Penetration testing (pentesting) assesses cybersecurity through controlled, authorized attacks, but traditional manual methods demand considerable human and time resources. Reinforcement learning (RL), with its agent-environment interaction paradigm, offers a promising approach for autonomous pentesting. Despite remarkable advancements in this field, there is a lack of comprehensive reviews and perspectives on RL-based autonomous pentesting. To address this gap, this paper presents a systematic review of RL-based autonomous pentesting research. We outline the key challenges faced when applying RL in autonomous pentesting and categorize the existing literature into two main areas: attack path planning and autonomous pentesting frameworks, based on the research objectives and hypotheses. Additionally, we offer an in-depth analysis of the latest advancements and limitations in this field, while proposing a perspective on future research directions in the field of RL-based autonomous pentesting. We hope that our work will provide valuable insights for researchers, contributing to the advancement of autonomous pentesting and its practical application in the complex and diverse scenarios of the real world.

In disaster relief operations, both emergency communication and personnel positioning are critical. This paper proposes an enhanced unscented Kalman filter (UKF) positioning algorithm for communication terminals, utilizing random access signals (RACH) and demodulated reference signals (DM-RS). This method can accurately determine the location of on-site terminals without interfering with emergency communication. The algorithm utilizes TOA and AOA two-dimensional observations for unscented Kalman filter. The severity of non-line-of-sight (NLOS) effects at the current location can be quantified by comparing the residual, which are difference between measured and predicted values, with the standard deviation of the measurement data. According to the severity, the residual value undergoes different processing, and then determines whether to reset Kalman filtering. The simulation results demonstrate that under complete NLOS conditions, the proposed coarse estimation method achieves an average root mean square error (RMSE) of 52 meters after 360 time steps (approximately 7 seconds) of measurement processing, representing a 27.3% improvement in positioning accuracy compared to the recent UKF-based approach. Meanwhile, when both coarse and precise estimations are combined, the average RMSE reaches 14 meters, improving performance by 81.3% compared to the recent UKF method.

Aerial vehicles are the primary enablers of urban low-altitude services. One of the key methods to achieve high data rate communication is the use of phased array narrow beams. However, equipping them can complicate the process of neighbor discovery, especially in the absence of clock synchronization among the nodes. This paper addresses the problem of asynchronous and prior information-agnostic Urban Aerial Ad-Hoc Networks (UAANs) and proposes a multi-Token-based network formation mechanism. By employing parallel token transmissions, the network formation duration is significantly reduced. Additionally, a neighbor cooperation mechanism is introduced during the network formation process, which improves the link number completeness through the Node Transition Phase. The simulation results show that, compared to D-SAND network formation strategy, when the number of nodes is 30, our strategy reduces the network formation time to 17.5% while ensuring network connectivity, at the cost of missing only 0.9% of the links. As the number of nodes grows, the advantages of our approach become increasingly evident.

In multiple-input multiple-output (MIMO) wireless communication systems, accurate and low-overhead channel state information (CSI) acquisition is crucial for achieving high performance, which is significantly hampered by user mobility. To address the challenge of efficient CSI acquisition in high-mobility scenarios, this paper proposes a clustered channel estimation and prediction method with historical channel knowledge, where a channel knowledge map (CKM) is constructed using historical channel estimates from other users. During the channel estimation phase, the clustered channel is projected onto a low-rank subspace learned from historical estimates, thereby exploiting the sparsity of the multipath channel without extra overhead. In the channel prediction phase, the CKM provides channel temporal correlation characteristics at the required time granularity, which is not limited by the current user’s pilot interval, enables accurate prediction of future channels. Simulation results demonstrate that the proposed method not only significantly improves accuracy of channel estimation and prediction but also maintains channel prediction performance with low pilot overhead.

