Varying Channel Quality Research Articles

Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things

The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN).

A Study of Transmission Overhead of a Hybrid Bundle Retransmission Approach for Deep-Space Communications

Bundle protocol (BP) was proposed as the core protocol of delay/disruption-tolerant networking for space. A hybrid reliable transmission mechanism based on integrating “proactive” retransmission and “reactive” retransmission of BP bundles has been presented as a means of achieving highly reliable file/data delivery in deep-space communications. Transmission overhead imposed by reliable delivery of data packets/bundles is consistently one of the key factors for efficient data delivery in space communications. It is especially important in deep-space networks, where the available electrical power on spacecraft for data delivery is a scarce resource but substantial energy is needed to propagate data over an extremely long deep-space channel. In this article, a detailed study of the transmission overhead for the hybrid retransmission approach of BP for reliable deep-space communications is presented. An analytical framework is presented for modeling the transmission overhead of BP as influenced by variations in channel quality, file size, bundle size, and retransmission timeout interval length. Its energy resource consumption is also briefly discussed. The analytical framework is validated by realistic file transfer experiments using a PC-based testbed infrastructure; the framework is found to be able to predict the performance with respect to the transmission overhead of BP.

Overbooking-Empowered Computing Resource Provisioning in Cloud-Aided Mobile Edge Networks

Conventional computing resource trading over mobile networks generally faces many challenges, e.g., excessive decision-making latency, undesired trading failures, and underutilization of dynamic resources, owing to the constraint of wireless networks. To improve resource utilization rate under dynamic network conditions, this paper introduces a novel computing resource provisioning mechanism empowered by overbooking, that allows the amount of booked resources to exceed the resource supply. Cloud-aided mobile edge networks are considered for the proposed framework, where an edge server can purchase more resources from a cloud server to offer computing services to multiple end-users with computation-intensive tasks. Specifically, the proposed mechanism relies on designing pre-signed forward trading contracts among edge and end-users, as well as between edge and cloud in advance to future practical trading; while encouraging an appropriate overbooking rate to improve resource utilization, via analyzing historical statistics associated with uncertainties such as dynamic resource supply/demand, and varying channel qualities. The contract design is formulated as a multi-objective optimization problem that aims to maximize the expected utilities of end-users, edge, and cloud, via evaluating potential risks; for which a two-phase multilateral negotiation scheme is proposed that facilitates the bargaining procedure among the three parties, to reach the final trading consensus (namely, contract terms). Experimental results demonstrate that the proposed mechanism achieves mutually beneficial utilities of three parties, while outperforming baseline methods on significant indicators such as task completion, trading failure, time efficiency, resource usage, etc., from various analytical angles.

Wireless Resource Scheduling for High Mobility Scenarios: A Combined Traffic and Channel Quality Prediction Approach

The rapid growth of mobile data traffic in a high-speed scenario imposes great challenges on wireless system design and optimization, where the inherent Doppler shift, frequent cell switching, and large penetration loss will significantly degrade the quality of transceiver signals. Wireless resource scheduling dynamically allocates wireless resources according to the user channel conditions and quality of service requirement, which is an enabling approach to maximize the resource utilization. In this paper, we develop a new wireless resource scheduling method in high mobility scenario to uplift the system performance. We first propose a feature extraction algorithm to extract the spatial traffic characteristics. On this basis, several time-series prediction algorithms are leveraged to predict the traffic volume. Then, we introduce cell switching indicator (CSI) into channel quality prediction to improve the signal-to-noise ratio (SNR) prediction performance. Based on the predicted results of service traffic and user channel quality variations, a resource scheduling strategy is proposed, which compromises the total system throughput and user satisfaction. Simulation results validate the effectiveness of the proposed scheduling algorithm.

