Varying Wireless Channel Research Articles

Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding

Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable communications. In this approach, bits are treated equally, and the communication system is oblivious to what meaning these bits convey or how they would be used. Future communications towards intelligence and conciseness will predictably play a dominant role, and the proliferation of connected intelligent agents requires a radical rethinking of coded transmission paradigm to support the new communication morphology on the horizon. The recent concept of "semantic communications" offers a promising research direction. Injecting semantic guidance into the coded transmission design to achieve semantics-aware communications shows great potential for further breakthrough in effectiveness and reliability. This article sheds light on semantics-guided source and channel coding as a transmission paradigm of semantic communications, which exploits both data semantics diversity and wireless channel diversity together to boost the whole system performance. We present the general system architecture and key techniques, and indicate some open issues on this topic.

Federated Learning With Over-the-Air Aggregation Over Time-Varying Channels

We study federated learning (FL) with over-the-air aggregation over time-varying wireless channels. Independent workers compute local gradients based on their local datasets and send them to a parameter server (PS) through a time-varying multipath fading multiple access channel via orthogonal frequency-division multiplexing (OFDM). We assume that the workers do not have channel state information, hence the PS employs multiple antennas to alleviate the fading effects. Wireless channel variations result in inter-carrier interference, which has a detrimental effect on the performance of OFDM systems, especially when the channel is rapidly varying. We examine the effects of the channel time variations on the convergence of the FL with over-the-air aggregation, and show that the resulting undesired interference terms have only limited destructive effects, which do not prevent the convergence of the learning algorithm. We also validate our results via extensive simulations, which corroborate the theoretical expectations.

Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges

Sixth generation (6G) internet of things (IoT) networks will modernize the applications and satisfy user demands through implementing smart and automated systems. Intelligence-based infrastructure, also called reconfigurable intelligent surfaces (RISs), have been introduced as a potential technology striving to improve system performance in terms of data rate, latency, reliability, availability, and connectivity. A huge amount of cost-effective passive components are included in RISs to interact with the impinging electromagnetic waves in a smart way. However, there are still some challenges in RIS system, such as finding the optimal configurations for a large number of RIS components. In this paper, we first provide a complete outline of the advancement of RISs along with machine learning (ML) algorithms and overview the working regulations as well as spectrum allocation in intelligent IoT systems. Also, we discuss the integration of different ML techniques in the context of RIS, including deep reinforcement learning (DRL), federated learning (FL), and FL-deep deterministic policy gradient (FL-DDPG) techniques which are utilized to design the radio propagation atmosphere without using pilot signals or channel state information (CSI). Additionally, in dynamic intelligent IoT networks, the application of existing integrated ML solutions to technical issues like user movement and random variations of wireless channels are surveyed. Finally, we present the main challenges and future directions in integrating RISs and other prominent methods to be applied in upcoming IoT networks.

Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. In traditional communication systems, the compressed CSI bits is treated equally and expected to be transmitted accurately over the noisy channel. While the errors occur due to the limited bandwidth or low signal-to-noise ratios (SNRs), the reconstruction performance of the CSI degrades drastically. As a branch of semantic communications, deep joint source-channel coding (DJSCC) scheme performs better than the separate source-channel coding (SSCC) scheme—the cornerstone of traditional communication systems—in the limited bandwidth and low SNRs. In this paper, we propose a DJSCC based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

A Novel 5G-NR Resources Partitioning Framework Through Real-Time User-Provider Traffic Demand Analysis

Network slicing (NS) is a key enabler of the 5G and beyond network architectures, allowing multiple dedicated logical networks with selected functionality to be executed on top of a common infrastructure. At the radio access network, novel flexibility paradigms and dynamic response to wireless channel variations are necessary for any NS solution. In this framework, this article proposes a novel real-time NS management framework for the 5G New-Radio, where the slice resource management is achieved through a joint dynamic evaluation of served users’ quality of service and tenants’ service level agreement. The design of the proposed framework within the standardized 5G architecture is discussed, together with the integration of the dynamic resource assignation procedure within the slice life cycle workflow. A novel mathematical model is proposed for the dynamic resource assignation within the slice life cycle workflow. The solution’s accuracy is tested by means of computer simulations, and found to be satisfactory according to the proposed evaluation metrics. Finally, the practicality of the optimal radio slicing configuration extracted from the proposed model is applied to our experimental platform, and the performance of a real case scenario is comprehensively evaluated.