Large language models (LLMs) have been applied across various domains due to their superior natural language processing and generation capabilities. Nonetheless, LLMs occasionally generate content that contradicts real-world facts, known as hallucinations, posing significant challenges for real-world applications. To enhance the reliability of LLMs, it is imperative to detect hallucinations within LLM generations. Approaches that retrieve external knowledge or inspect the internal states of the model are frequently used to detect hallucinations; however, this requires either white-box access to the LLM or reliable expert knowledge resources, raising a high barrier for end-users. To address these challenges, we propose a black-box zero-resource approach for detecting LLM hallucinations, which primarily leverages multi-perspective consistency checking. The proposed approach mitigates the LLM overconfidence phenomenon by integrating multi-perspective consistency scores from both queries and responses. In comparison to the single-perspective detection approach, our proposed approach demonstrates superior performance in detecting hallucinations across multiple datasets and LLMs. Notably, in one experiment, where the hallucination rate reaches 94.7%, our approach improves the balanced accuracy (B-ACC) by 2.3 percentage points compared with the single consistency approach and achieves an area under the curve (AUC) of 0.832, all without depending on any external resources.

Electromagnetic (EM) parameter plays a crucial role in wireless link prediction models such as Ray-tracing (RT), to shape and predict the effect of environmental materials on the characteristics of received signals. The use of field wireless measurements is a direct solution to sense EM parameters of materials. The locations of the field measurements have a significant effect on sensing and calibration performance of EM parameters. In this paper, a data collection algorithm is proposed to plan the measured locations of the field wireless measurement. Specifically, this paper presents an unmanned aerial vehicle (UAV) trajectory planning scheme for path loss data collection within a limited time to enhance the sensing and calibration of permittivity. The proposed algorithm is designed from two aspects simultaneously to enhance the sensing of permittivity: the detection of estimate-able permittivity parameters from the collected data, and the expected Cramér-Rao lower boundary (CRLB) of the detected permittivity parameters. The numerical results show that the proposed algorithm is effective in reducing the expected CRLB and detecting the permittivity parameters. Furthermore, these offline trajectories are validated in a simulation scenario, which demonstrates the benefits in estimating permittivity parameters, and the gain from sensing results in path loss prediction.

Location-specific and map-based radio propagation methods are increasingly crucial for wireless systems and applications. These map-based methods such as Ray-tracing (RT) necessitate precise environmental modeling, but acquiring accurate material parameters remains challenging. Emerging practices, by calibrating material parameters through wireless measurements, have been explored. However, considering the cost and limited time of wireless measurements, data collection needs to be optimized for the material calibration in one area. In this paper, a guided data collection approach is proposed for RT model permittivity calibration. Focusing on the flexible scenario of path loss data collection using an unmanned aerial vehicle (UAV), our objective is to present a UAV trajectory planning scheme for path loss data collection within a limited time to enhance permittivity calibration. The numerical results demonstrate that the proposed algorithm is effective in reducing the Cramer- Rao lower bound and detecting all permittivity parameters for permittivity estimation by planning a better estimator.

This paper provides a theoretical analysis of a recently proposed Asynchronous Grant-Free access scheme with rateless codes (A-GF-rateless), which has been demonstrated via simulation the potential to reduce. An approximate expression of its average delay is provided. And it is proved that when the blocking effect is negligible, A-GF-rateless is stable for any parameter settings, indicating that high traffic load can be supported; and with the signal-to-noise ratio (SNR) per user decreasing, the energy efficiency of A-GF-rateless approaches the limit of random access schemes with Gaussian Multiple-Access (GMA) channel given any spectral efficiency, suggesting its advantages in low power consumption and high throughput scenarios.

Multiparties in image secret sharing (ISS) need to verify (detect and recognize) each other, which is seldom considered and realized in traditional methods. In this paper, we introduce the definition of multiparty verification. It includes two stages, i.e., a detection stage and a recognition stage, with evaluation methods that are also discussed. A multiparty verification scheme without pixel expansion is developed, which is suitable for both dealer attendance and nonattendance. The classic hash function, public key cryptography and visual cryptography are technically fused in the developed scheme. In the shadow distribution phase, each participant can verify the received shadow using his private key. In the restoration phase, for the case of dealer attendance, he can verify each shadow received using his secret key; for the case of dealer nonattendance, participants can verify each other before exchanging their shadows. We conduct analyses and illustrations to validate the developed scheme.

With the continuous advancement of 5G, the system architecture of the network needs to be highly adaptable to serve new and legal cases while meeting the requirements of users for higher security of communications. This paper proposes a SDN/NFV-based core network slicing for secure mobile communication. This slicing provides a solution to the problem of unencrypted user data with strong connectivity, security and scalability. On the one hand, it is compatible with the existing network architecture and suitable for 5G networks. In addition, it uses symmetric keys corresponding to the user and the network to encrypt user data, which is different from VPN. Moreover, core network slicing based on SDN/NFV is the key to satisfying the complex and diverse scenarios of 5G, with future-oriented scalability and flexibility.