Robust Rateless Spatially Coupled Repeat-Accumulate-Repeat Multi-User Codes on IDMA Systems

In this paper, we design a kind of robust rateless multi-user code, which is called spatially coupled repeat-accumulate-repeat (SC-RA-R) code, for the interleave-division multiple-access (IDMA) system. The code is constructed by serially concatenating a fixed outer spatially coupled repeat-accumulate (SC-RA) code with an adjustable inner irregular repetition code. It achieves rateless property by simply adjusting the parameters of the inner repetition code. The IDMA system with the proposed SC-RA-R multi-user codes can adapt to varying channel qualities and varying number of users via rateless property while keeping encoder and decoder implementation unchanged. Analytical extrinsic information transfer (EXIT) functions are derived to determine belief propagation (BP) decoding thresholds of SC-RA-R codes on the IDMA system. The optimization expression is also given under the constraint that repeat parameters are monotonically non-decreasing with the sum rate decreasing. The numerical results show that the maximum achievable sum rates of the SC-RA-R codes can be close to the Shannon bound for arbitrary channel qualities. Besides, our proposed SC-RA-R codes have better BP thresholds and bit-error-rate (BER) performances than the conventional optimal parallel-concatenate codes (PCC) and the optimal partially repeated spatially coupled low-density parity-check (PR-SC-LDPC) codes on IDMA systems.

A Comprehensive Overview of TCP Congestion Control in 5G Networks: Research Challenges and Future Perspectives.

In today’s data networks, the main protocol used to ensure reliable communications is the transmission control protocol (TCP). The TCP performance is largely determined by the used congestion control (CC) algorithm. TCP CC algorithms have evolved over the past three decades and a large number of CC algorithm variations have been developed to accommodate various network environments. The fifth-generation (5G) mobile network presents a new challenge for the implementation of the TCP CC mechanism, since networks will operate in environments with huge user device density and vast traffic flows. In contrast to the pre-5G networks that operate in the sub-6 GHz bands, the implementation of TCP CC algorithms in 5G mmWave communications will be further compromised with high variations in channel quality and susceptibility to blockages due to high penetration losses and atmospheric absorptions. These challenges will be particularly present in environments such as sensor networks and Internet of Things (IoT) applications. To alleviate these challenges, this paper provides an overview of the most popular single-flow and multy-flow TCP CC algorithms used in pre-5G networks. The related work on the previous examinations of TCP CC algorithm performance in 5G networks is further presented. A possible implementation of TCP CC algorithms is thoroughly analysed with respect to the specificities of 5G networks, such as the usage of high frequencies in the mmWave spectrum, the frequent horizontal and vertical handovers, the implementation of the 5G core network, the usage of beamforming and data buffering, the exploitation of edge computing, and the constantly transmitted always-on signals. Moreover, the capabilities of machine learning technique implementations for the improvement of TCPs CC performance have been presented last, with a discussion on future research opportunities that can contribute to the improvement of TCP CC implementation in 5G networks. This survey paper can serve as the basis for the development of novel solutions that will ensure the reliable implementation of TCP CC in different usage scenarios of 5G networks.

Optimum Placement of Relay Nodes in WBANs for Improving the QoS of Indoor RPM System

Pandemic situation such as COVID-19 boosted the demand for remote patient monitoring (RPM) system. The medical sensors attached to the human body in RPM system experience varying channel quality due to body movements. This paper analyzes the signal received from RPM sensors when a patient rotates by different angles while sitting on a chair as well as heed the use of a relay node placed on his/her body. Literature suggests many relay-based communication protocols to deliver physio-signals efficiently in an RPM application. However, limited studies have focused on the position of a relay node on the human body. In this paper, we empirically analyze the off-body communication path of sensor nodes by collecting data from different body orientations in a residential room. We estimate the path loss parameters for underweight, normal and overweight body mass index (BMI) categories. The estimated parameters are then used to simulate the physical layer of a home-based indoor RPM application. We inspect different relay node positions on the human body and allude an optimal position of the relay node that cover the transmission range of all sensors and provides an improved channel quality. We improve the Quality of Service (QoS) during non-line-of-sight (NLOS) situation and design an adaptive cross-layer communication protocol for WBANs.

