Future sixth generation (6G) communications are expected to support robotic control tasks in applications such as industrial automation and emergency response, where sensors, computing units, and robots are interconnected via nervous system-like networks to form sensing-communication-computingcontrol (SC 3 ) closed loops.However, the limited battery capacities of devices within these SC 3 loops constrain operational duration and degrade control efficiency, particularly in remote or postdisaster scenarios.To address this challenge, wireless power transfer (WPT) can be leveraged to provide continuous energy supply for SC 3 closed loops.In this paper, we investigate a wireless-powered SC 3 system, where a satellite transfers energy via radio frequency (RF) signals to support the communication and computing processes of multiple SC 3 closed loops.By accounting for the intricate coupling among computing, communication, and energy transfer, we propose a holistic design framework to enhance overall control performance.Specifically, we adopt the linear quadratic regulator (LQR) cost as the performance metric and formulate a sum LQR cost minimization problem.The uplink/downlink transmit power, bandwidth allocation, computing capability, communication/computing time allocation, and WPT power allocation are jointly optimized.We recast the problem into a more tractable form and develop an iterative algorithm to solve it.For the special case of a single loop, we further analyze the properties of optimal solutions in energylimited scenarios to provide insights for practical parameter configuration.Simulation results demonstrate the performance gains of the proposed scheme.

This paper investigates a multi-satellite cooperative covert satellite communication (SatCom) scheme, where a positive covert rate is achieved in ultra-dense low Earth orbit (LEO) satellite constellations by exploiting both interference and directional uncertainty. Specifically, interference arises from aggregate sidelobe leakage from other satellite transmissions, while directional uncertainty stems from the random selection of the transmitting satellite among multiple accessible ones. To this end, we first propose a LEO satellite network model and formulate the corresponding hypothesis testing problem for the cooperative covert SatCom scheme. The power distribution of the aggregate interference is quantified and approximated using stochastic geometry. Next, by analyzing the detection error probability and outage probability under the impact of aggregate interference, we derive an approximate covert capacity expression for the case of a single accessible satellite, which maintains a positive covert rate even as the slot length approaches infinity. Furthermore, by leveraging directional uncertainty through hiding the signal’s angle of arrival, we analyze the multi-satellite cooperative covert SatCom scheme, leading to a concise approximate expression that reveals significant covert capacity improvement. Numerical simulations are performed to verify the superiority of the proposed scheme, suggesting that a positive covert capacity can be achieved with interference uncertainty and significantly enhanced by directional uncertainty as the number of satellites increases.

The covert communication scenario with multiple users involved needs to take into account multiple conflicting optimization goals, such as maximizing the number of served users, minimizing service energy consumption, and balancing resource allocation. And the UAV-assisted multi-user communication is a typical knapsack traveling salesman problem, which is difficult to solve because of its large solution space and NP-hard nature. Therefore, to address this, a novel UAV-assisted multi-user downlink covert communication scenario is considered. To find an optimal balance of the maximum transmission rate and the minimum energy consumption, a low-complexity solution is proposed. First, a closed-form solution for the hovering position and transmission power of the UAV is deduced. Then, the user selection and trajectory planning results from the heuristic algorithm are used to initialize the deep reinforcement learning (DRL) agent, providing a superior starting point for the subsequent policy search. This strategy combines the fast convergence of heuristics with the dynamic adaptability of DRL, resulting in a final design that is more efficient and lightweight than conventional, pure DRL-based methods. The simulation results demonstrate that the proposed scheme improves energy efficiency by at least 40% compared with the benchmark scheme.