Nowadays, fingerprint positioning is the mainstream method in indoor positioning and Weighted-K-Nearest-Neighbor (WKNN) is most widely used in fingerprint matching. However, the fingerprints which are far away from each other might also be similar and the target fingerprints have to match with all fingerprints every time, which results in unsatisfactory accuracy and efficiency of WKNN. In this paper, we propose an algorithm to classify regions with comprehensive consideration of geographic location and fingerprint similarity, meanwhile preprocessing and filtering the data are used to improve the accuracy. In addition, we propose that the cellular signal in Long Term Evolution (LTE) is more suitable to be the signal source in indoor passive localization scenario. Results show that in the LTE environment, our proposed algorithm effectively reduces the positioning error by about 24% and improves the convergence speed compared with WKNN.

Improved ReliefF (IReliefF) algorithm is proposed as a feature weighting method for fingerprint-based localization. ReliefF algorithm is a widely-used feature selection approach in machine learning, but some problems exist when it is directly applied to feature weighting in outdoor localization. IReliefF algorithm is our improved version, by assigning more reasonable initial values, introducing the variable of distance threshold and using Pearson correlation coefficient (PCC) to eliminate redundant features. Each base station (BS) serves as a feature and every mobile terminal (MT) can obtain received signal strength (RSS) from surrounding BSs. The weight values of BSs are generated according to the contributions to localization. They also provide a good estimation of BSs' quality. IReliefF algorithm and distance weighted k nearest neighbor (WKNN) compose the effective feature weighting fingerprint-based localization method. The performance of this method is verified by three measured data sets from commercial cellular networks. Our method proves to have higher localization accuracy than WKNN.

Due to the shortage of IPv4 addresses, many hosts are currently assigned to a single IPv4 address by using one or a number of NAT devices. However, numerous NAT devices cannot be upgraded for executing 6to4 due to technical and/or economic reasons. Solutions depending on Double Network Address Translation 64 are a good way to utilise shared IP4 addressing. Mapping of address and port using translation (MAP-T) is a technique that accomplishes double translation on Border Relay (BR) and customer edge (CE) devices. IPv4 and IPv6 forwarding, IPv4 and IPv6 fragmentation functions, and NAT64 translation functions are used by MAP-T. This enables increasing numbers of IPv6 in both clients and servers in order to possess the best defence against certain attacks, such as routing loop attacks, spoofing attacks, denial-of-service attacks. We have here proposed some procedures for creating frameworks and sustaining secure IPv6 networks according to applications, environs and architecture.

Received signal strength (RSS) is a typical measurement used for indoor and outdoor localization because of its low power consumption and no requirement of additional mode. Fingerprint localization is known to have higher accuracy than traditional methods and the most frequently used algorithm is K Nearest Neighbors (KNN), where common metrics have some disadvantages. In this paper, exponential transformation and tangent distance will be utilized to improve accuracy of weighted-KNN based fingerprint localization, where Manhattan tangent distance (MTD) and approximate Manhattan tangent distance (AMTD) are proposed. In addition, data are collected by a field tester from three different outdoor area for test. Experiments demonstrate that MTD achieves the lowest root mean square error (RMSE), AMTD achieves the second lowest RMSE with much lower computation complexity compared to MTD and both of them outperform common metrics.

A novel geometry-based model (GBM) is proposed to locate the transmitter based on received signal strength (RSS), where there is no anchor point. Previous studies were mostly based on maximum likelihood (ML) estimator. The novel GBM estimator gives a new approach for localization based on the geometrical significance, which is to find a point with least sum of distance to the edge of circles. The corresponding Cramer-Rao Lower Bound (CRLB) of unbiased estimator and a time-saving Monte Carlo approach to the CRLB of GBM are derived. In addition, the confidence interval of Root-Mean-Square Error (RMSE) is also derived based on Central Limit Theorem in order to make sure the results of simulation are statistical significant. The simulation results demonstrate that GBM estimator can achieve much higher accuracy compared to ML. The proposed GBM algorithm is verified with experimental data set from commercial cellular networks, and shows better performances.

