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In the ever-evolving landscape of wireless communication systems, including fifth-generation (5G) networks and beyond (B5G), accurate Modulation and Coding Scheme (MCS) prediction is crucial for optimizing data transmission efficiency and quality of service. Traditional MCS selection methods rely on predefined rules and heuristics, offering transparency and control but lacking adaptability in changing wireless conditions. The emergence of Machine Learning (ML) has brought transformative capabilities, particularly in MCS prediction. ML leverages data-driven models, promising improved accuracy and adaptability in dynamic wireless environments. This paper marks a novel endeavor in this domain, as it explores and evaluates a range of machine learning (ML) techniques for predicting MCS in orthogonal frequency-division multiplexing (OFDM) systems, representing the first such investigation in this field. Additionally, it introduces a specialized Deep Neural Network (DNN) architecture with two hidden layers for MCS prediction, guided by performance metrics such as accuracy, precision, recall, and F1-score. The examined ML methods include Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), and Bagging with k-NN (B-kNN). These methods undergo thorough training and evaluation using a dataset generated from simulations of non-standalone 5G networks. The study incorporates physical layer measurements and employs a ray-tracing path loss prediction model for comprehensive environmental characterization. Also, advanced data mining techniques preprocess raw data, addressing model underfitting and overfitting challenges. Finally, performance evaluation results reveal that the ANN with two hidden layers achieves the highest accuracy at 98.71%, while RF and B-kNN methods attain the lowest accuracy, below 88.65%. SVM and ANN models, with one and four hidden layers, respectively, demonstrate comparable MCS prediction accuracy, ranging from 97.02 to 97.30%. [ABSTRACT FROM AUTHOR]

The growing demand for communication technology capable of providing high transmission rates in maritime environments has led to the exploration of the very high frequency (VHF) data exchange system (VDES) as a promising solution. The VDES, the integration of an automatic identification system (AIS), application-specific messaging (ASM), and VHF data exchange (VDE), offers improved transmission rates and stable connections compared with existing technologies. Although the VDES supports high transmission rates through various modulation and coding scheme (MCS) technologies, it lacks a standardized mechanism for controlling MCS parameters and relies on user algorithms for operation. In this paper, we introduce the maritime auto-rate fall-back (mARF) technology, designed to effectively address the challenges of maritime communication scenarios using the MCS framework provided by the VDES. mARF technology incorporates fast drop-out and recovery mechanisms to swiftly adapt to changing MCS types in the presence of deep nulls, a common occurrence in maritime communication environments. These adaptive thresholds for fast drop-out and recovery operations are dynamically learned using historical communication data. Through extensive simulations, we demonstrate the effectiveness of mARF in enhancing the MCS control capabilities of the VDES. Our results show a significant performance improvement of 18% compared to the existing model, validating the potential of mARF in optimizing maritime communication channels and supporting a high transmission rate. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) were created more than eighteen years ago with the aim of reducing accidents on public roads and saving lives. Achieving this goal depends on VANET mobiles exchanging Road State Information (RSI) with their surroundings and acting on the received RSI. Therefore, it is essential to ensure that transmitted messages are accurately received. This requires controlling the quality of the sharing medium or link while considering Channel State Information (CSI), which provides information on channel quality and Signal-to-Noise Ratio (SNR). The process of adjusting the payload based on the CSI is known as Link Adaptation (LA). While several LA works have been published in VANETs, few have considered the effect of relative node mobility. This work presents a link adaptation strategy for VANETs that uses a Neural Network (NN) and the Levenberg-Marquardt Algorithm (LMA). While accounting for the Doppler Shift effect induced by the relative velocity, the simulation results demonstrate that the NN approach outperforms its peers by 77%, 115% and 853% in terms of transmitted errors, model efficiency, and throughput respectively, compared to the Cte, ARF, and AMC algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, the rising demand for digital communication technologies has contributed to the increase in the volume of traffic. This continuous trend of internet traffic has led to the deterioration of the quality of service (QoS) with reduced throughput and increased latency. This also is due to the proliferation of new broadband applications which require low latency