Task-Oriented Semantic Communication (TOSC) has been regarded as a promising communication framework, serving for various Artificial Intelligence (AI) task driven applications. The existing TOSC frameworks focus on extracting the full semantic features of source data and learning low-dimensional channel inputs to transmit them within limited bandwidth resources. Although transmitting full semantic features can preserve the integrity of data meaning, this approach does not attain the performance threshold of the TOSC. In this paper, we propose a Task-oriented Adaptive Semantic Communication (TasCom) framework to effectively facilitate the inference of different AI tasks. Based on the Generative AI (GAI) techniques, we first propose a Joint Source-Channel Coding (JSCC) that which only extracts and fuses task-related semantic features, and then transmits them to achieve efficient task-oriented semantic transmission. Then, we propose a generative training algorithm to train the proposed JSCC for optimal performance. Furthermore, an Adaptive Coding Controller (ACC) is proposed to find the optimal coding scheme for the proposed JSCC, which allows the semantic features with significant contributions to the task inference to preferentially occupy limited bandwidth resources for wireless transmission. The simulation results show that the proposed TasCom outperforms the existing TOSC and traditional codec schemes on the object detection and instance segmentation tasks under all considered channel conditions.

In this paper, coding for the covert computation over Gaussian multiple-access channel (GMAC) with feedback is investigated, where a receiver wishes to decode a function of the sources transmitted over the GMAC while ensuring a low probability of detection by a warden. Traditionally, in covert communication, the feedback from the receiver to the transmitters helps to share a secret key which is used to confuse the warden. This paper shows that a slight modification of the existing Schalkwijk-Kailath (SK) type feedback scheme for computation over GMAC satisfies covert constraint by itself, which indicates that the SK-type feedback coding schemes in the literature can also be viewed as covert computation/communication schemes for channels with feedback.

In the literature, it has been shown that feedback does not increase the optimal rate-distortion region of the dirty paper channel with state estimation at the receiver (SE-R). On the other hand, it is well-known that feedback helps to construct low-complexity coding schemes in Gaussian channels, such as the elegant Schalkwijk-Kailath (SK) feedback scheme. This motivates us to explore capacity-achieving SK-type schemes in dirty paper channels with SE-R and feedback. In this paper, we first propose a capacity-achieving feedback scheme for the dirty paper channel with SE-R (DPC-SE-R), which combines the superposition coding and the classical SK-type scheme. Then, we extend this scheme to the dirty paper multiple-access channel with SE-R and feedback, and also show the extended scheme is capacity-achieving. Finally, we discuss how to extend our scheme to a noisy state observation case of the DPC-SE-R. However, the capacity-achieving SK-type scheme for such a case remains unknown.

Unmanned Aerial Vehicle (UAV) swarms represent a transformative advancement in aerial robotics, leveraging collaborative autonomy to enhance operational capabilities. Co-operative positioning (CP) is fundamental for UAV swarms to precisely execute tasks such as swarm formation. Despite many theoretical works in literals, there still lack works on prototyping and experimental results. In this work, we report our progress on UAV swarm CP prototyping. The prototype includes UAVs and ground anchors. Each UAV is equipped with an ultra wide band (UWB) module and a visual-inertial system (VINS). For CP with other UAVs and ground anchors, a cubature transform-based belief propagation (BP) positioning algorithm is proposed for efficient message representation, merging, and approximation. An outdoor experiment is conducted with four UAVs and two ground anchors for performance evaluation. The experimental results show that the root mean square errors (RMSEs) of the four UAVs are 4.94 m, 3.74 m, 5.21 m, and 3.53 m over the 2 km roundtrip, during which the UAVs and the ground anchors lost contact at 0.5 km. The proposed BP-based CP algorithm outperforms the cubature Kalman filter (CKF) and extended Kalman filter (EKF) CP algorithms by approximately 10% and 16%, respectively.

To fully leverage communication resources for pervasive sensing within existing architectures, it is crucial to enable perception through the use of transmitted data. Unlike traditional waveform designs that focus exclusively on modulation, this paper introduces a novel approach starting from the information bit level. By applying controlled perturbations, approximate codewords are aligned with deterministic optimization waveforms, effectively suppressing sidelobe levels and enhancing data-driven sensing capabilities. Additionally, to address decoding impairments caused by these perturbations, we propose a closed-loop reception algorithm that progressively removes residual disturbances while maintaining decoding accuracy. Simulations confirm the algorithm’s effectiveness in balancing communication and sensing performance. Compared to current time-division processing technologies, such as 5G frame structure perception based on reference signals, the proposed waveforms more effectively resolves the trade-off between communication and sensing.

