Low Channel Quality Research Articles

Downlink Massive MIMO Systems: Reduction of Pilot Contamination for Channel Estimation with Perfect Knowledge of Large-Scale Fading

Massive multiple-input multiple-output (MIMO) technology is considered crucial for the development of future fifth-generation (5G) systems. However, a limitation of massive MIMO systems arises from the lack of orthogonality in the pilot sequences transmitted by users from a single cell to neighboring cells. To address this constraint, a proposed solution involves utilizing orthogonal pilot reuse sequences (PRS) and zero forced (ZF) pre-coding techniques. The primary objective of these techniques is to eradicate channel interference and improve the experience of end users who are afflicted by low-quality channels. The assessment of the channel involves evaluating its quality through channel assessment, conducting comprehensive evaluations of large-scale shutdowns, and analyzing the maximum transmission efficiency. By assigning PRS to a group of users, the proposed approach establishes lower bounds for the achievable downlink data rate (DR) and signal-to-interference noise ratio (SINR). These bounds are derived by considering the number of antennas approaches infinity which helps mitigate interference. Simulation results demonstrate that the utilization of improved channel evaluation and reduced loss leads to higher DR. When comparing different precoding techniques, the ZF method outperforms maximum ratio transmission (MRT) precoders in achieving a higher DR, particularly when the number of cells reaches 𝛶𝛶𝑝𝑝=7.

Channel Quality Prediction for TSCH Blacklisting in Highly Dynamic Networks: A Self-Supervised Deep Learning Approach

Cross-Technology Interference (CTI) is a severe issue for wireless networks operating in the license-free ISM bands. In particular, CTI can significantly affect the performance of low-power wireless links used in the Internet-of-Things (IoT). The IEEE 802.15.4 standard adopts a channel hopping scheme in its Time-Slotted Channel Hopping (TSCH) mode as a means to mitigating the adverse effect of CTI. However, the indiscriminate procedure by which TSCH nodes hop over different channels can suffer from severe interference in specific channels. Adaptive channel blacklisting is a technique to alleviate this issue by leaving out low-quality channels from the hopping list. To enable an effective blacklisting, especially in highly varying networks, an accurate real-time prediction of the quality of all available channels is of paramount importance. Previous studies rely on the past records of the channels as an indication of their quality in near future. Evidently, such approaches cannot extend to highly dynamic environments. This paper present a self-supervised approach for training deep neural networks capable of predicting the future behaviour of the frequency channels. The trained models can then substitute the quality assessment approaches in blacklisting schemas. Considering in-vehicle wireless networks as a target application, we evaluate this idea using a real-world experimental dataset, consisting of three measurement scenarios inside a moving vehicle. The experimental results show that using the proposed technique for TSCH blacklisting significantly improves the reliability of networks experiencing such highly dynamic interference and performs at least as good as the existing channel assessment methods in low-interference conditions.

LoRa Channel Characterization for Flexible and High Reliability Adaptive Data Rate in Multiple Gateways Networks

We characterize the LoRa channel in terms of multi-path fading, loss burstiness, and assess the benefits of Forward Error Correction as well as the influence of frame length. We make these observations by synthesizing extensive experimental measurements realized with The Things Network in a medium size city. We then propose to optimize the LoRaWAN Adaptive Data Rate algorithm based on this refined LoRa channel characterization and taking into account the LoRaWAN inherent macro-diversity from multi-gateway reception. Firstly, we propose ADRopt, which adjusts Spreading Factor and frame repetition number to maintain the communication below a target Packet Error Rate ceiling with optimized Time-On-Air. Secondly, we propose ADRIFECC, an extension of ADRopt in case an Inter-Frame Erasure Correction Code is available. The resulting protocol provides very high reliability even over low quality channels, with comparable Time on Air and similar downlink usage as the currently deployed mechanism. Simulations corroborate the analysis, both over a synthetic random wireless link and over replayed real-world packet transmission traces.

