Channel Reliability Research Articles

An Improved Reliability-Based Decoding Algorithm for NB-LDPC Codes

When non-binary low-density parity-check (NB-LDPC) codes with low column weights are adopted, the iterative reliability-based majority-logic decoding (IRB-MLGD) algorithms suffer from severe performance degradation compared to message passing algorithms. In this brief, based on the improved iterative soft reliability-based (IISRB)-MLGD algorithm, we propose a perturbation-injected (P)-IISRB algorithm, where a regular perturbation is introduced to alleviate the periodic point problem that is the main reason for IISRB decoding failure. Compared with the conventional IISRB, the new algorithm significantly improves the decoding performance, lowering the error-floor by at least two orders of magnitude for the example code. Besides, instead of traditionally pre-computing and storing all the q-ary channel reliability measures, a real-time computing method is presented, which reduces the memory cost from <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( q) to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> q).

Building Covert Timing Channel of the IoT-Enabled MTS Based on Multi-Stage Verification

Although the global shipping industry is experiencing a productivity revolution due to the adoption of IoTs (Internet of Things), the dependence on complex data transmission and interactive centers is also increasing, which makes the IoT-enabled Maritime Transportation Systems (MTS) one of the most valuable but vulnerable industries against network security attacks. To guarantee the transmission security of confidential data, an important alternative in an untrustworthy IoT-enabled MTS is to apply the covert timing channels. This paper mainly introduces the construction of covert timing channel with low bit shifting rate and high reliability by multi-stage verification and error correction. For the covert timing channel schemes realized by active packet loss, the packet loss noise interferes with the channel's reliability. However, due to the constraints of stealthiness, the active packet loss ratio during covert communication is low, so more effective reliable strategies are needed to reduce noise interference. In the excellent scenario, when the bit error rate is lower than 0.08%, the transmission performance is kept at 0.49 bps. In the good scenario with strong network noise, although this method loses some performance, it can still maintain the transmission performance of 0.2 bps under the condition of bit error rate less than 1%, which effectively proves the effectiveness of multi-stage verification and error correction.

A Collision-aware MAC Protocol for Efficient Performance in Wireless Sensor Networks

Both IEEE 802.11 and IEEE 802.15.4 standards adopt the CSMA-CA algorithm to manage contending nodes’ access to the wireless medium. CSMA-CA utilizes the Binary Exponential Backoff (BEB) scheme to reduce the probability of packet collisions over the communication channel. However, BEB suffers from unfairness and degraded channel utilization, as it usually favors the last node that succeeded in capturing the medium to send its packets. Also, BEB updates the size of the contention window in a deterministic fashion, without taking into consideration the level of collisions over the channel. The latter factor has a direct impact on the channel utilization and therefore incorporating it in the computation of the contention window’s size can have positive impacts on the overall performance of the backoff algorithm. In this paper, we propose a new adaptive backoff algorithm that overcomes the shortcomings of BEB and outperforms it in terms of channel utilization, power conservation, and reliability, while preserving the fairness among nodes. We model our algorithm using Markov chain and validate our system through extensive simulations. Our results show a promising performance for an efficient backoff algorithm.

Predictive Syndrome Based Low Complexity Joint Iterative Detection-Decoding Algorithm for Non-Binary LDPC Codes

This paper addresses the problem of decoding non-binary low density parity check codes(LDPC) over finite field GF(q) using symbol flipping approach. To achieve low complexity reliable communication, three new algorithms for improving the bit error rate performance of the non-binary LDPC decoder are presented. The first type is the symbol flipping decoding algorithm using a flipping function based on the channel reliability to identify the least reliable symbol position. In this algorithm, if the predicted symbol value satisfies the check sum, then the value is declared as correct otherwise the value is adjusted and sent back to the QAM detector. Algorithms 2 in this paper is an improvement to iterative joint detection-decoding algorithm by using the method of iterative hard decision based majority logic to select the new candidate symbol value. The feedback value to the QAM detector is adjusted by using Euclidean distance between the current symbol and the newly selected symbol value. Algorithm 3 is a low complexity version of Algorithm 2 which is derived by applying a majority voting scheme. In the majority voting scheme, symbols are short listed first by voting and all the computation are carried out only for the short listed least reliable symbols which significantly lowers the processing complexity. Numerical results and complexity analysis show that the proposed methods have good bit error rate versus complexity trade-off for various applications when compared with some existing algorithms.