Covert communication is a significant scenario towards 6G, where the transmission between transmitter and receiver should be covert without being detected. In covert communication, the transmitter called Alice transmits signal to the receiver called Bob, and a detecter called Willie continuously observes its received signal. Alice should control the transmit power under a certain value to mislead Willie judging the received signal containing only White Gaussian Noise. Several works have been carried out focusing on the covert communication performance from the viewpoint of timeliness, throughput, etc. However, the covert relay communication, especially the UAV based relay in transparent forwarding manner (TFM) is scarcely considered. In this paper, we study a novel scenario for strict and deteriorative covert communication with the UAV based relay: 1) The transmission of both Alice and the UAV relay cannot be detected by Willie. 2) The UAV must be on the sight of Willie. The three-dimensional placement of the UAV, the transmit power of Alice, and the amplifier gain of the UAV with TFM are jointly optimized by geometric programming, where several Lemmas are also derived. Finally, the performance of the proposed algorithm is verified by extensive simulations.

This paper investigates covert communication in ultra-dense low Earth orbit (LEO) satellite systems, where the uncertainty of the aggregated interference formed by sidelobe leakages of satellite transmissions can be exploited to hide wireless signals from being detected by a warden. Specifically, we first propose a LEO satellite network model and the corresponding hypothesis testing problem for the covert satellite communication scheme, where the power distribution of the aggregated interference is quantified and then approximated by using stochastic geometry. After that, to analyze the impact of the aggregated interference, the covert transmission problem under constraints of the detection probability at Willie and the outage probability at Bob is formulated, where the achievable covert capacity is analyzed by considering different number of satellites N. Finally, numerical simulations are performed to verify the derived analytical results, which demonstrate that the covert capacity initially increases as$N$increases, while it becomes approximately proportional to$1/\sqrt{N}$for sufficiently large N.

In non-cooperative communications, blind recognition of channel codes is the key to recover information from noisy intercepted messages. In this paper, we develop a progressive and iterative approach to reconstruct the parity-check matrices of large QC-LDPC codes under high bit error rate (BER). Specifically, in order to reduce the impact of noisy bits and improve efficiency for large codes, a novel sparse vector recovery (SVR) method based on sub-matrix sampling is first introduced. Then, SVR is operated iteratively after employing intermediate decoding with the partially reconstructed parity-check matrix on the intercepted messages. At last, the full parity-check matrix is built up progressively. Experimental results on a code of length 16200 show that the maximum bearable BER of the proposed approach to fully reconstruct is around 3E-4, at least 100 times higher than previous works.

Satellite Internet (SI) is an essential component of 6G as an effective supplement to terrestrial Internet in the future. Owing to its relatively limited processing power and bandwidth, distributed denial of service (DDoS) attacks may cause substan-tial damage. Moreover, the complexity and difficulty of defense also increase due to the constantly changing topology scale of SI when supporting various applications. Therefore, path tracking must be maintained with high precision at different scales to realize protection at the boundary. However, the marking rate limits traditional packet marking methods, causing a decrease in the tracking accuracy when the topology is expanded. Therefore, we propose a scalable traffic path tracking (TPT) scheme. Firstly, a lightweight description of malicious traffic in the entire network is realized by improving a multi-dimensional traffic feature digesting method. Secondly, the connection relationship sparse matrix of nodes is obtained based on the Hidden Markov Model modeling of the network topology and attack scenarios. These generate the observation probability matrix B and transition probability matrix A of the traditional Viterbi algorithm. Finally, we optimize the Viterbi algorithm by adding an index matrix of the next-largest probability value to eliminate tracking loops, thereby achieving high tracking accuracy for topologies of different scales. The Keysight Ixia platform is used to generate malicious traffic in the experiments. The results demonstrate that the scheme can maintain a tracking accuracy of over 99 % against various DDoS attacks in topologies of different scales, which is more accurate and scalable than existing methods.