Energy-Aware Distributed Edge ML for mHealth Applications With Strict Latency Requirements

Edge machine learning (Edge ML) is expected to serve as a key enabler for real-time mobile health (mHealth) applications. However, its reliability is governed by the limited energy and computing resources of user equipment (UE), along with the wireless channel variations and dynamic resource allocation at edge servers. In this letter, we incorporate both UE and edge server computing to satisfy the strict latency requirements of mHealth applications while efficiently utilizing the UE’s energy resources. Specifically, we separate the feature extraction and classification processes of Edge ML inference and formulate an optimization problem to distribute them between the UE and the edge server while determining the optimal UE transmit power. We demonstrate the effectiveness of the proposed approach using an mHealth case study for predicting epileptic seizures using data from wearable health devices.

Deep Reinforcement Learning Based Adaptive Modulation With Outdated CSI

The problem of adaptive modulation with outdated channel state information (CSI) is considered. Best existing approach to tackle this problem relies on using a (non-)linear auto-regressive moving average (ARMA) model to predict current CSI from outdated values. This approach is valid only if the wireless channel variations over time behave in a linear or smooth enough nonlinear fashion, which is not necessarily the case. We propose a deep reinforcement learning based adaptive modulation (DRL-AM) approach that can handle this limitation. While DRL-AM is more complex than (non-)linear AR(MA), it performs significantly better as corroborated via numerical results on real channel measurements. Furthermore, compared to capacity-achieving codes, complexity is moved from receiver to transmitter making this approach suitable for receiving nodes with limited resources such as internet of things (IoT) devices.

Adaptive Bitrate Video Transmission Over Cognitive Radio Networks Using Cross Layer Routing Approach

Due to the recent developments in the wireless mesh and ad-hoc networks, multi-hop cognitive radio networks (MCRNs) have attained the significant attention towards providing the reliable multimedia communications. However, in reliable multimedia communications each multimedia application observed a very stringent quality-of-service (QoS) requirements. Moreover, in MCRNs, channel allocated to the multimedia secondary users (MSUs) can be re-occupied by the primary users (PUs) at any time which causes the end-to-end path discontinuity that severely affect the performance of the MCRNs. Therefore, under the dynamic channel availability, selecting an end-to-end path that is not only stable but also fulfills the QoS requirements of the real time and multimedia (RM) applications is a challenging task and still an open research problem. Hence, in this paper, we propose a cross-layer routing scheme that supports adaptive bit-rate multimedia (ABM) transmissions over MCRNs. Moreover, our path selection is based on the QoS-aware end-to-end path delay, and PU-activity aware end-to-end path stability metrices. Furthermore, to avoid the PU interference, continuity in transmission, efficient channel utilization, and supporting error resilience over time varying wireless channels our selected end-to-end path is periodically updated. Simulation study shows that the proposed scheme is more robust and suitable for supporting ABM over MCRNs.

Adaptive delay-constrained resource allocation in mobile edge computing for Internet of Things communications networks

The traffic burden at each node in Internet-of-Things (IoT) communication networks becomes prohibitively high especially when involving exhaustive computation. Mobile edge computing (MEC) makes this complicated computation feasible while alleviates the traffic burden by providing the corresponding node with powerful computing resources through wireless transmission between the node and the MEC for offloading computation. However, the transmission via the varying wireless channel requires considerable energy consumption and imposes delay. In this paper, we study the trade-off between the energy consumption and the delay performance in IoT network due to the offloading computation and the wireless communication. An optimization problem involving the offloading ratio for MEC as unknown parameter is established by minimizing the total energy consumption subject to a delay constraint. The problem is then solved by analyzing the convexity of the cost function and the constraint. Moreover, the scaling law of both energy cost and delay performance of IoT networks is investigated with respect to the number of nodes employing the MEC. It is discovered that the delay performance decreases in the logarithm with increasing the number of nodes while the energy cost grows linearly with the increase of the number of nodes. Numerical simulations verifying the performances of the proposed method in the studied IoT networks with MEC are provided.