A Generalized Spatial Modulation System Using Massive MIMO Space Time Coding Antenna Grouping.

Massive multiple input multiple output (MIMO), also known as a very large-scale MIMO, is an emerging technology in wireless communications that increases capacity compared to MIMO systems. The massive MIMO communication technique is currently forming a major part of ongoing research. The main issue for massive MIMO improvements depends on the number of transmitting antennas to increase the data rate and minimize bit error rate (BER). To enhance the data rate and BER, new coding and modulation techniques are required. In this paper, a generalized spatial modulation (GSM) with antenna grouping space time coding technique (STC) is proposed. The proposed GSM-STC technique is based on space time coding of two successive GSM-modulated data symbols on two subgroups of antennas to improve data rate and to minimize BER. Moreover, the proposed GSM-STC system can offer spatial diversity gains and can also increase the reliability of the wireless channel by providing replicas of the received signal. The simulation results show that GSM-STC achieves better performance compared to conventional GSM techniques in terms of data rate and BER, leading to good potential for massive MIMO by using subgroups of antennas.

GreenMAC Protocol: A Q-Learning-Based Mechanism to Enhance Channel Reliability for WLAN Energy Savings

We have seen a promising acceptance of wireless local area networks (WLANs) in our day-to-day communication devices, such as handheld smartphones, tablets, and laptops. Energy preservation plays a vital role in WLAN communication networks. The efficient use of energy remains one of the most substantial challenges to WLAN devices. Several approaches have been proposed by the industrial and institutional researchers to save energy and reduce the overall power consumption of WLAN devices focusing on static/adaptive energy saving methods. However, most of the approaches save energy at the cost of throughput degradation due to either increased sleep-time or reduced number of transmissions. In this paper, we recognize the potentials of reinforcement learning (RL) techniques, such as the Q-learning (QL) model, to enhance the WLAN’s channel reliability for energy saving. QL is one of the RL techniques, which utilizes the accumulated reward of the actions performed in the state-action model. We propose a QL-based energy-saving MAC protocol, named greenMAC protocol. The proposed greenMAC protocol reduces the energy consumption by utilizing accumulated reward value to optimize the channel reliability, which results in reduced channel collision probability of the network. We assess the degrees of channel congestion in collision probability as a reward function for our QL-based greenMAC protocol. The comparative results show that greenMAC protocol achieves enhanced system throughput performance with additional energy savings compared to existing energy-saving mechanisms in WLANs.

Method for Effective Use of Cloudlet Network Resources

The article addresses the issue of balanced placement of mobile software applications of mobile users in cloudlets deployed near base stations of Wireless Metropolitan Area Networks (WMAN), taking into account their technical capabilities. It is noted that the proposed model is more efficient in meeting the demand for computing and memory resources of mobile devices, eliminating network delays and using a reliable communication channel. At the same time, a minimum of cloudlet-based communication channels with a mobile user was suggested, reducing the network load and reliability of the communication channel when using multimedia software on mobile devices. The article reviews the balanced distribution of the tasks in the cloudlet network. If a user offloads the task to the nearest cloud and resolves it there, then the delays and energy consumption will be less. When the cloudlet is far from the mobile device, as the number of communication channels increases the delays are observed. Moreover, the article discusses the issue of selecting the cloudlets that meet some of the user requirements. Using the possible values that determine the importance of cloudlets (vacant resources in cloudlets, closeness of cloudlets to the user, high reliability, etc.), the conditions, according to which the user's application is offloaded to the certain cloudlet, are studied and a method is proposed.

An Efficient Decoding Algorithm for Block Codes Based on the Communication Channel Reliability Information

For channel codes in communication systems, an efficient algorithm that controls error is proposed. It is an algorithm for soft decision decoding of block codes. The sufficient conditions to obtain the optimum decoding are deduced so that the efficient method which explores candidate code words can be presented. The information vector of signal space codes has isomorphic coherence. The path metric in the coded demodulator is the selected components of scaled regions. The carrier decision is derived by the normalized metric of synchronized space. An efficient algorithm is proposed based on the method. The algorithm finds out a group of candidate code words, in which the most likely one is chosen as a decoding result. The algorithm reduces the complexity, which is the number of candidate code words. It also increases the probability that the correct code word is included in the candidate code words. It is shown that both the error probability and the complexity are reduced. The positions of the first hard-decision decoded errors and the positions of the unreliable bits are carefully examined. From this examination, the candidate codewords are efficiently searched for. The aim of this paper is to reduce the required number of hard-decision decoding and to lower the block error probability.