Abstract We present an overview of quantum-safe cryptography (QSC) with a focus on post-quantum cryptography (PQC) and information-theoretic security. From a cryptographic point of view, lattice and code-based schemes are among the most promising PQC solutions. Both approaches are based on the hardness of decoding problems of linear codes with different metrics. From an information-theoretic point of view, lattices and linear codes can be constructed to achieve certain secrecy quantities for wiretap channels as is intrinsically classical- and quantum-safe. Historically, coding theory and cryptography are intimately connected since Shannon’s pioneering studies but have somehow diverged later. QSC offers an opportunity to rebuild the synergy of the two areas, hopefully leading to further development beyond the NIST PQC standardization process. In this paper, we provide a survey of lattice and code designs that are believed to be quantum-safe in the area of cryptography or coding theory. The interplay and similarities between the two areas are discussed. We also conclude our understandings and prospects of future research after NIST PQC standardisation.

Satellite Internet (SI) dramatically expanded the ground-based Internet, and it is also the future direction of 6G. However, due to limited computing power and bandwidth resources, Distributed Denial-of-Service (DDoS) attacks can cause more severe damage to SI, and even paralysis of the entire network. Current DDoS defense mechanisms are built on abundant computing power and bandwidth resources, making applying in the SI scenario challenging. Aiming at protecting SI from DDoS attacks, a blockchain-based distributed collaborative entrance defense (DCED) framework is proposed, in which network traffic characteristics can be recorded and aggregated at the entrances of SI. The proposed framework consists of a distributed detection digesting procedure, a digest virtual aggregation procedure, and an entrance control strategy. The former procedure detects and extracts multidimensional characteristics of DDoS attacks and pushes them onto the blockchain. The latter procedure collects block data and aggregates attack features using the MapReduce algorithm and then compares them with baseline and gives an alert. The strategy completes the filtering and interception of traffic. Experiments use the IXIA platform to generate malicious traffic, and results show that the framework can accurately identify attack traffic within 1500 ms, reaching an area of 0.99 under the receiver operating characteristic curve. The proposed framework is more effective than other similar DDoS methods, protecting the precious SI bandwidth resources.

Convolutional neural network-based (CNN-based) method has shown its superior performance on the image super-resolution (SR) task. However, several researches have shown that obtaining a better reconstruction result often leads to the significant increase in parameters and computation. To alleviate the burden in computational needs, we propose a novel global-local adjusting dense super-resolution network (GLADSR) to build a powerful yet lightweight CNN-based SR model. To enhance the network capacity, we present a global-local adjusting module (GLAM) which can adaptively reallocate the processing resources with local selective block (LSB) and global guided block (GGB). The GLAMs are linked with nested dense connections to make better use of the global-local adjusted features. In addition, we also introduce a separable pyramid upsampling (SPU) module to replace the regular upsampling operation, which thus brings a substantial reduction of its parameters and obtains better results. Furthermore, we show that the proposed refinement structure is capable of reducing image artifacts in SR processing. Extensive experiments on benchmark datasets show that the proposed GLADSR outperforms the state-of-the-art methods with much fewer parameters and much less computational cost.

This paper proposes a maritime transmission scheme in integrated satellite-maritime networks, where the satellite multicast and relay cooperation cooperatively provide the ubiquitous broadband coverage for the whole sea area. Under the proposed network, the maritime users that request the same content or belong to the same fleet can be divided into one user group. Besides, the satellite provides multicast services for the all user groups, while the relay nodes retransmit the signal to other users to reduce the satellite communication cost of user group. However, since the network performance is strongly correlated with satellite links and relay nodes, it is difficult to find out the optimal cooperation solution with low complexity. Meanwhile, the user groups work in the same carrier frequency, which inevitably causes the co-channel interferences among groups. Considering these factors, we propose a cooperative access and power allocation algorithms, respectively. The cooperative access algorithm is based on channel quality and considers the channel quality of satellite and relay node simultaneously. The simulation results show that the proposed scheme can effective improve system performance compared with existing scheme.