In future internet architectures, end-to-end (E2E) secured personal messaging is essential. So here an E2E user two-way authenticated double encrypted messaging architecture based on hybrid RSA for private messaging is proposed. Our P2P protocol works over TCP protocol for creating direct connections in between, with IPv4 broadcast options to discover peers on the same LAN. Our protocol implements perfect forward secrecy using Diffie-Hellman key exchange with renegotiation capability in every session with optimal asymmetric encryption padding and random salts. For making hybrid RSA with double encryption, in encryption level, main RSA is integrated with efficient RSA to give more statistical complexity. In the decryption process, the CRT is used for very high efficiency with integration with shared RSA. Our architecture also gives a hassle-free, secure, peer-to-peer, strong and reliable platform with E2E encryption for private messaging and it can also work with future internet architectures.

Efficient balancing of privacy and strong authentication in end-to-end (E2E) security constitutes a challenging task in the field of personal messaging. Since RSA is a ubiquitous approach, we here propose a hybrid RSA-based, highly efficient, reliable and strong personal full mesh networked messaging scheme. M-prime RSA and CRT-RSA with shared RSA makes our hybrid RSA decryption much more secure and efficient and protects our users with complete privacy. However, computational modular exponentiation complexity and partial key exposure vulnerability of RSA present two major obstacles. Low modular complexity and asymptotic very slow speed of decryption of RSA, with the ease and speed problem in encryption of RSA are also problems to be solved. Our hybrid RSA cipher resolves all of the above issues and provides protection against exploitation of multiplicative property and homomorphic property of RSA. Our full mesh networking scheme also ensures E2E encryption for all peers. So, our three-way authenticated hybrid RSA messaging scheme achieves a perfect balance of efficiency, security, authentication, reliability and privacy. Consequently, our scheme offers a smarter choice for private messaging in existing, as well as future, internet architectures.

Distributed Principal Component Analysis (DPCA) is a useful tool to make a trade-off between the cost of communication and data error. In this paper, DCPA is used for dimension reduction to compress data on the uplink. There is no information but the data error threshold in the base station (BS). Based on it, the relationship between the data error threshold in the BS and in user equipments is proved and revised. The revised relationship is very simply and irrelevant to the quantity of user equipments. After that, a novel energy-saving algorithm on the uplink for multimedia push is proposed. In the algorithm, the dimension reduction is applied in every user equipment with the data error threshold, and the compressed data matrixes would be transmitted to the BS. In simulation, the energy could be saved while using the algorithm, and the effect is changed with different data sets. Afterwards, the upper bound about the ratio of energy saving is estimated.

Wireless communication network is always resource limited, which is hard to satisfy the rapidly growing QoS requirements of users, such as high data rate and low energy consumption. The simple and straightforward solution is to alleviate the network resources and energy consumption. This paper proposes Intelligent Content Service Network (ICSN) to satisfy users' requirements based on the concept of spatial-temporal decoupling. The main idea of ICSN is to make contents predictions by analyzing users' accessing history, then choose the right time and location to accomplish content transmission according to the predicting results. Experiments are conducted under two typical and ubiquitous scenarios, which are Tsinghua XuetangX and THU Online Learning Platform, in order to illustrate the energy efficiency of ICSN, here represented by the network traffic flow reduction rate. After analyzing the users' behaviour, we put forward predicting methods according to their respective characteristics, and results show that the network traffic is reduced by 23% and 68% under these two scenarios respectively. Moreover, for ideal condition that both of time and location can be predicted precisely, the reduction rate reaches 98%. The results show that ICSN can greatly improve the energy efficiency, which depends on the prediction precision of content transmission time and location.

Abstract In this paper, we develop a delay‐centric parallel multi‐path routing protocol for multi‐hop cognitive radio ad hoc networks. First, we analyze the end‐to‐end delay of multi‐path routing based on queueing theory and present a new dynamic traffic assignment scheme for multi‐path routing with the objective of minimizing end‐to‐end delay, considering both spectrum availability and link data rate. The problem is formulated as a convex problem and solved by a gradient‐based search method to obtain optimal traffic assignments. Furthermore, a heuristic decentralized traffic assignment scheme for multi‐path routing is presented. Then, based on the delay analysis and the 3D conflict graph that captures spectrum opportunity and interference among paths, we present a route discovery and selection scheme. Via extensive NS2‐based simulation, we show that the proposed protocol outperforms the benchmark protocols significantly and achieves the shortest end‐to‐end delay. Copyright © 2015 John Wiley & Sons, Ltd.