Counterintuitive Characteristics of Optimal Distributed LRU Caching Over Unreliable Channels

Least-recently-used (LRU) caching and its variants have conventionally been used as a fundamental and critical method to ensure fast and efficient data access in computer and communication systems. Emerging data-intensive applications over unreliable channels, e.g., mobile edge computing and wireless content delivery networks, have imposed new challenges in optimizing LRU caching in environments prone to failures. Most existing studies focus on reliable channels, e.g., on wired Web servers and within data centers, which have already yielded good insights and successful algorithms. Surprisingly, we show that these insights do not necessarily hold true for unreliable channels. We consider a single-hop multi-cache distributed system with data items being dispatched by random hashing. The objective is to design efficient cache organization and data placement that minimize the miss probability. The former allocates the total memory space to each of the involved caches. The latter decides data routing and replication strategies. Analytically, we characterize the asymptotic miss probabilities for unreliable LRU caches, and optimize the system design. Remarkably, these results sometimes are counterintuitive, differing from the ones obtained for reliable caches. We discover an interesting phenomenon: allocating the cache space unequally can achieve a better performance, even when channel reliability levels are equal. In addition, we prove that splitting the total cache space into separate LRU caches can achieve a lower asymptotic miss probability than organizing the total space in a single LRU cache. These results provide new and even counterintuitive insights that motivate novel designs for caching systems over unreliable channels.

An Object Tracking Algorithm with Channel Reliability and Target Response Adaptation

In order to solve the problems of lower precision of target location in short-term occlusion and inaccurate of scale estimation of target in rotation by Spatial-Temporal Regularized Correlation Filters (STRCF), an object tracking algorithm with channel reliability and target response adaptation is proposed in this paper. In this algorithm, target response regularization is added to train target model. By updating the ideal target response function in the process of solving model, the target can be tracked again after being occluded for a short time. The reliability of each feature channel is evaluated by coefficient of channel reliability, which can improves the model's expression of the target. Scale filters can be trained in log-polar coordinates to improve the accuracy of scale estimation when target is rotating. The experimental results show that the proposed algorithm reduces 28.54 pixels in the average center position error and improves the average overlap rate by 22.8% compared with STRCF.

A Deep Learning Assisted Node-Classified Redundant Decoding Algorithm for BCH Codes

This paper proposes a node-classified redundant decoding (NC-RD) algorithm based on the received sequence’s channel reliability for high-density parity-check (HDPC) codes. Two preprocessing steps are proposed prior decoding. The variable nodes of the parity-check matrix are firstly classified by the $k$ -median algorithm based on the number of shortest cycles associated with each variable node before decoding. Then, by searching among the automorphism group of the HDPC codes, we generate a list of permutations for bit positions by computing and sorting the permutation reliability metrics. The redundant decoder conducts the message-passing decoding according to the sorted permutations, which limit the unreliable information propagation for each permutation. Besides proposing a list decoding algorithm on top of the NC-RD algorithm to augment the decoder’s performance, we show that the NC-RD algorithm can be transformed into a neural network system. More specifically, multiplicative tuneable weights are attached to the decoding messages to optimize the decoding performance. Simulation results of BCH codes over the AWGN channels show that the NC-RD algorithm provides a performance gain compared to the random redundant decoding algorithm. Additional decoding performance gain can be obtained by both the list decoding method and the neural network “learned” NC-RD algorithm.

Auto-correct-integrated trackers with and without memory of first frames

Visual short-term tracking in a long sequence of images with a lot of pose variations, target deformations and different types of occlusion is one of the harshest tasks in image processing. Overcoming such challenges can be performed in a superior way by combining different trackers. In this paper, we propose a method that combines different algorithms for tracking the given target. The algorithms are KCF (Henriques et al. in IEEE Trans Pattern Anal Mach Intell 37(3):583–596, 2014), MIL (Babenko et al. in Visual tracking with online multiple instance learning. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009), CSR-DCF (Lukezic et al. in Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017) and MOSSE (Bolme et al. in Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010) trackers. In this method, these algorithms can continuously correct each other’s faults. They also implicitly search for the target even when they are tracking it, to make sure the object they are tracking is the real target. Thus, they outperform many of the state-of-the-art trackers as well as their components when they work independently. In this paper, we examine three trackers which we made in such way. Two of these trackers run at speeds close to real-time on a CPU and so we compare them with some famous wide-spread tracking algorithms both in terms of accuracy and speed.