The shipping lane is one of the most important information of vessels, which is useful in maritime communications. This paper presents a cache-enabled maritime communication network architecture where the shipborne base stations with the cache storages serve as the relay points between the user ships and the shore based networks. Under this architecture, we promote the total network throughput by exploiting the specific characteristics of the maritime shipping lane information. In particular, we use the shipping lanes to calculate the large-scale channel state information (CSI) and predict the future user associations. We propose a shipping lane aware cross-layer resource allocation algorithm to optimize the system throughput in terms of the caching strategy, power allocation and bandwidth allocation, which is a mix-integer non-convex programming problem. We divide the original problem into a capacity optimization problem and a caching placement problem. The capacity optimization problem is relaxed into a large-scale-CSI-only convex problem and solved by Lagrangian dual method. The caching placement problem is solved by a greedy algorithm. The numerical simulation results show the effectiveness of the shipping lane information in optimizing the system throughput compared with the existing algorithms.

Quantum secure direct communication (QSDC) is capable of direct confidential communications over a quantum channel, which is achieved by dispensing with the key agreement channel of the well-known quantum key distribution (QKD). However, to make QSDC a practical reality, we have to mitigate its reliance on quantum memory, its immediate communication interruption caused by eavesdropping and its low transmission reliability due to the heavy qubit losses. Hence a new QSDC protocol is proposed based on a sophisticated coded single-photon DL04 QSDC protocol to tackle the open challenges. In particular, quantum memory is dispensed with and a high-accuracy secrecy capacity estimate is derived for this protocol by conceiving dynamic joint encryption and error-control (JEEC) coding. We demonstrate that this quantum-memory-free DL04 QSDC (QMF-DL04 QSDC) protocol inches closer to the quantum channel's capacity and significantly improves the original DL04 QSDC's robustness. Moreover, a rate-compatible low-rate JEEC coding scheme is designed for the proposed framework, and the JEEC code advocated is shown to approach the secrecy capacity, despite tolerating an extremely high loss of qubits in the time-varying wiretap channel. Our simulations and experimental results demonstrate that the QMF-DL04 QSDC scheme significantly increases both the secure information rate and the communication distance of the original DL04 protocol.

In this paper, we investigate a multicast beamforming terrestrial-satellite cooperation system to optimize the communication capacity and quality of service. Different from traditional link-based terrestrial network, we design the terrestrial and satellite beamforming vectors cooperatively based on the required contents of users in order to realize more reasonable resource allocation. Meanwhile, the backhaul links between content provision center and satellite and base stations are limited, and the users always need high quality of service, considering these, our object is maximizing the sum of user minimum ratio under the constraints of resource allocation, backhaul link and quality of service in reality. We first formulate the optimization problem and propose a joint optimization iterative algorithm to design the beamforming vectors of satellite and base stations cooperatively. Then, to obtain the global optimum solution, we propose a Bound-based algorithm and solve the optimization problem by shrinking the upper bound and lower bound of the optimization feasible region. To decrease the complexity, we then design a heuristic scheme to solve the problem. The simulation results show that, our proposed cooperative optimization algorithms have better performance than non-cooperative methods, and the heuristic scheme has little poor performance but has significant advantage in low complexity.