Repeat transmission of hotspot traffics results in great waste of energy and bandwidth in wireless network, for the communication of current network is content careless. To address this issue, a novel content aware transmission schema named CASoRT System was put forward in our previous research to reduce wireless resource waste by the benefit of broadcast. In this paper, we propose a K neighbors collaborative filtering prediction method to forecast the request of hotspot traffics, which will be broadcasted by CASoRT. Firstly, we introduce the traditional prediction methods and describe the framework of our job. Then, we deliver the expression of our method in formulation, and discuss the time complexity in quantitative analysis. At last, our method is validated by simulation, and the results demonstrate that the new schema can potentially lead to 20% deduction compared with the unicast schema in hotspot traffic transmission. And the results also show that our method reduces 25% prediction time than traditional methods, but with the same performance in saving wireless resources.

This paper considers the problem of cognitive access to the spectrum of primary users in a hierarchical network with multiple time varying channels. We propose a robust policy which is not only based on the occupation and quality situations of the channels, but also considering the inaccuracy of the channel quality estimation. The contribution in this paper is twofold. First we have established a new and more meaningful performance metric to evaluate the secondary user's total throughput among time varying channels, rather than just the channel occupation rate. The optimization objective of cognitive access is designed to maximize the total effective throughput among multiple channels subject to collision constraints for primary users protection. Second we develop a robust algorithm in the case that the parameter of the channel quality distribution is unknown and thus only can be estimated using finite channel samples. The proposed robust algorithm shows excellent effective throughput performance meanwhile meets the collision constraints without perfect knowledge on channel quality information, which makes the robust algorithm more practical and easy to implement in various application scenarios. In order to validate the theoretical analysis of robust algorithm, numerical results are presented to reveal that by introducing the robust access policy, which can take the advantage of access opportunities of the channel in time and frequency domain when it is free and of high quality, the total effective throughput can be improved significantly.

This paper considers an OFDM wiretap channel with a legitimate transmitter (Alice), multiple legitimate receivers (Bobs), and an eavesdropper (Eve). Alice simultaneously transmits confidential message to each individual Bob. The timedomain Artificial noise (AN) is firstly employed to the wiretap OFDM channel with multiple Bobs. Under the proposed AN approach, a nonconvex sum secrecy rate maximization problem is formulated to jointly optimize subcarrier allocation, power allocation and AN design. To solve this tough problem, the optimal subcarrier allocation is found at first, and then a low-complexity Lagrange dual method is developed to jointly optimize power allocation and AN design. Finally, numerical results demonstrate the effectiveness of the proposed algorithms, including power allocation gain, AN gain and multiuser gain.

The current wireless communication network is a content-careless network, in which every request needs a new transmission, even those requests imply the same content. However, the Long-Tail distribution of users' interests caused by the converging behavior, determines that the redundant traffic, which is the re-transmission bearing same content, is rather enormous. Therefore the content-careless scheme of network transmission introduces large energy waste. In our previous work, we put forward a novel transmission scheme CASoRT to mitigate such energy waste by broadcasting those hot contents. After the contents are broadcast, the terminals in coverage could cache those contents in local memory and load those contents when request. Thus, the massive redundant transmission of the very content is relieved by one broadcast transmission. In order to exploit the advantage of broadcast, the proper recommender scheme, deciding which contents should be broadcast, needs to be optimized. In this paper, we present certain perspective in analyzing the log of campus network in Tsinghua University. It shows that utilizing users converging character could improve the efficiency of CASoRT with analysis. Finally, location based content recommendation scheme is proposed, verified by simulation.