OPTIMAL TRANSMIT POWER ALLOCATION FOR MAXIMIZING THE CHANNEL CAPACITY AND SINR IN MU-MIMO SYSTEM USING DIRTY PAPER CODING

Massive MIMO is the emerging technology to design 5G wireless communication systems. Perfect channel state information(CSI) is the essential condition to design the performance metrics for MU-MIMO system since the inaccuracies present in the CSI reduces data rates and channel capacity. The objective of this paper is to design a optimum power allocation scheme for MU-MIMO using dirty paper coder pre-coding. An expression is derived to determine the optimum power allocation and the ambient condition with degree of perfection factor under channel reliability at the base station to reduce the transmit power and the results obtained are presented and analysed. 2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V.

Ograniczenia prędkości statków do niezawodnego utrzymania ścian nabrzeży zlokalizowanych przy kanałach nawigacyjnych w portach

There is a number of ports where approach or inside navigation channels are located close to the quay walls. In difficult hydrometeorological conditions appropriate speed of ship is needed to keep proper ship’s steering while passing through channel. The ships that pass near the quay walls with high speed create high interaction forces on moored ships and negatively interact on their mooring equipment and quay walls. Ports should ensure relevant maintenance and reliability of quay walls and ships’ mooring equipment. That is why investigation of the ships interaction forces during ship passing near the ships moored to quay walls is very important to find limitations of the passing ship speed depending on passing ship’s parameters, distances and environmental conditions to provide reliable maintenance of navigation channel. In the article the conditions of dynamic forces caused by passing ships are investigated, including possible external forces influencing on moored ships, mooring equipment and quay walls. The methodology to assess the forces exerted on ship moored to quay wall by ship passing close to them is created. On the basis of the case study analysis results, the recommendations for the limitations to ships passing near the ships moored to quay walls were proposed that will allow providing relevant maintenance and reliability of navigation channel and its infrastructure.

MIMO channels: optimizing throughput and reducing outage by increasing multiplexing gain

The two main aims of deploying multiple input multiple out (MIMO) are to achieve spatial diversity (improves channel reliability) and spatial multiplexing (increase data throughput). Achieving both in a given system is impossible for now, and a trade-off has to be reached as they may be conflicting objectives. The basic concept of multiplexing: divide (multiplex) transmit a data stream several branches and transmit via several (independent) channels. In this paper, we focused mainly on achieving spatial multiplexing by modeling the channel using the diagonal Bell Labs space time scheme (D-BLAST) and the vertical Bell Labs space time architecture (V-BLAST) Matlab simulations results were a lso given to further compare the advantages of spatial multiplexing.

Convolution operators for visual tracking based on spatial–temporal regularization

In recent years, the method based on discriminative correlation filter has been shown excellent performance in short-term visual tracking. However, discriminative correlation filter-based method heavily suffers from the problem of the multiple peaks and model drift in responds maps incurred by occlusion and rotation. To solve the above problem, we proposed convolution operators for visual tracking based on spatial–temporal regularization. Firstly, we add spatial–temporal regularization in loss function, which will guarantee continuity of the model in time. And we use preconditioned conjugate gradient algorithm to obtain filter coefficients. Secondly, we proposed channel reliability to estimate quality of the learned filter and fuse the different reliability coefficients to weight response map in location. We set a threshold to reduce the number of iteration in location and accelerate the compute speed of algorithm. Finally, we use two different correlation filters to estimate location and scale of target, respectively. Extensively experiment in five video sequences show that our tracker has been significantly improved performance in case of occlusion and rotation. The AUC in success plot improves 33.2% than ECO-HC and 41.5% than STRCF, respectively.