Miniature Dual-Band Substrate Integrated Waveguide Slotted Antenna Array for Millimeter-Wave 5G Applications

A compact substrate integrated waveguide (SIW) antenna array that operates at 28 GHz and 38 GHz is proposed for fifth generation (5G) applications. The proposed array consists of four SIW cavities fabricated on one single layer of substrate. Each cavity implements a rhombic slot and a triangular-split-ring slot, resonating on TE101 and TE102 modes at 28 GHz and 38 GHz, respectively. In comparison with dual-band SIW antennas in the literature, the proposed configuration depicts a miniature footprint (28.7 × 30.8 mm2) without stacking substrates. To excite the four cavities with equal power, a broadband power divider that supports the propagation of TE10 mode is designed. Accordingly, the impedance bandwidths are 26.6–28.3 GHz and 36.8–38.9 GHz. The measured realized peak gain over the lower and higher bands is 9.3–10.9 dBi and 8.7–12.1 dBi, respectively. The measured half-power beam widths (HPBWs) at 28 GHz and 38 GHz are 20.7° and 15.0°, respectively. Considering these characteristics, including dual bands, high gain, narrow beam widths, miniaturization, and single layer, the proposed antenna array is a suitable candidate for millimeter-wave 5G communication systems with the flexibility in switching operating frequency bands against channel quality variations.

Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks.

The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user’s perspective as well as from the channel’s perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte–Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user’s satisfaction and channel utilization was also analyzed.

Communication-Efficient Edge AI: Algorithms and Systems

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields, ranging from speech processing, image classification to drug discovery. This is driven by the explosive growth of data, advances in machine learning (especially deep learning), and the easy access to powerful computing resources. Particularly, the wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data, which provides the opportunity to derive accurate models and develop various intelligent applications at the network edge. However, such enormous data cannot all be sent to the cloud for processing, due to the varying channel quality, traffic congestion and/or privacy concerns, and the enormous energy consumption. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative. AI at the edge requires close cooperation among edge devices , such as smart phones and smart vehicles, and edge servers at the wireless access points and base stations, which however result in heavy communication overheads. In this paper, we present a comprehensive survey of the recent developments in various techniques for overcoming these communication challenges. Specifically, we first identify key communication challenges in edge AI systems. We then introduce communication-efficient techniques, from both algorithmic and system perspectives for training and inference tasks at the network edge. Potential future research directions are also highlighted.

Whittle index approach to opportunistic scheduling with partial channel information

Opportunistic scheduling in wireless cellular systems utilizes random channel quality variations in time by favoring the users with good channel conditions. However, the success of such schedulers is heavily depending on the accuracy of the available information on the channel states of users. In this paper, we consider the opportunistic scheduling problem of downlink data traffic with partial channel information, where the target is to minimize the flow-level holding costs. In earlier works, the Whittle index approach has successfully been utilized to develop near-optimal scheduling policies for the corresponding problems, however, typically with exact channel information. Using the same approach, we complement and extend the results found thus far. More specifically said, our novel contributions are (i) proving that the flow-level opportunistic scheduling problem with partial channel information is indexable at least in certain parts of the parameter space; (ii) deriving an explicit formula for the corresponding Whittle index; (iii) proving that, in these parts of the parameter space, the optimal policy for the relaxed optimization problem is of threshold type; and (iv) demonstrating that, in the remaining parts of the parameter space, it is possible that the optimal policy for the relaxed problem is not of threshold type. In addition, we evaluate the performance of the derived Whittle index policies and compare them with some greedy policies by numerical simulations.

SCRAS Server-Based Crosslayer Rate-Adaptive Video Streaming over 4G-LTE for UAV-Based Surveillance Applications

This research focuses on intelligent unmanned aerial vehicle (UAV)-based, real-time video surveillance to ensure better monitoring and security of remote locations over 4G-LTE cellular networks by maximizing end-user quality of experience (QoE). We propose a novel server-based crosslayer rate-adaptive scheme (SCRAS) for real-time video surveillance over 4G-LTE networks using UAVs. Our key contributions are: (1) In SCRAS, mobile UAVs having preprogrammed flight co-ordinates act as servers, streaming real-time video towards a remote client; (2) server-side video rate adaptation occurs in 4G-LTE based on the physical characteristics of the received signal conditions due to variations in the wireless channel and handovers; (3) SCRAS is fully automated and independent of client assistance for rate adaptation, as it is intended for real-time, mission-critical surveillance applications; (4) SCRAS ensures that during rate adaptation, the current video frame should not be damaged by completing the current group of packet (GoP) before adaptation. Our simulations in NS3 provide credible evidence that SCRAS outperforms recently proposed schemes in providing better QoE for real-time, rate-adaptive, video surveillance over 4G-LTE under varying channel quality and frequent handovers that occur during flight by UAVs.

