rather than multicast. Channel conditions can be estimated by measuring the received signal strength (RSS) of a feedback message (CTS, ACK). However, IEEE 802.11 standard does not provide feedback messages for MAC layer recovery on multicast frames. This is due to collisions which may occur to simultaneous feedback messages from multicast group members. Therefore, in order to rate adapt multicast, a reliable multicast MAC protocol has to be introduced. In this paper, we propose a protocol which provides multicast reliability to WLANs and enhances its throughput by using rate adaptation. Further, we evaluate our protocol by throughput analysis and computer simulation. Simulation results suggest that our protocol performs better than a related/existing protocol in both throughput as well as reliability performances

Burst synchronization scheme for a millimeter band OFDM uplink system is proposed. For the estimation of subscribers access and its frequency offset and time offset, an OFDM-CDMA pilot signal is adopted. The accuracy and probability of error estimation are investigated by the theoretical analysis and simulations. This paper clarified that the proposed synchronization scheme has the capacity of four users simultaneous access with 10% of error estimation probability, when 64 of the subchannels are used for spreading each subscribers pilot signal.