This paper studies the problem of cognitive access in a hierarchical network with practical time varying channels. We propose an access policy which is not only based on the sensing outcomes about the occupation states of the channel, but also based on the channel quality situations. The contribution in this paper is as follows. First we propose an access policy which takes advantages of the characteristics of the channel and thereby fully utilizes the access opportunities that channel could offer. The optimization objective of cognitive access is designed to maximize the effective throughput subject to collision constraints imposed by channels. It is shown that our proposed algorithm can achieve high throughput performance by grasping the access opportunities which have good channel qualities. Second we extend the throughput region achieved by secondary users under general collision constraints. Specifically, our algorithm can achieve better throughput performance not only when collision constraints are tight, but also under loose collision constraints, which has not been studied in previous related work. Besides, We also give the practical algorithms to implement our policy, which makes it easily to be deployed in practice. In order to validate the theoretical analysis, numerical results are presented to show that by introducing the proposed policy, which can fully utilize the characteristics of application scenarios and take the advantages of access opportunities of the channel when it is of high quality, the effective throughput can be improved significantly under general collision constraints.

Coexistence research for LTE systems is indispensable to ensure that the time division duplex (TDD) Long Term evolution (LTE) system can coexist with the frequency division duplex (FDD) LTE system operating at the adjacent frequency band within the same geographical area. Previous studies on this topic only focus on the throughput loss of the victim system from the data channel viewpoint. In this paper, the performance of control channels (CCHs) in the typical coexistence scenario is investigated, since the performance of communication systems is largely dependent on the quality of CCHs. The CCHs' interferences are analyzed in detail, including the coexistence issue for CCHs, Co-Channel Interference (CCI) analysis, and Adjacent Channel Interference (ACI) on CCHs. The simulation results indicate that the ACI generated by the Base Station (BS) of collocated LTE system degrades the performance of the uplink (UL) CCHs of the victim LTE system significantly.

End-to-end delay minimization is one of key challenges in multi-hop Cognitive Radio Ad Hoc Networks (CRAHNs), where the opportunistic transmission impacts on each hop of routing paths. However, the problem is only considered in routes establishment while traffic assignment is also an important process of packet delivery in highly dynamic transmission environment. For this reason, a novel queue theory based optimal traffic assignment algorithm for multi-path routing in CRAHNs is proposed to minimize the overall end-to-end delay in this paper, which dynamically assigns the traffic load on multiple routing paths considering the spectrum availability and service rate of each hop. The algorithm is performed in using a gradient-based search method to find the optimal traffic assignment strategy. The simulation results demonstrate that the proposed algorithm significantly outperforms other baseline schemes in end-to-end delay in CRAHNs.

This paper considers the problem of cognitive access to channels of primary users in a hierarchical network with multiple practical time varying channels. The proposed access strategy is not only based on the sensing about the occupation of the channel by the primary user, but also based on the channel state of the secondary user. We have established a new and more meaningful performance metric to measure secondary user's total throughput among channels, rather than just the channel occupation rate. The optimization objective of cognitive access is designed to maximize the total throughput among multiple channels subject to collision constraints used for primary users protection. It is shown that our proposed algorithm can achieve higher throughput under tight collision constraints through grasping the access opportunities which have good channel qualities. Numerical results are also presented to validate the theoretical analysis and the expressions of algorithm parameters. This paper shows that the total throughput can be improved significantly by introducing the new access strategy, which takes the advantage of access opportunities of the channel in time domain when it is free and of high quality.

In this paper, a two-way relay channel is considered, where two sources transmit information to each other with the help of a half-duplex decode-and-forward (DF) relay. We investigate the energy efficiency (EE) for the DF two-way relay transmission (TWRT). The EE expression is derived first and the properties of the EE are then analyzed. An novel algorithm is proposed to find the maximal EE under the relay power constraint for the DF-TWRT. Simulation results confirm the theoretical findings of the EE for the DF-TWRT.

SUMMARY Cognitive radio (CR) can significantly alleviate the network pressure caused by the rapid development of wireless communications through allowing secondary users (SUs) to obtain spectrum resource from primary users (PUs). One key issue of CR technology is spectrum sharing, that is, how spectrum should be allocated between SUs without causing interference to PUs. In this paper, we propose abilateral bargainingmechanism to achieve this goal between two SUs. The general network scenario with multiple SUs can be decomposed into multiple pairs of bilateral bargaining studied in this paper. The SUs have to reach a mutual satisfactory agreement on the partition of spectrum by making alternating offers to each other. We model such bargaining process as dynamic finite/infinite horizon multistage game with observed actions and fully characterize the corresponding subgame perfect equilibria. Moreover, theoretical analysis and numerical results indicate that our proposed scheme can effectively allocate spectrum resource between SUs. Copyright © 2012 John Wiley & Sons, Ltd.