Kalman Filter Based Recursive Estimation of Slowly Fading Sparse Channel in Impulsive Noise Environment for OFDM Systems

In this paper, we propose a recursive sparse channel estimation algorithm in the presence of impulse noise. Firstly the channel impulse response and impulsive noise are jointly viewed as an unknown sparse vector. Then a novel recursive Kalman filtering based compressed sensing algorithm for joint channel and impulsive noise estimation is proposed by using the first order autoregressive model for tracking slowly time varying wireless channel. This algorithm can be extended also to quasi-static, block-fading scenario conveniently. Simulation results illustrate the efficiency of the proposed techniques in terms of the mean square error and bit error rate performance.

Task Allocation in Fog-Aided Mobile IoT by Lyapunov Online Reinforcement Learning

Fog-aided mobile IoT is proposed to speed up service response by deploying fog nodes at network edges. We investigate the task allocation in fog-aided mobile IoT networks, where mobile users generate computing tasks at different locations and offload them to fog nodes, i.e., to intelligently distribute tasks to different fog nodes in order to adapt to the varying wireless channel conditions and different fog resources. The objective is to minimize the average task completion time constrained by the mobile device’s battery capacity and each task’s completion deadline. In practice, future tasks are usually unknown in advance owing to the unpredictable environments and hence an online algorithm is required to make decisions on the fly. Moreover, the local task information may be incomplete and hence historical statistics should be utilized to estimate the most appropriate fog node for the current task. Therefore, we design an online reinforcement learning algorithm to address the two challenges. We also derive and analyze the computational complexity and theoretical bound. Simulation results show that our online algorithm achieves the optimal performance asymptotically, illustrate the performances of our online reinforcement learning algorithm as compared with existing works, and validate the theoretical bound analysis.

QoS-Aware Power Control in Internet of Drones for Data Collection Service

Internet of Drones (IoD) utilizes drones as the Internet of Things devices to collect information (e.g., air pollutant level and traffic condition) over different points of interests, and has been explored in several applications such as object tracking and traffic surveillance. However, owing to the limited battery capacity of drones, energy efficient strategies are required in IoD systems. Meanwhile, the varying wireless channel conditions as a drone flies in the air may degrade the user quality of service (QoS). Power control, which adapts to the varying channel conditions and also controls the energy consumption, helps address these challenges. In this paper, we investigate power control in IoD for the data collection service to minimize the energy consumption of a drone while meeting the QoS requirement. The problem is formulated as a sum-of-ratios fractional programming problem, which is NP-complete. We then propose a PowEr conTROL (PETROL) algorithm to solve this problem and derive its rate of convergence. Extensive simulations demonstrate the performances of PETROL.

QoS-Aware Fog Resource Provisioning and Mobile Device Power Control in IoT Networks

Fog-aided Internet of Things (IoT) addresses the resource limitations of IoT devices in terms of computing and energy capacities, and enables computational intensive and delay-sensitive tasks to be offloaded to the fog nodes attached to the IoT gateways. A fog node, utilizing the cloud technologies, can lease and release virtual machines (VMs) in an on-demand fashion. For the power-limited mobile IoT devices (e.g., wearable devices and smart phones), their quality of service may be degraded owing to the varying wireless channel conditions. Power control helps maintain the wireless transmission rate and hence the quality of service (QoS). The QoS (i.e., task completion time) is affected by both the fog processing and wireless transmission; it is thus important to jointly optimize fog resource provisioning (i.e., decisions on the number of VMs to rent) and power control. This paper addresses this joint optimization problem to minimize the system cost (VM rentals) while guaranteeing QoS requirements, formulated as a mixed integer nonlinear programming problem. An approximation algorithm is then proposed to solve the problem. Simulation results demonstrate the performance of our proposed algorithm.