The increasing maritime activities with the exclusive economic zone lead to increasing demands for wideband communications in recent years. This paper presents a maritime communication network architecture where the onshore high-tower base station provides wireless backhaul for the shipborne base stations, while the shipborne base stations serve as the mobile access points for the user ships. Under this architecture, considering the out-of-band full-duplex (OBFD) backhauling mode, we propose a joint backhaul and access link resource management scheme in respect to the wireless backhaul power allocation, access link power allocation and user association, for the sake of maximizing the network capacity. Specially, this scheme takes the inter-cell interference and the multiuser association into account in order to model the realistic maritime communication scenarios. The optimization problem is solved by the successive convex approximation (SCA) method and the numerical simulation results show the effectiveness of the algorithm in terms of the network capacity and the iteration convergence.

In this paper, we investigate the downlink cooperative multigroup multicast transmission in the integrated terrestrial-satellite network, in which base stations (BSs) and the satellite provide the multicast service for ground users in a cooperative manner while reusing the entire bandwidth. For both terrestrial BSs and the satellite, multiantennas are equipped and beamforming techniques are utilized for improving the system performance. Based on the architecture, we formulate a weighted max-min fair (MMF) beamforming design problem to jointly optimize the beamforming vectors of BSs and the satellite, which is solved based on the relation between the quality of service problem and the MMF problem. When it comes to the large scale case, where large numbers of BSs are distributed within the coverage of the satellite, we propose a time division cooperative multigroup multicast scheme consisting of two phases for the BSs and the satellite. Then, an iterative algorithm is proposed to solve the weighted MMF problem in the time division case. Finally, numerical results are provided to evaluate the cooperative multicast schemes as well as the proposed algorithms.

Unreliable message storage severely degrades the performance of LDPC decoders. This paper discusses the impacts of message errors on LDPC decoders and schemes improving the robustness. Firstly, we develop a discrete density evolution analysis for faulty LDPC decoders, which indicates that protecting the sign bits of messages is effective enough for finite‐precision LDPC decoders. Secondly, we analyze the effects of quantization precision loss for static sign bit protection and propose an embedded dynamic coding scheme by adaptively employing the least significant bits (LSBs) to protect the sign bits. Thirdly, we give a construction of Hamming product code for the adaptive coding and present low complexity decoding algorithms. Theoretic analysis indicates that the proposed scheme outperforms traditional triple modular redundancy (TMR) scheme in decoding both threshold and residual errors, while Monte Carlo simulations show that the performance loss is less than 0.2 dB when the storage error probability varies from 10−3 to 10−4.

Due to the broadcast and time‐varying natures of wireless channels, traditional communication systems that provide data encryption at the application layer suffer many challenges such as error diffusion. In this paper, we propose a code‐hopping based secrecy transmission scheme that uses dynamic nonsystematic low‐density parity‐check (LDPC) codes and automatic repeat‐request (ARQ) mechanism to jointly encode and encrypt source messages at the physical layer. In this scheme, secret keys at the transmitter and the legitimate receiver are generated dynamically upon the source messages that have been transmitted successfully. During the transmission, each source message is jointly encoded and encrypted by a parity‐check matrix, which is dynamically selected from a set of LDPC matrices based on the shared dynamic secret key. As for the eavesdropper (Eve), the uncorrectable decoding errors prevent her from generating the same secret key as the legitimate parties. Thus she cannot select the correct LDPC matrix to recover the source message. We demonstrate that our scheme can be compatible with traditional cryptosystems and enhance the security without sacrificing the error‐correction performance. Numerical results show that the bit error rate (BER) of Eve approaches 0.5 as the number of transmitted source messages increases and the security gap of the system is small.

Increasing marine activities taking place within the exclusive economic zone (EEZ) have made broadband maritime communications very attractive in recent years. In this paper, a coordinated satellite and terrestrial architecture (II-CST) is presented to implement real-time and broadband Internet access at sea. Further, since the mobility of shipborne base stations (S-BSs) may cause complex inter-cell interference (ICI), an S-BS sleeping control scheme is proposed to adapt the system resource allocation corresponding with the user load. When the proportion of blocked users is beyond threshold, the sleeping control process is triggered to reduce ICI and recover broadband access. Then we use the sailing position of accessed users as a constraint to adjust the downlink power allocation of S-BSs. Simulation results with real ship data of China's Yellow Sea show that under the II-CST, our adaptive S-BS sleeping control can contribute to achievable downlink data rate, mobility robustness, and power efficiency of the dynamic broadband maritime communication system.

