This paper investigates a covert and reliable short-packet communication system with movable antenna (MA). Covertness is achieved through the uncertainty introduced by short-packet transmission, but the uncertainty will also introduce unreliability. To this end, we formulate an optimization framework that utilizes MA to effectively balance covertness and reliability. Specifically, the minimum effective throughput is maximized by jointly designing beamforming and antenna positions under covertness requirements. The simulation results demonstrate that the system with MA can achieve higher reliability with less resources utilization and higher transmission rate than the systems with fixed position antennas (FPA). Additionally, the effective throughput is improved by approximately 5.7 dB.

Reconfigurable intelligent surface (RIS) is a promising technique for positioning systems. A large number of reference signals is needed for traditional base station (BS) with RIS localization system which occupy communication resources. To solve this problem, we directly use the DMRS (Demodulation Reference Signal), which does not consume additional communication resources to estimate the user equipment (UE) position. In this paper, we consider a RIS-assisted downlink localization system that takes account for the channel time variability caused by UE mobility. To estimate the UE position, DPSCN (Depthwise Separable Convolution) network consisting of three stages is adopted to capture the relationship between the received signal and the UE position.Based on the estimated position, the RIS phase is dynamically adjusted, to achieve accurate and continuous tracking of the UE. Extensive simulation results are also presented to demonstrate that the proposed DPSCN learning algorithm can estimate the UE position accurately and outperforms the traditional algorithms.

In the sixth generation (6G) Internet of Things (IoT), integrated sensing and communication (ISAC) emerges as a key technology, which is expected to significantly enhance the perception capabilities and spectrum efficiency of base stations (BSs). It holds potential for applications in unmanned aerial vehicle (UAV) monitoring, vehicle positioning, and crowd detection. However, developing an integrated waveform that efficiently conserves spectrum resources while maintaining lower complexity remains challenging. This paper designs an ISAC system that utilizes orbital angular momentum (OAM) waves generated by a uniform circular array (UCA), which enhances communication and sensing capabilities by allocating distinct modes. This paper proposes a multimode multiplexing communication scheme based on a single UCA and an OAM-circular reception method (OAM-CRM) for the two-dimensional direction-of-arrival (DOA) estimation of targets, encompassing both azimuth and elevation angles. Additionally, an OAM-different modes (OAMDM) algorithm is designed to optimize communication and sensing performance across various mode sets. Simulation results verify the effectiveness of the OAM ISAC system and demonstrate the superior performance of the proposed algorithms compared to conventional methods.

Coordinated Multi-Point (CoMP) transmission enhances the reliability and throughput of communication systems. However, its reliance on precise CSI feedback and retransmission introduces undesirable delays, limiting its applicability in Ultra-Reliable Low Latency Communication (URLLC). To address this challenge, we propose an enhanced RaptorQ-based downlink CoMP scheme that optimizes the decoding process. This scheme utilizes a limited number of Belief Propagation (BP) iterations and a self-adaptive Ordered Statistics Decoder (OSD) to reconstruct ordered information sequences based on the accumulated Log-Likelihood Ratio (LLR) transitions of variable nodes. Simulations demonstrate an average 38.2 % improvement in resource utilization compared to traditional CoMP schemes under a Block Error Rate (BLER) of 10–5in low Signal-to-Noise Ratio (SNR) scenarios.

Orbital angular momentum (OAM) multiplexing techniques have great potential in high-speed and high-capacity short-range wireless communication. However, the divergence angle of OAM waves changes with mode and frequency, increasing the receiver complexity. This paper proposes a Bessel constraint method for generating OAM waves with the same divergence angle. Specifically, this paper first analyzes the factors affecting the divergence angle by combining two uniform circular arrays (UCAs) as uniform concentric circular arrays (UCCAs). Then, this paper defines the intensity proportion between the two UCAs to analyze constraint conditions. Additionally, two algorithms are designed to generate multimode OAM waves with equal divergence angles, considering scenarios with and without multiple frequency bands. Simulation results demonstrate the effectiveness of these algorithms in generating OAM waves with matching maximum strength ring radii, enabling the receiver to fully receive OAM waves of multiple modes and frequencies with just one UCA. Furthermore, the proposed approach facilitates the adjustment of OAM waves with different modes and frequencies by flexibly modifying the intensity proportion without necessitating alterations to the antenna radius.