Channel assignment in TSCH-based wireless sensor networks using fuzzy logic

Recent advances in wireless sensor networks, especially in industry (IWSN), have brought important improvements to deploy a sensor network in such environments. However, there is a high interference level, noise, shadowing, and multipath fading, caused by machines, several metallic objects, which affect the link quality. Some mechanisms have been proposed to deal with these effects, such as frequency hopping and channel blacklisting, or rather, denylisting. However, there are still open issues, such as an appropriate method for managing both mechanisms to avoid similar or adjacent channels in simultaneous transmissions. In addition to denylisting with low quality channels, this paper proposes a greylist with uncertain quality channels, and a whitelist, or rather, allowlist, with good quality channels. A fuzzy logic method to classify them and insert the channels into the appropriate list is used. The mechanism was compared to the channel hopping approach employed on the time slotted channel hopping (TSCH) protocol with and without channel offset management in tree topology networks. The evaluations were made through simulations using a realistic channel model for IWSN, and the results demonstrate that the proposed approach presented better performance in terms of packet delivery rate and determinism. In the studied examples, the network with 53 devices and with the triple channel list approach presented 8% better in terms of packet delivery rate in the application layer than the blind approach (without link quality estimation) of TSCH with channel offset management (no collision), and from 51 to 59% better than approaches without the channel offset management.

Near-Instantaneously Adaptive Multi-Set Space-Time Shift Keying for UAV-Aided Video Surveillance

Unmanned Aerial Vehicles (UAV) are more suitable for surveillance systems than their traditional stationary counterparts. To meet the associated demanding video compression requirements, the H.265/High Efficiency Video Coding (HEVC) codec is invoked. Its scalable video coding extension is capable of supporting diverse video resolutions by encoding the video clips captured into multiple layers, namely the Base Layer (BL) and several Enhancement Layers (EL). In this treatise, we propose a multi-functional Multiple-Input Multiple-Output (MIMO) transceiver relying on Multi-Set Space-Time Shift Keying (MS-STSK) for UAV-aided near-instantaneously adaptive layered video streaming, which is capable of promptly varying the throughput as a function of both the estimated channel quality as well as of the UAV mobility, while maintaining near-flawless video quality. Additionally, we invoke EXtrinsic Information Transfer (EXIT) charts for characterizing our Unequal Error Protection (UEP) based video transceiver with the aid of near-capacity channel coding. Some of the perceptually less important ELs may have to be dropped during instances of low channel quality to retain the robustness of the transmitted video stream when a low-throughput MIMO configuration is activated. The simulation results show that our proposed UEP assisted adaptive system is capable of attaining the best Y-Peak SNR (PSNR) performance in comparison to both its non-adaptive and Equal Error Protection (EEP) counterparts.

Верхняя оценка вероятности ошибки приема апериодической псевдослучайной последовательности в условиях организованных помех

The article discusses the obtained estimate of the upper bounds for the probable receiving error of the synchronizing sequence during sensor phasing of aperiodic pseudo-random sequences (CRR) in broadband communication systems in the channels of low quality with strong disturbances of natural and organized structure. The obtained results allow to design synchronization systems of pseudo-random sequences for the worst case, which guarantees their reliable operation in low-quality channels, and, unlike the well-known methods, the estimation of synchronization of aperiodic pseudo-random sequence sensors doesn’t depend on the error distribution in the communication channel and the period of sequence. The appointed differences simplify the evaluation of synchronization in the operation of broadband communication systems in low-quality channels.

Resource allocation based on hybrid genetic algorithm and particle swarm optimization for D2D multicast communications

Device-to-Device (D2D) multicast communication is a new technology of the fifth-generation networks (5G) for efficiently coping with the ever-increasing demand for content sharing among users. In fact, it enables direct communication between devices in proximity and improves spectral efficiency by reusing a licensed cellular spectrum. The existing related studies show that D2D communications increase network capacity and reduce latency. Nevertheless, the interference management should be carried out in a coordinated manner in order to realize the full potential of this technology and enable its integration into the cellular architecture. We consider the joint uplink subcarrier allocation and power control in D2D underlying cellular networks. In single rate multicast communications, the achieved data rate is greatly limited by the nodes with low channel quality. In this article, we formulate the resource allocation as a max–min optimization problem. Such an optimization problem is in general an NP-hard combinatorial problem and its solution typically requires searching enormous search trees. We propose a multicast schemes based on Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO). We implemented 8 different transfer functions combined with 2 update position strategies in order to assess the performance of BPSO. For the GA, we implemented Uniform and Multipoint crossover methods combined with Roulette Wheel and Tournament selectors. In addition to providing a deep understanding of the algorithm behavior, we present numerical results that demonstrates that GA outperforms BPSO in terms of minimum achieved data rate when the pressure of infeasibility is high.

