Time-varying Channels Research Articles

Optimal Power Allocation in Wireless Communication Networks Using Stochastic Control

Wireless communication networks operate over time-varying channels that depend on channel gains, power allocation algorithms, and inter-channel interference. In this paper, We present techniques for computing the optimal control inputs, which lead to the attainment of the intended Signal to Interference plus Noise ratio (SINR) at different receivers. This optimization problem can be efficiently resolved using non-linear programming tools and permits the simultaneous assignment of multiple desired SINRs at different instances. We establish the conditions necessary to ensure the feasibility of the optimal control problem.

Ratio Shift Keying Modulation for Time-Varying Molecular Communication Channels.

Molecular Communications (MC) is a bio-inspired communication technique that uses molecules to encode and transfer information. Many efforts have been devoted to developing novel modulation techniques for MC based on various distinguishable characteristics of molecules, such as their concentrations or types. In this paper, we investigate a particular modulation scheme called Ratio Shift Keying (RSK), where the information is encoded in the concentration ratio of two different types of molecules. RSK modulation is hypothesized to enable accurate information transfer in dynamic MC scenarios where the time-varying channel characteristics affect both types of molecules equally. To validate this hypothesis, we first conduct an information-theoretical analysis of RSK modulation and derive the capacity of the end-to-end MC channel where the receiver estimates concentration ratio based on ligand-receptor binding statistics in an optimal or suboptimal manner. We then analyze the error performance of RSK modulation in a practical time-varying MC scenario, that is mobile MC, in which both the transmitter and the receiver undergo diffusion-based propagation. Our numerical and analytical results, obtained for varying levels of similarity between the ligand types used for ratio-encoding, and varying number of receptors, show that RSK can significantly outperform the most commonly considered MC modulation technique, concentration shift keying (CSK), in dynamic MC scenarios.

Design of Farrow Structured Variable Fractional Delay Filter for Time-Varying LEO Communication Channel Emulator With SRRC Communication Waveforms

Design of Farrow Structured Variable Fractional Delay Filter for Time-Varying LEO Communication Channel Emulator With SRRC Communication Waveforms

Joint Beam Alignment and Doppler Estimation for Fast Time-varying Wideband mmWave Channels

Joint Beam Alignment and Doppler Estimation for Fast Time-varying Wideband mmWave Channels

Adaptive Channel Estimation for RIS-Assisted Systems in Time-Varying mmWave Channels

Adaptive Channel Estimation for RIS-Assisted Systems in Time-Varying mmWave Channels

Joint MCS Adaptation and RB Allocation in Cellular Networks Based on Deep Reinforcement Learning With Stable Matching

Joint modulation-coding scheme (MCS) adaptation and resource block (RB) allocation is an effective approach to guarantee different quality of service (QoS) requirements of all UEs under dynamic network environments. In this paper, we consider a fifth generation (5 G) cellular network with time-varying wireless channels, in which the BS serves multiple user equipments (UEs) under limited available RBs. We aim to minimize the total RB consumption subject to the rigorous constraints of each UE's QoS requirement. To attain this objective, this paper puts forth an online learning technique, referred to as integrated Deep Reinforcement learning and stable Matching (DeepRM), in the sense that the MCS adaptation and RB allocation decisions are conducted without acquiring the real-time channel quality indicator (CQI) feedback. DeepRM is a closed-loop framework, where the output of deep reinforcement learning (DRL) is imported into the stable matching to guide optimal RB allocation whilst the output of stable matching is fed into the DRL framework to assist efficient MCS decision-making. Specifically, in DeepRM, we first develop a powerful DRL algorithm, termed as Action-and-Reward Branching Deep Q-network (ARBDQ), by incorporating the action branch architecture into conventional DRL and modifying the traditional deep neural network training mechanism, to perform judicious MCS decisions on different links in parallel. Then, a new many-to-one stable matching algorithm, called adaptive deferred acceptance, is exploited to dynamically adjust the RB quota of each UE in a computationally efficient fashion. Simulation results demonstrate that compared with ACO-HM, OLLA-ADA, and ARBDQ-Random algorithms, DeepRM induces much less RB consumption while guaranteeing the QoS requirements of all UEs in various network scenarios. Furthermore, under miscellaneous QoS requirement, number of UEs, and CQI reporting period setups, DeepRM is more robust than other baselines.