With the rapid development of artificial intelligence, computing energy consumption has become a research focus. Due to privacy protection, it is difficult to exchange data between participants, which leads to repeated training of the same model for different clients. This paper aims to reduce the computing energy consumption caused by repeated training of the same model. This paper first proves the effect of Federated Learning (FL) on lowering computing energy consumption. Then considering the client computing architecture, this paper proposes an FL algorithm for the energy-efficiency-ratio-based client selection (FedECS). The proposed algorithm reduces the total energy consumption of FL through the trade-off of iteration rounds and energy consumption per round. In addition, this paper performs extensive simulations, including different data partitions and computing architecture distributions. Experiments show that compared with the FedAvg algorithm, the proposed algorithm can reduce energy consumption by 17.2% ∼ 48.2%.

Gesture awareness plays a crucial role in promoting human–computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability, and reliability. In this article, we propose CentiTrack, the first centimeter-level passive gesture-tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the channel state information (CSI) measurement error sources in the physical-layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal component analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of the dynamic path reflected off the hand. In addition, we adopt the multiple signal classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range, and device cost, compared with the state-of-the-arts.

Recently, commodity Wi-Fi devices have been shown to be able to construct human pose images, i.e., human skeletons, as fine-grained as cameras. Existing papers achieve good results when constructing the images of subjects who are in the prior training samples. However, the performance drops when it comes to new subjects, i.e., the subjects who are not in the training samples. This paper focuses on solving the subject-generalization problem in human pose image construction. To this end, we define the subject as the domain. Then we design a Domain-Independent Neural Network (DINN) to extract subject-independent features and convert them into fine-grained human pose images. We also propose a novel training method to train the DINN and it has no re-training overhead comparing with the domain-adversarial approach. We build a prototype system and experimental results demonstrate that our system can construct fine-grained human pose images of new subjects with commodity Wi-Fi in both the visible and through-wall scenarios, which shows the effectiveness and the subject-generalization ability of our model.

Device-free gesture recognition plays a crucial role in smart home applications, setting human free from wearable devices and causing no privacy concerns. Prior WiFi-based recognition systems have achieved high accuracy in a static environment, but with limitations in adapting changes in environments and locations. In this paper, we propose a fine-grained deep adaptation networks based gesture recognition scheme (DANGR) using the Channel State Information (CSI). DANGR applies wavelet transformation for amplitude denoising, and conjugate calibration to remove CSI time-variant random phase offsets. A Generative Adversarial Networks (GAN) based data augmentation approach is proposed to reduce the large consumptions of data collection and the over-fitting risks caused by incomplete dataset. The distribution of CSI in various environments may be biased. In order to shrink these domains discrepancies in environments, we adopt domain adaptation based on multikernel Maximum Mean Discrepancy scheme, which matches the mean-embeddings of abstract representations across domains in a reproducing kernel Hilbert space. Extensive empirical evidence shows that DANGR yields mean 94.5% accuracy of gesture recognition confronting environmental variations, providing a promising scheme for practical and long-run implementation.

Mobile Edge Computing (MEC) has potential to improve performance of 5G networks in terms of latency and bandwidth. Recently, the architecture of distributed MEC host integrated with 5G networks is proposed by ETIS, providing a new paradigm for implementing edge networks. In this paper, considering the performance optimization and user mobility in edge networks, Docker container is applied to MEC host to form a container-based multi-MEC collaboration system (CMMCS) so that it can optimize computation resources among MEC hosts while guaranteeing QoS. Then the resource allocation optimization is formulated to the system cost minimization and the general pattern of virtual machine resource consumption is considered. After that, we prove that the system cost minimization problem is an NP-hard problem, and a DABF scheme based container deployment algorithm is proposed to figure out the optimal solution. Simulation results demonstrate that our proposed algorithm achieves significant reduction on system cost in CMMCS compared to other baselines in any network environment.