Spectrum pricing for cognitive radio networks with user’s stochastic distribution

Amid the dynamic spectrum access in cognitive radio networks, when complex spectrum conditions should be taken into account, how to price the spectrum in order to benefit primary systems in maximization is still under-investigated. In this paper, we devise a spectrum pricing method to address this issue in cognitive networks. In our proposed mechanism, leasing spectrum is collected for uniform selling and classified into three kinds of channels—high-quality channel, mid-quality channel and low-quality channel, respectively. They will be priced variously according to different interference characteristics caused by versatile path fading and user positions. In respond to heterogeneous channel qualities, secondary users also have own selection preferences. They can purchase one kind of channel for usage in based of channel quality and available budget. Then, we obtain the final pricing solution which is an iterative algorithm converging to a fixed point. Also, the existence of a pure Nash equilibrium is discussed to ensure the rationality of the method. In numerical results, we evaluate the effects of this proposal in spectrum pricing and primary systems’ profits.

Problems and Suggestions on the Mode of Intellectual Property Financing of Small and Medium-sized Technological Enterprises

Medium-sized Technological Enterprises is the key to China's innovation-driven strategy, but its widespread shortage of funds. Intellectual property financing has become an effective way to solve the problem of shortage of funds. This paper combs the relevant literature on the financing mode of intellectual property at home and abroad, and draws some problems such as shortage of talents, low quality and low financing channels in intellectual property financing, and puts forward suggestions from four aspects: colleges and universities, enterprises, banks and governments. The effective integration of the relationship between the four can make corporate intellectual property financing to run better.

Semantic Security with Practical Transmission Schemes over Fading Wiretap Channels

We propose and assess an on–off protocol for communication over wireless wiretap channels with security at the physical layer. By taking advantage of suitable cryptographic primitives, the protocol we propose allows two legitimate parties to exchange confidential messages with some chosen level of semantic security against passive eavesdroppers, and without needing either pre-shared secret keys or public keys. The proposed method leverages the noisy and fading nature of the channel and exploits coding and all-or-nothing transforms to achieve the desired level of semantic security. We show that the use of fake packets in place of skipped transmissions during low channel quality periods yields significant advantages in terms of time needed to complete transmission of a secret message. Numerical examples are provided considering coding and modulation schemes included in the WiMax standard, thus showing that the proposed approach is feasible even with existing practical devices.

Improved KMV-Cast with BM3D Denoising

Recently, a novel scheme called Knowledge-enhanced Mobile Video Broadcasting (KMV-Cast) was proposed to improve the quality of mobile video transmission. In this paper, we propose to use the well-known block-matching and three dimensional filtering (BM3D) algorithm to remove noise for KMV-Cast. BM3D is realized by block-matching, collaborative filtering, aggregation, and Wiener filtering. In this paper, BM3D will be used to remove noise after videos/images reconstruction using KMV-Cast receiver. The block-matching is used to collect similar pixel groups to reveal the property of the patch, while the collaborative filtering preserves the unique feature of each block through hard-thresholding. The aggregation process performs averaging to get the estimation results. The simulation results show that KMV-Cast with BM3D denoising performs the best compared with other two algorithms including SoftCast and KMV-Cast, especially in case of low channel quality and all image blocks are needed to be transmitted. The proposed scheme has 9 dB gains in terms of PSNR under low channel SNR (-5 dB) compared with the conventional KMV-Cast scheme, when all image blocks are transmitted through additive white Gaussian noise (AWGN) channel.