Content Retrieval Based on Prediction and Network Coding in Vehicular Named Data Networking

Named Data Networking (NDN) has the advantages of content-based, location-independent and in-network caching. These characteristics are naturally suitable for highly dynamic Vehicular Ad hoc Networks (VANETs), so VNDN has become a promising network architecture. However, achieving effective and reliable multi-hop content retrieval is still a major challenge in VNDN where network parameters change frequently and the channel reliability is poor. In this paper, we propose a protocol based on prediction and network coding for content retrieval in vehicular named data networking (PreNCCR). In order to adapt to the dynamic and random packet loss characteristics of VANETs and restrict flooding, a prediction-based opportunistic routing in which the priority of candidate forwarder is determined through prediction mechanism is proposed. On this basis, a network coding-based packet forwarding strategy is proposed in which the available network capacity are effectively utilized. The evaluation results show that under moderate network configuration parameters, compared with VNDN-geo, CCVN, and SelNC protocols, PreNCCR can not only achieve 33.8% ~ 99.8% higher request satisfaction ratio, 46.6% ~ 65.1% lower delay, but also can reduce the consumption of Interest and Data by 32.6% ~ 80.7%, 52.3% ~ 60.1%.

Distributed ecosystem of voice communications for air traffic control system

One of the critical elements of air traffic control (ATC) systems is the ground-to-air voice communications system. The work of ATC dispatchers is provided by main and standby radios. Each radio station is a monoblock that performs all the functions of radio communication. Modern radios have a modular structure. Their architecture consists of structurally independent modules. With such architecture, redundancy of radios can be carried out not at the level of the device as a whole, but at the level of its individual modules. In this case, the communication ecosystem takes the form of a cyber-physical environment. The homogeneous modules of all radio stations form a multi-level cloud system. By connecting modules of various levels, a virtual radio station is formed during communication session. The channel reliability of the communication system for two architectures of connecting redundant modules in a virtual environment providing ground-to-air ATC voice radio communication is considered: structure with individual switch for each standby module of the group, and structure with common switch for all standby modules of the group. The conditions for choosing the most effective of the considered architectures ac-cording to the reliability criterion are determined.

호텔이용고객의 검색매체에 따른 온라인 정보채널의 신뢰도와 만족도에 관한 연구

The purpose of this study is to present effective online marketing measures to attract customers by analyzing the difference in reliability and satisfaction of online information channels according to search media. To achieve the goal, a survey was conducted on hotel customers. The results of the analysis of 306 questionnaires are as follows. It was analyzed that the reliability of each online information channel varies from one search medium to the other in three out of 13 categories. According to the search media, hotel users showed a difference in satisfaction level by online information channel in eight out of 13 categories. According to search media, IPA analysis of reliability and satisfaction of each online information channel showed a difference in one item. Based on the analysis results, effective online marketing measures for attracting customers from hotel companies are presented as follows. First, we should focus our marketing efforts on Smart phone areas rather than PCs. Second, information on official sites should be strengthened rather than marketing using SNS. Third, we need to reinforce the contents of the hotel's homepage. Fourth, we need to strengthen video contents. Fifth, Internet blog marketing is suitable for PC users rather than smart phone users. Sixth, marketing via Twitter, which is on the decline of specifications, should be sorted out.

Joint Channel Reliability and Correlation Filters Learning for Visual Tracking

Multi-channel discriminative correlation filter (DCF) tracking methods have exhibited superior performance on several benchmarks. However, existing methods usually treat each channel of the features equally, whereas they pay less attention to the contribution of different channels. Different channels exhibit variant properties in the tracking process. A DCF learned with equally important channels is likely to be contaminated by the unreliable ones, which results in model degradation. To address this problem, we propose a new formulation for jointly learning the channel reliability and the correlation filters. The formulation is generic, and it can be combined with existing techniques in the DCF framework to further improve the performance. Our method can adaptively increase the impact of reliable channels and down-weight the corrupted ones. To solve the joint learning problem, we propose an optimization strategy that alternates between the correlation filters and the channel weights. Further, we prove the upper bound of the objective function and solve the channel weights efficiently. The joint learning strategy makes the correlation filters more discriminative and the channel weights more accurate. To verify the joint formulation, we propose a tracker based on the proposed formulation and the techniques used in the ECO tracker. We conduct extensive experiments to evaluate the proposed tracker on three benchmarks. The experimental results show that our formulation is effective and efficient, and that it performs favorably against other state-of-the-art trackers.