In this paper, we consider the problem of interference mitigation in the downlink of multicell networks via base station coordination. In this paper, a simple and efficient scheme for interference management based on potential game is proposed. The main emphasis of this paper is placed on the problem of users' quality of service (QoS) in order to maximize the efficient throughput of system. Meanwhile, a pricing factor is introduced which is proportion to the co-channel interference to other base stations. Furthermore, an improved gradient projection rule with variable step size and Jacobi iterative algorithm are utilized to solve the optimization problem. Pareto optimal is verified by using "price of anarchy" as an optimize performance indicators in potential game. Simulation results show that our proposed scheme can significantly improve the performance of multicell networks.

In this paper, a novel resource allocation with QoS guarantee and fairness (RAQF) scheme is considered for orthogonal frequency division multiple access (OFDMA) two-way relay networks. Multiple source nodes exchange information with the assistance of an amplify-and-forward (AF) relay node (RN) in the presence of an eavesdropper. A joint power allocation, subchannel allocation and subchannel pairing scheme is proposed, aiming to maximize the secrecy capacity for legitimate users, and fulfill the diverse quality of service (QoS) requirements for different users and guarantee the fairness. RAQF scheme formulates the optimization problem into a mixed integer programming problem, and then solve it with constrained particle swarm optimization (CPSO), binary CPSO (B CPSO) and classic Hungarian algorithm. Simulations are conducted to assess the effectiveness of the proposed strategies.

With the sharp increasing demand of the indoor coverage and network capacity, dense femtocells are the ultimate target of the femto cellular network deployment in urban environment However, dense femtocell deployment could introduce excessive inter-femtocell interference (IFI). To address this problem, this paper proposes a distributed power self-optimization scheme for the downlink of dense femtocell networks. First, a non-cooperative power self-optimization game framework is established based on the exact potential game, which is demonstrated to converge to a pure and unique Nash Equilibrium. Then, a novel convex pricing mechanism is presented to price the transmit power of femtocells. Finally, combined with Firefly Intelligence Optimization (FIO), an effective power self-optimization algorithm with guaranteed convergence is proposed to achieve the Nash Equilibrium. With practical LTE parameters and 3GPP dual-strip femtocell model, simulation results show that the proposed scheme dramatically reduce femtocells transmit power while improving the throughput of dense femtocell networks compared to the scheme with non-linear pricing.

Due to the individualistic nature of the femtocells and the uncertainty on the number and location of these devices, self-organization techniques play a very important role in successfully deploying and managing a large femtocell tier. This paper proposes a distributed power self-optimization scheme to suppress the interference and optimize the energy efficiency. First, the non-cooperative power self- optimization game is established, which is demonstrated to converge to a pure and unique Nash Equilibrium. Then, the impact of different power constraint conditions on the equilibrium is analyzed. Finally, a distributed power self- optimization algorithm is presented to achieve the equilibrium. With practical LTE parameters and 3GPP dual-strip femtocell model, simulation results show that the proposed scheme has fast and stable convergence and improves the energy efficiency significantly compared with Iterative Water Filling (IWF) algorithm. Furthermore, two power constraint conditions manifest different performance in terms of the energy efficiency.

This paper presents a game-theoretic approach of power allocation in two-tier OFDMA femtocell networks. A dualutility based non-cooperative game with pricing is formulated for effectively allocating power on each sub-channel, targeting at maximizing energy efficiency for each macro BS and protecting the SINR for each femto BS for customers' interests, under power constraints in each sub-channel. The existence of a unique equilibrium for the proposed game is studied, and the impact of tunable coefficients of the proposed utility function on the equilibrium is investigated theoretically. An effective distributed power allocation algorithm with firefly algorithm (FA) theory is proposed to achieve the equilibrium. Furthermore, how to set the proper parameters of FA for the utility function is studied through numerical analysis. Simulation results show that the proposed scheme can improve energy efficiency of macrocell networks and mitigate the cross-and co-tier interference significantly while guaranteeing target QoS of femtocell users.

Due to large-scale and self-deployed of femtocells overlaid on traditional cellular networks, serious cross-and co-tier interference may arise without good coordination. To address this problem, this paper proposes a distributed power control scheme for the uplink transmission of two-tier OFDMA femtocell networks. Two models are considered: single-channel femtocell networks and multiple-channel femtocell networks. In single-channel case, each user announces a price that reflects compensation paid by other users. Then an asynchronous distributed power control algorithm is presented to update power levels and prices. Moreover, a simple cellular link protection scheme is also proposed for protecting cellular users by increasing their prices. In multiple-channel case, this paper studies power control scheme based on Stackelberg game, in which each user prices its limited power and sells it to subchannels. Simulation results show that the proposed scheme can improve spectrum efficiency and mitigate the cross- and co-tier interference significantly while guaranteeing QoS (quality of service) requirement of cellular users.