OmniCast: Wireless Pseudo-Analog Transmission for Omnidirectional Video

Wireless virtual reality (VR) applications that provide users extraordinary viewing experience are now drawing great attentions. Transmitting VR video via wireless channel to users’ head-mounted display devices efficiently with low latency is very important for many emerging VR applications. In this paper, we propose a pseudo-analog transmission framework named OmniCast, which provides graceful quality degradation and competitive performance for unpredictably varying wireless channels while featuring low latency, low complexity, and low energy cost. In particular, we analyze the influence of projection between the spherical representation and the 2-D plane representation, and derive a power optimization scheme to minimize the distortion on the sphere. In addition, we develop an approach to measure the efficiency of decorrelation transform in the spherical domain. Based on that, an appropriate transform option can be determined. Experimental results show that the proposed framework improves the transmission efficiency of omnidirectional videos, while achieving elegant quality degradation for channel fluctuation in a wide channel SNR range.

Switching Frequency Techniques for Universal Ambient Backscatter Networking

This work offers both analog and digital tag modulation schemes and respective receiver designs, for ultra-low power, high performance, ambient backscatter communications. All proposed techniques are based on simple, but careful, switching frequency control at the tag, allowing for the easy frequency-domain multiple access. First, a digital modulation scheme is offered, namely pseudo-frequency shift keying, assuming illumination from constant envelope-modulated signals and a fully coherent detector is derived along with closed-form probability of error. A second digital modulation scheme is also offered, based on a frequency-shifted form of binary phase shift keying (S-BPSK), relaxing the constant-envelope requirement for the illuminator and an illumination-agnostic detector is derived. Based on S-BPSK, short packet error correction coding is utilized for ambient backscatter communication, for the first time in the literature. It is shown that the proposed coded scheme under modulated ambient signal illumination & wireless channel variation, offers tremendous performance gains, i.e., modulation of the ambient signal is helpful. Finally, a third, purely analog, modulation scheme is analyzed, based on FM remodulation principles. A low-cost tag is implemented, demonstrating tag-to-receiver ranges up to 26 meters outdoors, power consumption of 24 μWatts in continuous operation, able to be interrogated by any conventional frequency modulation (FM) receiver. The proposed techniques cover a large variety of omnipresent wireless industry systems, enabling universal ambient backscatter and relevant wireless information and power transfer (WIPT) applications.

Exploiting Shadowing Stationarity for Antenna Selection in V2V Communications

Antenna selection (AS) techniques are considered as ideal candidates for vehicle-to-vehicle communications, since they improve system's performance and simultaneously satisfy the hardware and signal processing constraints that exist in these systems. However, the achieved gain over single antenna links is affected by the fast varying wireless channel, since AS is frequently performed using outdated versions of the signal-to-noise ratio (SNR). In this paper, we propose an AS technique that exploits the stationarity of large-scale fading. In this context, by employing shadowing information as an AS criterion, the negative consequences of the outdated channel state information (CSI) can be alleviated, since large-scale fading varies more slowly than small-scale fading. The performance of the proposed technique is analyzed using the criteria of outage probability and average output SNR. It is shown that the proposed scheme outperforms the corresponding one that is based on outdated CSI, especially in scenarios with mild fading/shadowing channel conditions. Moreover, the influence of correlated shadowing on the system's performance has been also analytically investigated. The main results have been also verified by empirical data based on measurement campaigns in non-stationary communication conditions.