Unreliable message storage severely degrades the performance of LDPC decoders. This paper discusses the various impacts of bit errors of finite-precision messages on LDPC decoders. Discrete density evolution indicates that the sign bits of finite-precision messages have the most influence on the decoding threshold. As a result, this paper proposes to protect the sign bits of messages by Hamming product codes. Simulation results show that the proposed scheme only has a degradation of 0.1 dB for the LDPC decoder with a storage error ratio of 0.001, which outperforms the traditional triple modular redundancy scheme.

A class of check-hybrid low-density parity-check (CH-LDPC) codes is designed and implemented to mitigate the problem of periodical signal blockage over the helicoptersatellite channels. The CH-LDPC code is derived by replacing one layer check nodes of a quasi-cyclic LDPC (QC-LDPC) code by simplex code constraints or first-order Reed-Muller (RM) code constraints, based on the property that the check matrix of the QC-LDPC code has a layered structure. Moreover, extrinsic information transfer (EXIT) functions are established to analyze the iterative decoding performance of the CH-LDPC codes. Simulation results show that, compared with the QCLDPC coding scheme, the CH-LDPC coding scheme designed in this paper achieves more than 25% bandwidth efficiency improvement over the helicopter-satellite channels.

Increasing marine activities taking place within the exclusive economic zone (EEZ) have made broadband maritime communications very attractive in recent years. In this paper, a coordinated satellite and terrestrial (II-CST) architecture is presented to enable real-time and broadband Internet access within the EEZ. Further, a voyage-based resource allocation scheme is proposed to dynamically adapt the system resource consumption corresponding with the user load. We make switching plans for shipborne base stations (S-BSs) considering the proportion of blocked users and changes in user distribution to recover broadband access and reduce inter-cell interference at sea, and take the sailing position of accessed users as a constraint to cooperatively adjust the downlink power of S-BSs. Simulation results under real ship data of China's Yellow Sea show that under the II-CST architecture, our resource allocation scheme can effectively improve the signal to interference noise ratio (SINR) of users, reduce the number of handover-related link failures, and reduce the power consumption of S-BSs.

This paper proposes a novel approach, based on unequal error protection, to enhance rateless codes with progressive recovery for layered multimedia delivery. With a parallel encoding structure, the proposed Progressive Rateless codes (PRC) assign unequal redundancy to each layer in accordance with their importance. Each output symbol contains information from all layers, and thus the stream layers can be recovered progressively at the expected received ratios of output symbols. Furthermore, the dependency between layers is naturally considered. The performance of the PRC is evaluated and compared with some related UEP approaches. Results show that our PRC approach provides better recovery performance with lower overhead both theoretically and numerically.

Adaptive network coded cooperation (ANCC) scheme has been shown to have better performance than the conventional repetition-based schemes and the space-time coded cooperation (STCC) frameworks for data transmissions from a large collection of terminals to a common destination in large wireless networks. However, the random selection strategy for ANCC protocol may generate many short cycles in the distributed low-density generator-matrix(LDGM) codes, which may cause error-floor and performance degradation. In this paper, an optimized relay selection strategy for ANCC is proposed. By exploiting information interaction between destination and terminals before data communication, and matching the instantaneous network topology, the proposed method generates ensembles of distributed LDGM codes free of short cycles. Simulation results demonstrate that the proposed relay selection protocol significantly outperforms the random relay selection strategy for various network sizes and different fixed numbers of selected terminals.

Minimum distance lower bounds are derived using graphical enumeration and integer programming for girth-constrained repeat-accumulate (RA) code ensembles. Moreover, the worst subgraphs in Tanner graphs leading to the minimum distance lower bounds and the probabilities they occur are analyzed.