Interference management is one of the most important issues in two-tier femtocell networks in which a central macrocell underlaid with several femtocell hotspots. To address the cross-tier and co-tier interference, this paper introduces an energy-efficient power control algorithm via convex pricing scheme, where both circuit and transmit power have been considered. To achieve the target SINR, the macrocell protects itself by actively adapting its interference cap on the total interference they are willing to tolerate and pricing the interference from the femtocell user equipments (FUEs), while the FUE is to maximize its own throughput and keep the fairness with the other FUEs. Then, the existence and uniqueness of the equilibrium for the proposed scheme are studied. Simulation results show that the proposed scheme can improve energy efficiency of femtocell networks and mitigate the crossand co-tier interference significantly while guaranteeing the minimum QoS requirement of macrocell users.

In this paper, a novel joint resource allocation including sub-channel scheduling and power control is proposed for downlink multi-cell orthogonal frequency division multiple access (OFDMA) networks. We provide a utility function consisting of energy efficiency and interference pricing, in which power and sub-channel resources have been jointly considered. To reduce the complexity of the joint resource allocation, a novel iterative two-step algorithm is presented. First, we utilize a non-cooperative supermodular game in power control given the sub-channel scheduling. Then we schedule sub-channels to maximize the utility function given the power allocation. Because power control and sub-channel scheduling share the same utility function, the iterative algorithm can be proved to converge to the best response pair which achieves the higher throughput and better utility. The numerical results are provided to evaluate the performance of the proposed algorithm and show that both throughput and energy efficiency can be improved compared with traditional methods.

Power control techniques are becoming increasingly important for a two-tier femtocell network, where a central macrocell underlaid with femtocells, since femtocells may cause serious interference and consume large amounts of energy for large scale application. In this paper, we investigate a distributed energy-efficient power optimization scheme for the uplink transmission of a spectrum-sharing femtocell network based on fictitious game, in which each user announces a price that reflects compensation caused by other users for their interference. Then we present an asynchronous distributed power optimization algorithm, where power and price update are finished at terminal and base station (BS), respectively. Furthermore, we proposed a macrocell link protection scheme that macrocell user (MUE) protects itself by increasing its price. That is, the femtocell user (FUE) that causes more interference to macrocell base station (MBS) will pay more for its interference. According to numerical results, the proposed scheme can improve energy efficiency and mitigate interference significantly.

In this paper, we consider the problem of secure resource allocation in orthogonal frequency division multiple access (OFDMA) two-way relay networks. Multiple sources exchange information with the assistance of an amplify-and-forward (AF) relay node in the presence of an eavesdropper. The joint subcarrier allocation, subcarrier pairing and power allocation problem aims to maximize the secrecy capacity for legitimate sources subject to limited power budget and orthogonal subcarrier allocation constraints. The optimization problem is modeled as a mixed integer programming problem, and then solved in an asymptotically optimal manner based on the dual method. Moreover, a suboptimal algorithm is proposed to reduce the complexity. Simulations are conducted to evaluate the effectiveness of the proposed near optimal and suboptimal algorithms.

In this paper, we consider the joint subchannel and power allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing macrocells and femtocells. A joint subchannel and power allocation scheme for co-channel femtocells is proposed, aiming to maximize the capacity for delay-tolerant users subject to delay-sensitive users' quality of service and interference constraints imposed by macrocells. The joint subchannel and power allocation problem is modeled as an mixed integer programming problem, then transformed into a convex optimization problem by relaxing subchannel sharing, and finally solved by a dual decomposition approach. The effectiveness of the proposed approach is verified by simulations and compared with existing scheme.

Macrocells may suffer serious uplink interference introduced by the deployment of co-channel femtocells. In this paper, an interference-aware pricing-based resource allocation algorithm for co-channel femtocells is proposed to alleviate their interference to macrocells without degrading the femtocell's capacity. The subchannel and power allocation problem is modeled as a non-cooperative game. A suboptimal subchannel allocation algorithm and an optimal power allocation algorithm are proposed to implement the resource allocation game. Simulation results show that the proposed algorithm not only improves the capacity of the macrocell but also the total capacity of the two-tier network, as compared with the unpriced subchannel allocation and Modified Iterative Water Filling (MIWF) power allocation algorithm.