Boosting Spatial Reuse via Multiple-Path Multihop Scheduling for Directional mmWave WPANs

With huge unlicensed bandwidth available in most parts of the world, millimeter wave (mmWave) communications in the 60 GHz band has been considered as one of the most promising candidates to support multi-gigabit wireless services. Due to high propagation loss of mmWave channels, beamforming is likely to become adopted as an essential technique. Consequently, transmission in 60 GHz band is inherently directional. Directivity enables concurrent transmissions (spatial reuse), which can be fully exploited to improve network capacity. In this paper, we propose a multiple paths multi-hop scheduling scheme, termed MPMH, for mmWave wireless personal area networks, where the traffic across links of low channel quality is transmitted through multiple paths of multiple hops to unleash the potential of spatial reuse. We formulate the problem of multiple paths multi-hop scheduling as a mixed integer linear program (MILP), which is generally NP-hard. To enable the implementation of the multiple paths multi-hop transmission in practice, we propose a heuristic scheme including path selection, traffic distribution, and multiple paths multi-hop scheduling to efficiently solve the formulated problem. Finally, through extensive simulations, we demonstrate MPMH achieves near-optimal network performance in terms of transmission delay and throughput, and enhances the network performance significantly compared with existing protocols.

Adaptive Prefetching in Wireless Computing

In this paper, we consider a basic issue in wireless computing, where mobile devices (with relatively limited memory) fetch data (text, images, multimedia, etc.) from access points over wireless channels of fluctuating quality. When the channel quality is low, slow data downloads can contribute to application latency, and degradation of the user experience. To mitigate this issue, mobile devices can prefetch data during good quality channel periods preemptively, in anticipation of using them during low quality channel epochs. In other words, under favorable wireless channel conditions, the mobile terminal can prefetch data aggressively to reduce application latency when channel conditions have degraded. Considering channel fluctuations, memory constraints, and application latency, the issue at each point in time is whether to prefetch or not. A dynamic programming approach is taken here to determine the optimal prefetching policy. The latter is leveraged to develop a prefetching algorithm “Fetch-or-Not” (FON). We then design a randomized “Fetch-or-Not” (RFON) prefetching algorithm, which uses a randomized approximation of FON, thus lifting the need for online optimization and substantially reducing the computational complexity. Simulations are used to demonstrate that our low-complexity schemes perform well when compared to the optimal. In addition, our schemes outperform benchmark design techniques in realistic channel conditions.

On the benefits of contractual inefficiency in quality-differentiated markets

Contractual inefficiencies within supply chains increase an input price above its marginal cost, therefore they are considered detrimental to consumer surplus. We argue that such inefficiencies may be beneficial to consumers in quality-differentiated markets. Indeed, enhancing contractual efficiency in high-quality supply chains may adversely affect the market structure by driving low-quality vertical chains out of the market and consequently reduce consumer surplus. Due to the finiteness property, (counter-)integration in the low-quality channel does not allow this channel to be in business. Our result holds irrespective of whether the contractual inefficiencies originate from the double marginalization or the ‘commitment effect’.

<b>A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English</b> - doi: 10.4025/actascitechnol.v35i4.19825

Recognition of isolated spoken digits is the core procedure for a large number of applications which rely solely on speech for data exchange, as in telephone-based services, such as dialing, airline reservation, bank transaction and price quotation. Spoken digit recognition is generally a challenging task since the signals last for a short period of time and often some digits are acoustically very similar to other digits. The objective of this paper is to investigate the use of machine learning algorithms for spoken digit recognition and disclose the free availability of a database with digits pronounced in English and Portuguese to the scientific community. Since machine learning algorithms are fully dependent on predictive attributes to build precise classifiers, we believe that the most important task for successfully recognizing spoken digits is feature extraction. In this work, we show that Line Spectral Frequencies (LSF) provide a set of highly predictive coefficients. We evaluated our classifiers in different settings by altering the sampling rate to simulate low quality channels and varying the number of coefficients.

Vertical Relations and Number of Channels in Quality-Differentiated Markets

Contractual inefficiencies within supply chains increase an input price above its marginal cost, therefore they are considered detrimental to consumer surplus. We argue that such inefficiencies may be beneficial to consumers in quality-differentiated markets where the property holds. Indeed, enhancing efficiency in high-quality supply chains through vertical integration may adversely affect the market structure by driving low-quality chains out of the market, and, consequently reduce consumer surplus. Due to the finiteness property, (counter-)integration in the low-quality channel does not allow this channel to be in business. Our result holds both with secret and public contracts, and with buyer power.