Over-the-Air Federated Learning Under Time-Varying Wireless Channels Using OTFS

Over-the-Air Federated Learning Under Time-Varying Wireless Channels Using OTFS

FEC-Aided Decision Feedback Blind Mismatch Calibration of TIADCs in Wireless Time-Varying Channel Environments

FEC-Aided Decision Feedback Blind Mismatch Calibration of TIADCs in Wireless Time-Varying Channel Environments

Investigating the WSSUS Assumption in 300 GHz Time-Variant Channels in Industrial Environments

Investigating the WSSUS Assumption in 300 GHz Time-Variant Channels in Industrial Environments

Joint DFO and channel estimation scheme for RIS assisted OFDM systems

In the reconfigurable intelligent surface (RIS) assisted mobile wireless communications, the performance gains provided by RIS cannot be achieved without the accurate channel estimation. Many time-varying channel estimation algorithms have been proposed for such systems, but none of them consider the effect of Doppler frequency offset (DFO) synchronization error, which can seriously degrade the accuracy of channel estimation, especially in the Rician fading channel for the orthogonal frequency division multiplexing (OFDM) systems. In our work, the effect of DFO synchronization error on the time-varying channel estimation is firstly analyzed, and then a deep learning based joint DFO and channel estimation scheme is proposed. Specifically, the threshold-based noise reduction algorithm and the dual convolution neural networks (CNN) are applied for improve the estimation accuracy. To enhance the practicability of the proposed method, the training target of CNN network is set as the actual estimation value with high accuracy, rather than the ideal information. The simulation results show that the proposed method exhibits superior performance compared to the existing algorithms, and it has an acceptable computational complexity.

Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5G RAN Network Slicing

Network slicing is a technique introduced by 3GPP to enable multi-tenant operation in 5 G systems. However, the support of slicing at the air interface requires not only efficient optimization algorithms operating in real time but also its tight integration into the 5 G control plane. In this paper, we first present a priority-based mechanism enabling defined performance isolation among slices competing for resources. Then, to speed up the resource arbitration process, we propose and compare several supervised machine learning (ML) techniques. We show how to embed the proposed approach into the ITU-T standardized ML architecture. The proposed ML enhancement is evaluated under realistic traffic conditions with respect to the performance criteria defined by GSMA while explicitly accounting for 5 G millimeter wave channel conditions. Our results show that ML techniques are able to provide suitable approximations for the resource allocation process ensuring slice performance isolation, efficient resource use, and fairness. Among the considered algorithms, polynomial regressions show the best results outperforming the exact solution algorithm by 5–6 orders of magnitude in terms of execution time and both neural network and random forest algorithms in terms of accuracy (by 20–40 %), sensitiveness to workload variations and training sample size. Finally, ML algorithms are generally prone to service level agreements (SLA) violation under high load and time-varying channel conditions, implying that an SLA enforcement system is needed in ITU-T's 5 G ML framework.

Doppler-Shift-Based Sybil Attack Detection for Mobile IoT Networks

The rapid growth of Internet of Things (IoT) networks brings new security challenges for service providers. Due to the resource constrained nature of IoT networks, conventional security methods are not always suitable. Therefore, physical layer security (PLS) has come to the forefront, providing a high level of security while respecting the limited resources of IoTs. A Sybil attack is an insider attack in which a malicious node illegitimately fakes multiple identities, to impersonate legitimate nodes in the IoT network. This study introduces a novel Sybil attack detection scheme for mobile IoT networks that corresponds to time-varying channel. Doppler shift caused by mobile IoT nodes is considered as a novel detection metric to identify available Sybil attacks. The proposed scheme is analyzed by both the true positive rate and the false positive rate, which are mathematically formulated to verify the simulation results. Results demonstrate that as the randomness in the mobility pattern of IoT nodes increases, the proposed detection mechanism based on Doppler shift offers improved identification of the Sybil nodes. Moreover, this work provides receiver operating characteristics (RoCs) for mobile IoT networks to evaluate the effect of different system parameters including carrier frequency, velocity, subcarrier spacing (SCS), and the size of cyclic prefix (CP). The performance of the proposed scheme improves with an increase in both the carrier frequency and velocity, as the Doppler shift becomes more pronounced.

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks.

Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented "end-edge-cloud" multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.

Orthogonal Space-Time Block Coding for Double Scattering V2V Links with LOS and Ground Reflections

This work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS.

Understanding Deep MIMO Detection

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most of the DL-based MIMO detection algorithms are lack of interpretation on internal mechanisms. In this paper, we analyze the performance of the DL-based MIMO detection to better understand its strengths and weaknesses. We investigate and compare two different models: data-driven DL detector with neural networks activated by rectifier linear unit (ReLU) function and model-driven DL detector based on traditional detection algorithms. We show that the data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires a large amount of training samples to converge in time-varying channels. On the other hand, the model-driven DL detector utilizes the expert knowledge to alleviate the impact of channels and achieves relatively high detection accuracy with a small set of training data. Simulation results confirm our analytical results and demonstrate the effectiveness of the DL-based MIMO detection for both linear and nonlinear signal systems.