A major challenge in the two-tier femtocell networks-comprised of a central macrocell underlaid with femtocells is the efficient use of radio resources. Power optimization technique is one important component of radio resource management, since the deployment of femtocells may cause serious cross-tier interference and consume large amounts of energy. This paper presents an energy-efficient power control solution for interference-limited two-tier femtocell networks in the analytical setting of a game theoretic framework, where both circuit and transmission powers have been considered. First, we investigate a non-cooperative power control game for two-tier spectrum-sharing femtocell networks. In order to guarantee that cellular user can obtain its Signal-to-Interference-Pius-Noise Ratio (SINR) target, then we propose an algorithm that reduces transmission powers of the strongest femtocell interferers. According to simulation results, the proposed scheme can improve energy efficiency of femtocell networks and mitigate cross-tier interference significantly.

In order to suppress cross-tier interference (CTI) and inter-cell interference (ICI) between adjacent macrocells, this paper proposes an effective resource allocation scheme for the two-tier OFDMA femtocell networks, in which macro BSs adopt the soft frequency reuse (SFR) strategy. Consider that macrocell users (MUEs) are prior to femtocell users (FUEs), firstly the subbands are allocated to MUEs utilizing SFR for the ICI mitigation, based on which the sub-bands are efficiently allocated to femocells for CTI mitigation. The proposed scheme is optimized in terms of energy efficiency, while guarantees that both MUEs and FUEs attain at least a given data rate, especially for cell-edge MUEs. With a three dimensional two-tier femtocell network model and practical LTE parameters, simulation results show that the proposed scheme rivals other two prominent related schemes on the CTI and ICI suppression, while outperforms them on energy efficiency for wireless overlay networks.

A large-scale deployment of femto BSs (FBSs) will result in the substantial energy consumption. One fairly intuitive way to improve energy efficiency for femtocells is a sleep mode where the FBS periodically transmits pilot signal especially in low traffic scenario. Nevertheless, it is inefficient when not involved an active call in the FBS coverage area. To address this issue, this paper proposes a dynamic FBS-cooperative power management algorithm (FCPMA) for virtual cell based femto network, where the FBS without the active user can entirely switch off pilot transmissions and the related processing all the time. Based on the proposed scheme, the state transition model is established, and the analytic formulas of energy consumption and average cumulative delay are derived. With the practical LTE system parameters, the numerical simulation and the theoretical analysis match pretty well. Furthermore, the tradeoff relation between energy consumption and average cumulative delay is also showed. The results provide some guidelines for deploying energy efficient femtocell networks.

In two-tier femtocell networks, where a central macrocell underlaid with femtocells, femtocells may cause serious interference and consume large amounts of energy for its large-scale application. To solve the problem this paper presents an energy-efficient power optimization for two-tier femtocell networks in the analytical setting of a game theoretic framework, where both circuit and transmission power have been considered. Notice that the Nash equilibrium of the game is not efficient. We introduce pricing of interference, so as to obtain Pareto improvement of the non-cooperative power control game. In order to protect cellular user from severe cross-tier interference and guarantee that cellular user can obtain its Signal-to-Interference-Plus-Noise Ratio (SINR) target, we propose an algorithm which reduces transmit powers of the strongest femtocell interferers. According to simulation results, pricing can improve user utilities significantly.

In order to mitigate inter-femtocell interference (IFI) and utilize spectrum resource efficiently, this paper proposes a dynamic clustering-based sub-band allocation scheme (DCCSA). Firstly, DCCSA constructs a weighted interference graph base on the user measurement report mechanism. Secondly, according to the interference graph, the disjoint IFI-minimizing clusters are formed by a Max k-Cut clustering algorithm. Thirdly, a SINR- maximizing heuristic algorithm assigns sub-bands to each cluster. Finally, due to the time variation characteristic of the femtocell networks, a cognitive sub-band self-management mechanism is provided, especially to allocate the sub-band for the newly added femtocell, a reuse entropy based hybrid centralized/distributed interference-aware sub-band allocation algorithm is presented. Simulation results show that the DCCSA scheme can improve the throughput of femtocells and suppress the IFI significantly while guarantee the required QoS.