Content Aware Soft Real Time Media Broadcast (CASoRT) is a new solution for information service of cellular network. As the similar distribution of user's interest, the data of same content may be accessed and retransmitted frequently in cellular network during certain period of time, which caused the dissipation of both energy and spectrum efficiency. With the development of Data Mining, the CASoRT system could discovers the user's common interests and broadcast such content to users who may be interested in. With those users accessing the content locally, the poential retransmission could be avoided and thus it could save energy from carrier's view while providing the same real time experience to the users. In this paper, we propose a set of algorithm for the optimization of broadcasting scheme for the CASoRT system to achieve more energy efficiency.

In this paper, we discuss a fairness energy algorithm for the M2M renewable network. The algorithm mainly contains the flow control and transmission strategy and has low computational complexity. The aim is to reach an asymptotically optimal average network performance while keeping the fairness among nodes.

Cooperative spectrum sharing can effectively improve spectrum usage by allowing secondary users (SUs) to dynamically share the licensed bands with primary users (PUs). Meanwhile, an SU can relay a PU's traffic to improve the PU's effective data rate. In this paper, we consider a sequential spectrum bargaining process to achieve cooperative spectrum sharing between one PU and one SU over multiple time slots. The SU may be a Low type or a High type, depending on its energy cost. Such information is private to the SU and is unknown to the PU. We model such a dynamic bargaining with incomplete information as a dynamic Bayesian game, and characterize several types of equilibria under different system parameters. In particular, we show that a Low type SU may maximize its total utility by utilizing the reputation effect, i.e., rejects profitable offers initially in order to create the reputation of a High type SU.

This paper studies a spectrum sharing scenario between a cooperative relay network (CRN) and a nearby ad-hoc network. In particular, we consider a dynamic spectrum access and resource allocation problem of the CRN. Based on sensing and predicting the ad-hoc transmission behaviors, the ergodic traffic collision time between the CRN and ad-hoc network is minimized subject to an ergodic uplink throughput requirement for the CRN. We focus on real-time implementation of spectrum sharing policy under practical computation and signaling limitations. In our spectrum sharing policy, most computation tasks are accomplished off-line. Hence, little real-time calculation is required which fits the requirement of practical applications. Moreover, the signaling procedure and computation process are designed carefully to reduce the time delay between spectrum sensing and data transmission, which is crucial for enhancing the accuracy of traffic prediction and improving the performance of interference mitigation. The benefits of spectrum sensing and cooperative relay techniques are demonstrated by our numerical experiments.

In this paper we propose a novel algorithm to predict the performance of interleaver division multiple access (IDMA) systems, which is called mutual information evolution algorithm (MIEA). The proposed MIEA is a semi-analytical technique, which is based on but a little different from the traditional extrinsic information transfer (EXIT) chart especially when used for the successive interference cancellation (SIC) structure in IDMA systems. Based on MIEA, not only the convergence behavior of iteration structures in IDMA systems can be analyzed, but also the trade-off between code-rate (CR) and spreading-factor (SF) can be obtained. Moreover, the MIEA can be used to search the optimal codes in IDMA. Simulation results are also presented to confirm our analysis.

In this paper, we study a cognitive coexistence strategy for heterogeneous networks. While a lot of previous works focused on spectrum underlay approaches for weak interference scenarios, a high power infrastructure (IS) transmitter creates a large dead zone for nearby ad-hoc (AH) links using the same spectrum. To address this problem, we propose to utilize a half-duplex decode-and-forward (DF) relay node to assist the IS transmitter. The transmission time and power of relay-assisted IS network is optimized to reduce its generated interference while still guaranteeing its quality-of-service (QoS) level. The resource allocation problem of relay-assisted IS network is formulated as a convex optimization problem, for which a tailored dual optimization method is proposed. Our numerical results show that our relay-assisted scheme can produce less interference and/or achieve a higher QoS level.