Expectation–maximization‐based joint data detection and channel estimation for a cellular multi‐carrier code division multiple access network using single‐hop relaying

SUMMARYThis paper is concerned with channel estimation and data detection for a cellular multi‐carrier code division multiple access network using single‐hop relaying in the presence of frequency selective fading channels. The proposed expectation–maximization (EM) algorithm was used to jointly estimate both the coefficients of the channel between a relay and a base station and the data. EM algorithm is particularly suited to multi‐carrier code division multiple access systems because they have multi‐carrier signal format. The considered network uses single‐hop relaying technique to provide a higher quality transmission to the users with low quality channels. The base station (managing mechanism) gives them an opportunity to send their messages via the users with high quality channels in a time sharing mode. The performance of the proposed EM algorithm, with and without hopping and with cooperative communication technique, was analyzed by a computer simulation, and the results are presented. Copyright © 2011 John Wiley & Sons, Ltd.

Efficient Selective Feedback Design for Multicell Cooperative Networks

Multicell cooperative processing (MCP) has been recognized as a promising technique for increasing the spectral efficiency of future wireless systems. Unfortunately, the provided benefits of MCP come at the cost of increased radio feedback and backhaul overhead; for downlink transmission in frequency-division duplexing (FDD) systems, users need to feed back their channel-state information (CSI) to the MCP scheduler, and user data need to be exchanged between all cooperating base stations (BSs) through the backhaul network. In the context of conventional noncooperative networks, it has been suggested that radio feedback load could be reduced by preventing users with low-quality channels from feeding back their CSI (concept of selective feedback), at the cost of a small fraction of the multiuser diversity gain. In this paper, we investigate the translation of this selective feedback concept to MCP systems. According to this technique, users with weak interference links are prevented from feeding back their full CSI to the MCP scheduler. Although efficient, this technique alone cannot mitigate the backhaul overhead related to routing user data possibly to several BSs. To overcome this condition, we propose two schemes: one scheme based on Media Access Control (MAC)-layer scheduling and the other scheme based on physical-layer precoding. These schemes, combined with feedback load reduction, allow for a substantial mitigation of the MCP overheads related to feedback and backhaul load. We analyze the improvements in terms of both user-to-BS feedback reduction and backhaul load reduction, and we show that this framework results in a good tradeoff between performance and overhead for multicell cooperative networks.

Effects of distribution channel structure in markets with vertically differentiated products

This study examines how the structure of distribution channels may influence firms’ quality and price strategies and how they may in turn affect consumer welfare. It treats product quality as a decision variable so that the degree of product substitution becomes endogenous rather than exogenous as in previous studies. We find that, with vertically differentiated firms, the changes in channel structure have asymmetric effects depending on whether they occur in the high-quality channel or in the low-quality channel. The product quality of the high-quality channel decreases when it decentralizes unilaterally. However, product quality of the low-quality channel would increase when it decentralizes. The high-quality manufacturer and its channel suffer more from decentralization in comparison with their low-quality counterparts, and the low-quality manufacturer actually receives greater profits when both channels are decentralized. An important driver behind these asymmetries is the interaction between firms’ pricing incentives in integrated versus decentralized channels and what consumer segments they serve. Our analysis indicates that decentralization may reduce consumer welfare, but decentralization in the high-quality channel hurts consumers more than that in the low-quality channel. Therefore in a competitive environment where firms make both quality and price decisions, channel integration would have significant welfare enhancement effects through the elimination of double marginalization, especially if it happens in the high-quality channel. Moreover, we demonstrate that once quality is endogenized, integration is the only equilibrium of channel structure choices. This suggests that the private incentives of firms may actually benefit consumers but do not have to be in line with the general preference of industry regulation for decentralization.

E‐CLEMA: A cross‐layer design for improved quality of service in mobile WiMAX networks

AbstractIn this paper, an efficient cross‐layer design that performs joint adaptation of the physical (PHY) and application layers of a mobile WiMAX network is proposed. The design takes into account channel state and performance information from the PHY and medium access control (MAC) layers, respectively. It uses a decision algorithm to evaluate this information, specify unfavorable conditions regarding low channel quality and increased congestion, and take measures by coordinating modulation order, transmission power, and media encoding rate, toward improved overall quality of service (QoS) offered to the user. Extensive simulation results show that the proposed design achieves considerably reduced packet loss and power consumption, combined with increased throughput as compared to a typical mobile WiMAX system. Copyright © 2008 John Wiley & Sons, Ltd.