Channel-Quality Aware RFID Tag Identification Algorithm to Accommodate the Varying Channel Quality of IoT Environment

Radio Frequency Identification (RFID) technique is broadly adopted as the automated identification system for the Internet of Things (IoT). Many RFID anti-collision algorithms were proposed to accelerate the tag identification process. However, they misjudged some unreadable slots which were due to collision instead of the bad channel condition, causing low bandwidth usage. This study proposes the Channel-quality Aware Query Tree algorithm (CAQT) to improve the identification performance in an error-prone channel environment. CAQT has three novel features: (1) it estimates the channel quality continuously and statistically in the rapidly changing channel quality environment; (2) it asks the tag for retransmission or to split the collide tags based on the channel quality; (3) the number of the groups which it splits tags is based on the estimated number of tags collide in current slot. The simulation results show that CAQT uses less than 31% slots compared with the conventional algorithms. The simulation results also demonstrate that CAQT provides enhanced performance when the channel quality is varying especially in outdoor environment, for example, ticket checking for railway or subway system.

Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications.

Opportunistic scheduling with flow size information for Markovian time-varying channels

Opportunistic scheduling refers to algorithms that try to exploit the random variations of the physical layer channel quality in wireless systems for the allocation of radio resources. As indicated by some recent papers, a promising approach to optimize the resource allocation in such a context is to utilize the notion of Whittle index, originally developed for restless multi-armed bandits. In this paper, we apply the Whittle index approach for the opportunistic scheduling problem of downlink data flows assuming two-state Markovian time-varying channels. Until now, this has been done only for geometric flow sizes. Our aim is to allow arbitrary flow size distributions and study how to optimally combine opportunistic scheduling with exact flow size information. We use a phase-type approximation for the flow sizes to make the problem amenable to the Whittle index approach. In the first step, we show that the opportunistic scheduling problem is indexable for Erlang distributed flow sizes and derive the corresponding Whittle index, which generalizes earlier results. In the second step, we utilize these results to develop a size-aware index policy for the original problem. The result is also heuristically generalized to cover Markovian channels with multiple states. By simulation-based numerical studies, we demonstrate that the resulting size-aware index policy is able to appropriately make use of the more exact size information and thus systematically improves performance when compared to earlier developed schedulers.

Robust QoS-Aware Cross-layer Design of Adaptive Modulation Transmission on OFDM Systems in High-Speed Railway

In this paper, we consider an orthogonal frequency division multiplexing communication system that adopts frame-by-frame transmission in high-speed railway (HSR) scenario. Due to the increase in demand for the QoS sensitive services, an efficient QoS-aware transmission strategy that improves the system performance is required urgently. Many efforts have been devoted to addressing this problem with the assumption of block fading channel in a frame duration. However, due to the frequent channel quality variation in a frame duration and serious inter channel interference in HSR scenario, the throughput of the QoS sensitive services degrades severely. Hence, a robust cross-layer transmission strategy that combines adaptive modulation (AM) scheme with truncated automatic repeat request protocol is proposed. In this cross-layer formulation, the normalized average throughput is optimized subject to the average power and the packet loss rate (PLR) requirements. First, we derive the closed form average bit error rate that represents the PLR requirement at the physical layer. Second, we obtain the solution of robust AM scheme and power allocation policy in the case of continuous rate. Third, we present the adaptive bits and power allocation scheme in the case of discrete rate, which can be implemented in practice. Finally, the performance of the proposed transmission strategy is evaluated by extensive simulations. Comparing with the constant transmit power AM scheme, the throughput increases by 20%, which demonstrates that the proposed robust cross-layer design is suitable for the HSR communication systems.