Creating GUI can be a time and money consuming work in application development. In AUTOSAR methodology for automotive electronics software development, configuration GUI with a large number of configuration items is required in the ECU (Electronic Control Unit) configuration step. To reduce the development time and cost, and meet the requirement of future update of AUTOSAR standard, we present a way for automatic GUI generation of AUTOSAR ECU configuration tool. Our approach adds the configuration information to class models in form of annotations and binds the configuration type and attributes information with configuration values implicitly. Based on the annotated models, the Configuration GUI can be generated. With the approach, our ECU configuration tool has been developed with great enhancement of efficiency, scalability and upgradeability.

In this paper, we study the joint power and channel resource allocation problem for a multiuser F/TDMA decode-and-forward (DF) relay network under per-node power constraints and a total channel resource constraint. Our goal is to maximize the total throughput achieved by the systems. To that end, we formulate a joint power and channel resource allocation problem. We develop an iterative optimization algorithm to solve this problem, whose convergence and optimality are guaranteed. Due to the per-node power constraints, more than one relay node may be needed for a single data stream. Our solution also provides a way of finding the optimal relays among the assisting relay nodes.

In this paper, a new nonlinear Refined Extended Chirp Scaling (RECS) imaging algorithm is proposed for spaceborne ScanSAR with large cell migration to resolve the effects of residual cubic phase error in the deducing of the traditional RECS algorithm. The algorithm achieves cubic phase error correction of RECS by nonlinear filter to improve the spaceborne ScanSAR image qualities. The full derivation and the realizing approach of the algorithm are presented. And the algorithm is verified with simulations.

A computationally efficient optimal discrete bit allocation algorithm is proposed for medium and high target bit rate discrete multitone (DMT) transmissions. Unlike conventional greedy bit-loading algorithms, which compare the incremental transmission power of multicarriers bit-by-bit and allocate one bit at a time, the proposed algorithm is based on parallel bit-loading with reference to a virtual equal power assignment bit allocation. After maximum bit rate allocation, the algorithm achieves the target bit rate by two types of parallel bit- loading. If the target bit rate allocation is not optimal, bit swapping is performed to obtain the optimal solution. Simulation results using the standard asymmetric digital subscriber line (ADSL) test loops demonstrate the reduced computational load of the proposed algorithm compared to the existing optimal discrete bit allocation algorithm when the target rate is between 60% and 99% of the loop's maximum bit rate.

Routing in large-scale multi-radio wireless mesh networks (WMNs) is facing two challenges in achieving a high throughput. One is the long path between the source and the destination, and the other is the high routing overhead. We study the both aspects and develop our schemes accordingly. Firstly, a new routing metric for selecting multi-channel routes with maximum end-to-end capacity is presented. Secondly, a feedback based algorithm to maximize the control message broadcasting interval is proposed to minimize the routing overhead, offering more residual capacity for data traffic while keeping the routes reliable. Both the theoretic analysis and simulation experiments demonstrate the effectivity of our proposals.

Connected dominating set (CBS) has been proposed as virtual backbone or spine of wireless ad hoc networks. Most literatures take the same assumption that the ad hoc network is distributed in a 2-dimensional plane. In this paper, the characters of CBS structure are deduced to 3-dimensional space, and a distributed approximation algorithm is analyzed for ad hoc network, whose performance ratio is proved to be 16 at most, in the case of 3-dimensions.

The stable adaptive routing protocol (SAR) is a novel on-demand stable adaptive routing protocol for ad hoc network proposed last year, which considers joint route hop counts, node stability and route traffic load balance as the route selection criteria in routes searching and maintain. This paper describes the performance evaluation of SAR protocol by a practical ad hoc network test-bed under typical indoor and outdoor environments. And compared to AODV routing protocol, SAR has better performance on system cost, successfully delivery ratio and average delay jitter, as shown in the measurement results.

Routing algorithm has been the challenge in the wireless ad-hoc network for a long time because of the quick mutability of the network topology introduced by the mobility of nodes. AODV is appealing for ad hoc networks as an efficient on-demand routing protocol because of the low routing overhead and high performance. As an optimization for the current AODV, a novel stable adaptive enhancement for AODV routing protocol was proposed in this paper, which considers joint route hop count, node stability and route traffic load balance as the route selection criteria. Comparing with AODV protocol, the new algorithm has better performance as shown with the simulations results.