We proposed a novel cross-layer optimization approach for video streaming applications in orthogonal frequency division multiplexing (OFDM) based networks. Parameters in media access control (MAC) layer and physical layer are optimized jointly in a cross-layer framework. The main objective is to maximize video quality by reducing the end-to-end distortion in application layer for each user. Potential games theory is adopted to solve this cross-layer problem in distributed way. Convergence of game is guaranteed by gradient projection, which reduces the time complexity of optimization considerably. Simulation results have confirmed that the gradient projection based cross-layer optimization can significantly enhance the video quality performance with low time complexity and high scalability.

This paper proposes a no-reference video quality estimation model over burst loss wireless networks. The estimation model is an end-to-end, cross-layer framework which considers two parts as feature extraction and quality prediction. The first part considers content-aware parameters obtaining from rebuilt video sequences, transmission-aware parameters getting from network layer and encoding parameters which are set at application layer. Firstly, for the content-dependent parameters, the temporal and spatial features are extracted representing the videos' nature. Then, for transmission-aware features, Principal Component Analysis (PCA) is used to reduce the number of parameters to give high prediction accuracy in test with low training costs. Frame Rate (FR) and Sent Bit Rate (SBR) are selected as the encoding features which bring the effects from quantization. In the second part, Support Vector Machine (SVM) is provided by using all cross-layer parameters to make a tradeoff between accuracy and learning ability. Results show that the prediction values are well correlated with subject scores with Pearson coefficient of 0.86 at least.

Femtocell is a small access point using the wire broadband connections or wireless technologies to access the mobile operator's network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for wireless communication system.Femtocell related handover cost reduction is one of the important targets in LTE-Advanced SON (Self-Organising Networks). In this paper, a handover optimization algorithm based on the UE's mobility state is proposed. An analytical model was presented for the handover signalling cost analysis. Numerical results are provided to compare the signalling cost of different handover management schemes. The comparison between the proposed algorithm and the traditional handover control algorithm shows that the algorithms proposed in this paper have a significant reduction in the signalling overhead.

Home eNodeB (HeNB) is a low-power access point using the local broadband connections or a separate RF backhaul to access the mobile operator's network for the user equipment(UE), which can provide better indoor coverage and satisfy the upcoming demand of high data rate for users. Considering the potential frequent mobility between HeNB-HeNB and HeNBeNB, HeNB mobility enhancement is proposed as one of the most important work items in 3GPP LTE-Advanced. In this paper, four X2 interface based HeNB mobility enhanced architectures are explicitly discussed in terms of signalling overhead evaluation. The numerical results show that the direct-X2 based option 2 in HeNB-HeNB scenario and the X2-GW based option 3 in eNB-HeNB scenario have the best trade-off in signalling overhead and complexity respectively.

Both SIP and various peer-to-peer protocols have been widely deployed, there is little operational experience with SIP using overlay networks. Also, some researches have proposed to leverage peer-to-peer computing to control multimedia sessions in a decentralized manner (P2PSIP) [1]. However, most proposals in research only focused on the design of P2PSIP protocols using one specific DHT overlay. They donpsilat take full account of the DHTs performance requirements for a P2PSIP system and the differences between various DHTs. In this paper we will list the key properties of the DHTs and give a performance evaluation and simulation of the representative DHTs that have been proposed to be used in P2PSIP by IETF.

In this paper, a new method for detecting shot boundaries in video sequences using box-counting to extract texture feature and judging the shot boundaries by an improve method to get a dynamic threshold is proposed. Many techniques have been developed to detect the video shot boundaries. But automatic shot boundary detection is difficult. In particular, gradual transitions are generally more difficult to detect. So the paper use the box-counting method to extract the texture feature. Then the paper use the improve method to get the dynamic threshold to detect the shot boundaries. The dynamic threshold is the average frame difference in a slide window multiplied by a threshold coefficient. Experimental results successfully validate the paper method and show that it can effectively detect both the cuts and gradual transition. In particular, the performance is better than others method when the gradual transition shot boundaries are detected.