In this work, we first study and then propose an optimal version on the medium access control mechanism known as carrier sense multiple access with collision avoidance (CSMA/CA). This protocol is adopted by many wireless communication standards, such as the IEEE 802.11 series and IEEE 802.15.4 (ZigB

In this paper, we discuss a certain route configuration problem via optimization theory. The problem is potentially relevant for designers of both wireless sensor and wireless ad hoc networks with multihop and link adaptation capability. We consider the optimal bit error rate (BER) and transmission rate allocations subject to overall BER and delay constraints for a designated route. The pivot of the problem lies in the delay constraint, which divides the problem into two cases - the loose delay and the tight delay case. In the former, analytical solutions are obtained by applying the Karush-Kuhn-Tucker (KKT) theorem. Specifically, we discover in this case that for a given target BER, the optimum solutions are only related to the hop lengths in the route. When the delay constraint is tight, there exists a mapping which can be used to reduce the dimension of the problem by a factor of two; however, a numerical optimization algorithm has to be used to find the optimum. The problem can however be shown to be a convex optimization problem, which ensures that any local minimum will be global. Simulation results show that by optimally configuring a chosen route, substantial energy savings could be obtained, especially under tight delay constraints.

In this paper, we propose MICRO (MInimum cost routing with optimized data fusion), an energy-efficient routing protocol for event-driven dense wireless sensor networks. The proposed routing protocol is an improvement over PEGASIS, which exploits a new cost function for energy balancing among sensor nodes and uses an iterative scheme with optimized data fusions to compute the minimum-cost route for each event-detecting sensor node. Compared to the previously suggested PEGASIS routing protocol, the MICRO protocol substantially improves the energy-efficiency of each route by optimizing the trade-off between minimizing the total energy consumption of each route and balancing the energy state of each sensor node. It is demonstrated that the MICRO protocol is able to outperform the LEACH and the PEGASIS protocols with respect to network lifetime by 100 - 300% and 10 - 100%, respectively.

This paper presents a novel unequal error protection (UEP) video transmission scheme based on dynamic resource allocation in a MIMO-OFDM system in order to improve the video transmission performance over wideband wireless channels. In this new scheme, a low density parity code (LDPC) is combined with a complexity reduced power reallocation algorithm to utilize the excess powers which margin from the powers necessary to meet the modulation level selected under a certain BER constraint. Furthermore, by taking full advantage of the residual excess powers, unequal error protection on important bits of video streams is provided by the LDPC code. Accordingly, the transmission efficiency and quality are both increased. Simulation results have shown that the proposed scheme may achieve great improvement in both systems with or without perfect channel estimation.

Abstract Joint source–channel coding schemes have been proven to be very effective for reliable multimedia communications. In this paper, we develop a joint source–channel coding scheme that combines the hidden Markov source (HMS) estimation and the low‐density parity‐check (LDPC) codes with an iterative estimation/decoding scheme. With this innovative combination, multimedia source redundancy could be accurately extracted by the hidden Markov estimation without any a priori information about the source. Moreover, the interleaver that is usually used to separate the source coding and channel coding can be avoided by exploiting the randomizing property of the LDPC codes. Furthermore, the channel decoding procedure may be implemented in parallel, resulting in good performance with a fairly low decoding complexity and delay. Simulation results have shown that the proposed scheme can achieve much better performance than the standard coding scheme over the binary input additive white Gaussian noise (BIAWGN) channels. Copyright © 2002 John Wiley & Sons, Ltd.

Based on the properties of cyclic codes, a new decoding algorithm for burst-error-correction is proposed in this paper. This algorithm can effectively correct burst errors with length that approaches to n-k for (n,k) Reed-Solomon (RS) codes. Moreover, due to the use of error locations correlation within a burst, divisions are exempted from the decoding process, achieving a fast decoding algorithm with much less computational complexity compared with existing algorithms. It is shown that the proposed algorithm can be widely used in wireless communications, wireless digital broadcast systems, and so on.