A Markovian Model for Analyzing Opportunistic Request Routing in Wireless Cache Networks

In this paper, we investigate the request routing delay of opportunistic routing for cache-enabled wireless networks considering uncorrelated and temporally correlated wireless channels. We model wireless channel variation at different time scales via two approaches: an abstract modeling approach where we model the variation considering Rayleigh fading and shadowing channel models and an empirical approach where the variation is modeled directly using signal strength measurements. We develop Markovian models to analyze the performance of opportunistic forwarding, leverage the wireless channel models to determine the packet transmission success probabilities and then utilize them to obtain the request routing delay. We first perform numerical experiments and simulations considering Rayleigh fading and shadowing channel models and then conduct a trace-based evaluation using signal strength measurements collected over a wireless sensor network testbed. Our experiments demonstrate the validity and effectiveness of our Markovian model in determining the request routing delay in real-world settings. Our work takes a step forward in providing network operators a tool for analyzing network performance before deploying their networks.

Adaptive Distributed Association in Time-Variant Millimeter Wave Networks

The underutilized millimeter-wave (mm-wave) band is a promising candidate to enable extremely high data rate communications in future wireless networks. However, the special characteristics of the mm-wave systems such as high vulnerability to obstacles (due to high penetration loss) and to mobility (due to directional communications) demand a careful design of the association between the clients and access points (APs). This challenge can be addressed by distributed association techniques that gracefully adapt to wireless channel variations and client mobilities. We formulated the association problem as a mixed-integer optimization aiming to maximize the network throughput with proportional fairness guarantees. This optimization problem is solved first by a distributed dual decomposition algorithm, and then by a novel distributed auction algorithm where the clients act asynchronously to achieve near-to-optimal association between the clients and APs. The latter algorithm has a faster convergence with a negligible drop in the resulting network throughput. A distinguishing novel feature of the proposed algorithms is that the resulting optimal association does not have to be re-computed every time the network changes (e.g., due to mobility). Instead, the algorithms continuously adapt to the network variations and are thus very efficient. We discuss the implementation of the proposed algorithms on top of existing communication standards. The numerical analysis verifies the ability of the proposed algorithms to optimize the association and to maintain optimality in the time-variant environments of the mm-wave networks.

Secure and Efficient Content Distribution in Crowdsourced Vehicular Content-Centric Networking

In future intelligent transportation systems, a large amount of content needs to be efficiently and securely exchanged between vehicles and roadside units via vehicular networks to improve the driving and traveling experience. To solve the challenges caused by poor-quality wireless links and the mobility of vehicles, vehicular content-centric networking (VCCN) emerges as a promising paradigm, which has a better content distribution efficiency, mobility, and security via named data and in-networking caching compared with an IP-based network. However, providing a high-quality experience for content distribution in VCCN is challenging due to the dynamic network topologies, varying wireless channel conditions, and vehicle user privacy. In this paper, we propose a novel crowdsourced VCCN framework for secure and efficient content distribution. This framework enables the nearby vehicles to crowdsource their caching resources and radio links for cooperative content distribution. We formulate the problem as the maximization of all users’ payoff and propose an online scheduling method to solve this solution. Furthermore, we adopt identity-based proxy reencryption and named function networking to secure the process of content distribution. The simulation results show that our proposals improve the performance of VCCN in terms of average requester utility compared with original CCN forwarding strategies.

Exploiting Temporal Correlation in Wireless Channel for Energy-Efficient Communication

Widespread adoption of Internet-of-Things (IoT) technology depends on cost-affordability of the devices and convenience of their usage in terms of long life. Energy-efficiency of the miniaturized wireless IoT devices is a key to the cost and convenience factors. While there have been a few prior studies on wireless channel adaptive communication strategies, in this paper we relook at the problem aiming at effectively characterizing the wireless fading channel and devising energy-efficient link-layer retransmission strategy suitable for IoT devices. In particular, we propose a new channel-adaptive strategy that depends on the rate of variation of wireless channel irrespective of the underlying channel fading distribution. We further show that the concept of average fade duration dependent retransmission is a special case of the proposed strategy. Extensive simulations show that the proposed scheme most effectively characterizes the temporal variations of wireless channel in comparison with the other existing schemes. Performance of the proposed retransmission strategy is compared with the competitive protocols using the Markov model. Numerical results demonstrate that, the proposed scheme offers a gain of about 9% in terms data throughput and about 12% in terms of energy efficiency in comparison to its nearest existing benchmark scheme.