Radio Resource Allocation on Complex 4G Wireless Cellular Networks

In this article we consider the heuristic algorithm which improves step by step wireless data delivery over LTE cellular networks by using the total transmit power with the constraint on users’ data rates, and the total throughput with the constraints on the total transmit power as well as users’ data rates, which are jointly integrated into a hybrid-layer design framework to perform radio resource allocation for multiple users, and to effectively decide the optimal system parameter such as modulation and coding scheme (MCS) in order to adapt to the varying channel quality. We propose new heuristic algorithm which balances the accessible data rate, the initial data rates of each user allocated by LTE scheduler, the priority indicator which signals delay- throughput- packet loss awareness of the user, and the buffer fullness by achieving maximization of radio resource allocation for multiple users. It is noted that the overall performance is improved with the increase in the number of users, due to multiuser diversity. Experimental results illustrate and validate the accuracy of the proposed methodology.

Cross-layer quality enhancement scheme for video transmission over multi-hop wireless networks

Wireless networks have a varying channel quality and a limited bandwidth. Therefore, ensuring a satisfactory video quality over wireless networks is extremely challenging. Most existing studies for enhancing the received video quality over wireless networks consider only single-hop transmission environments. Consequently, when applied to multi-hop wireless networks, such as wireless mesh networks, VANETs or wireless sensor networks, the video quality is typically rather poor. Accordingly, this study proposes a cross-layer mechanism for improving the video transmission quality in multi-hop wireless networks. The proposed scheme considers both the video frame type and the output interface queue length at the intermediate nodes while determining the drop probability during the packet forwarding process. Besides, in the event of transmission failures, the number of retransmission attempts is varied adaptively in accordance with the network conditions and the relative priority of the corresponding frame. Furthermore, the forwarding probability at each node is increased in accordance with the number of hops previously traversed by the packet. Thus, the proposed scheme improves video delivery quality by means of providing unequal protection for frames of different importance. The NS2 simulation results show that the average PSNR results obtained with the proposed scheme have significant improvements compared to that obtained with the traditional IEEE 802.11 mechanism, thereby confirming the effectiveness of the proposed scheme.

Video Multicasting With Channel Diversity in Wireless OFDMA Networks

The demand for real-time video services coupled with the need for increased spectral efficiencies has brought a lot of attention to video multicasting in next generation OFDMA systems. Layered video provides an efficient solution to address the channel quality variation across users. However, the frequency selectivity across sub-channels introduced by OFDMA serves as a drawback for multicast scheduling. This significantly diminishes the benefits of video layering and can potentially degrade the system performance to worse than that without channel diversity. Layering the PHY with the help of multi-resolution modulation serves as an effective tool to help retain channel diversity and leverage the benefits of layered video. While layering the PHY provides benefits to multicast in general, we show that its benefits are much more significant in a system with channel diversity. The corresponding multicast scheduling problem now becomes all the more challenging with multi-resolution modulation. In addressing this problem, we provide efficient scheduling solutions both for systems with and without channel diversity. Our solutions have provable performance guarantees and are amenable to practical implementation. Further, evaluation in realistic scenarios reveals close to optimal performance of our proposed solutions as well as the large benefits obtained by layering the PHY in OFDMA systems with channel diversity.

Robust and Optimal Opportunistic Scheduling for Downlink Two-Flow Network Coding With Varying Channel Quality and Rate Adaptation

This paper considers the downlink traffic from a base station to two different clients. When assuming infinite backlog, it is known that inter-session network coding (INC) can significantly increase the throughput. However, the corresponding scheduling solution (when assuming dynamic arrivals instead and requiring bounded delay) is still nascent. For the two-flow downlink scenario, we propose the first opportunistic INC + scheduling solution that is provably optimal for time-varying channels, i.e., the corresponding stability region matches the optimal Shannon capacity. In particular, we first introduce a new binary INC operation, which is distinctly different from the traditional wisdom of XORing two overheard packets. We then develop a queue-length-based scheduling scheme and prove that it, with the help of the new INC operation, achieves the optimal stability region with time-varying channel quality. The proposed algorithm is later generalized to include the capability of rate adaptation. Simulation results show that it again achieves the optimal throughput with rate adaptation. A byproduct of our results is a scheduling scheme for stochastic processing networks with random departure, which relaxes the assumption of deterministic departure in the existing results.