Deep Learning Based Pilot-Free Transmission: Error Correction Coding for Low-Resolution Reception Under Time-Varying Channels

Deep Learning Based Pilot-Free Transmission: Error Correction Coding for Low-Resolution Reception Under Time-Varying Channels

On the Doppler Squint Effect in OTFS Systems Over Doubly-Dispersive Channels: Modeling and Evaluation

Extensive work has demonstrated the excellent performance of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios. Time-variant wideband channel estimation serves as one of the key compositions of OTFS receivers since the data detection requires accurate channel state information (CSI). In practical wideband OTFS systems, the Doppler shift brought by the high mobility is frequency-dependent, which is referred to as the Doppler Squint Effect (DSE). Unfortunately, DSE was ignored in overall prior estimation schemes employed in OTFS systems, which leads to severe performance loss in channel estimation and the consequent data detection. In this paper, we investigate DSE of wideband time-variant channel in delay-Doppler domain and concentrate on the characterization of OTFS channel coefficients considering DSE. The formulation and evaluation of OTFS input-output relationship are provided for both ideal and rectangular waveforms considering DSE. The channel estimation is therefore formulated as a sparse signal recovery problem and an orthogonal matching pursuit (OMP)-based scheme is adopted to solve it. Simulation results confirm the significance of DSE and the performance superiority compared with traditional channel estimation approaches ignoring DSE.

A Spatial-Specific Neural Network-Based OFDM Channel Estimation Under Time-Varying Channels

A Spatial-Specific Neural Network-Based OFDM Channel Estimation Under Time-Varying Channels

Dynamic User-Scheduling and Power Allocation for SWIPT Aided Federated Learning: A Deep Learning Approach

Federated learning (FL) has been considered as a promising paradigm for enabling distributed machine learning (ML) in wireless networks. To address the limited energy capacity of wireless devices, we propose a simultaneous wireless information and power transfer (SWIPT) aided FL, in which one FL server (FLS) co-located at a cellular base station (BS) uses SWIPT to simultaneously broadcast the global model to wireless user-devices (UDs) and provide wireless power transfer to them. The UDs then use the harvested energy to train their local models and further transmit the local models to the FLS for aggregation. To improve the spectrum efficiency, we consider that the UDs form a non-orthogonal multiple access (NOMA) group for simultaneously sending their local models over the same spectrum channel. Taking the UDs' time-varying available energy and channel conditions into account, we propose a dynamic optimization of the UDs-scheduling, the BS's transmit-power allocation, and the UDs' power-splitting factors for SWIPT, with the objective of minimizing the long-term energy consumption while ensuring the FL convergence. The optimization problem, however, is challenging to solve since it is a finite-horizon dynamic programming problem but with an unknown stopping time, and moreover, the action space covers both discrete and continuous variables. To address these difficulties, we first execute a series of equivalent transformations to reduce the number of decision variables and then formulate the problem as a stochastic shortest path problem, based on which we propose an actor-critic deep reinforcement learning algorithm with the proximal policy optimization to efficiently learn the policy that dynamically adjusts the UDs-scheduling for FL as well as the BS's transmit-power for SWIPT. Numerical results validate the effectiveness and performance of our proposed algorithm. The results demonstrate that our proposed algorithm can effectively reduce the long-term energy consumption in comparison with two baseline algorithms.

SlimFL: Federated Learning With Superposition Coding Over Slimmable Neural Networks

Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices’ distributed computing capabilities. However, applying FL in practice is challenging due to the local devices’ heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions. To cope with these issues, this paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to time-varying channel conditions and data distributions. In addition, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, which makes SNN ill-suited for FL. Motivated by this, we propose a communication and energy-efficient SNN-based FL (named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SlimFL</i> ) that jointly utilizes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">superposition coding (SC)</i> for global model aggregation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">superposition training (ST)</i> for updating local models. By applying SC, SlimFL exchanges the superposition of multiple-width configurations decoded as many times as possible for a given communication throughput. Leveraging ST, SlimFL aligns the forward propagation of different width configurations while avoiding inter-width interference during backpropagation. We formally prove the convergence of SlimFL. The result reveals that SlimFL is not only communication-efficient but also deals with non-IID data distributions and poor channel conditions, which is also corroborated by data-intensive simulations.