and high throughput such as virtual reality and real-time gaming. Therefore, considering the aforementioned challenge in QoS of wireless networks, a link adaptation method is suggested in this study, in order to enhance the performance of the QoS in IEEE 802.11ac amendment wireless local-area network (WLAN). The proposed technique adaptively changes the transmission data rate by increasing or decreasing the modulation and coding scheme (MCS) level according to the traffic conditions. With the use of an OMNeT++ computer-aided design (CAD)-based simulation model, the effectiveness of the suggested approach is examined. Simulated findings were compared with the link adaptation approach of the default condition. The results of the simulation demonstrate that the proposed technique significantly increases throughput (36.48%) and decreases latency in comparison to the default situation. These findings demonstrate the technique's potential to improve WLAN QoS efficiency, notably in regard to throughput and latency. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) were created more than eighteen years ago with the aim of reducing accidents on public roads and saving lives. Achieving this goal depends on VANET mobiles exchanging Road State Information (RSI) with their surroundings and acting on the received RSI. Therefore, it is essential to ensure that transmitted messages are accurately received. This requires controlling the quality of the sharing medium or link while considering Channel State Information (CSI), which provides information on channel quality and Signal to Noise Ratio (SNR). The process of adjusting the payload based on the CSI is known as Link Adaptation (LA). While several LA works have been published in VANETs, few have considered the effect of relative node mobility. This work presents a link adaptation strategy for VANETs that uses a Neural Network (NN) and the Levenberg-Marquardt Algorithm (LMA). While accounting for Doppler Shift effect induced by the relative velocity, the simulation results demonstrate that the NN approach outperforms its peers by 77%, 115% and 853% in terms of the transmitted errors, model efficiency and throughput respectively, compared to Cte, ARF, and AMC algorithms.

This paper presents a detailed framework for adaptive low-complexity and power-efficient resource allocation in decentralized device-to-device (D2D) networks. The adopted system model considers that active devices can directly communicate via specified signaling channels. Each D2D receiver attempts to allocate its D2D resources by selecting a D2D transmitter and one of its spectral channels that can meet its performance target. The process is performed adaptively over successive packet durations with the objective of limiting the transmit power on D2D links while reducing the processing complexity. The proposed D2D link adaptation scheme is modeled and analyzed under generalized channel conditions. It considers the random impact of potential D2D transmitters as well as the random number of co-channel interference sources on each D2D link. Interference cancelation schemes are also addressed to alleviate co-channel interference, which can ease the D2D resource allocation process. Generalized formulations for the statistics of the resulting signal-to-interference plus noise ratio (SINR) of the proposed adaptation scheme are presented. Moreover, generic analytical results were developed for some important performance measures as well as processing load measures. They facilitate tradeoff studies between the achieved performance and the processing complexity of the proposed scheme. Insightful results for the distributions of SINRs on individual D2D links under specific fading models are shown in this paper. The results herein add enhancements to some previous contributions and can handle various practical constraints. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power throughout the frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. In comparison to recently published methods, our results show that the success rate improves. The suggested method directly senses the physical channel because it computes the SINR and codding rate of received signal just after the signal is detected by successive interference cancellation (SIC). Hence, because of this direct sense, this algorithm can really decrease occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity during the eNode B connections. Simulation results compared to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Network (VANET) was initiated about two decades ago in view of saving lives by mitigating and reducing the number of accidents and incidents on public roads. Moreover, this objective can only be achieved if VANETs mobiles regularly exchange Road State Information (RSI) with their neighborhood and take decisive actions based on the RSI received. Therefore, it becomes paramount to ensure that the transmitted message is well received. And this is only possible if the quality of the sharing medium or link is controlled, and transmission performed while taking into consideration the Channel State Information (CSI). The CSI provides information related to channel quality, Signal-to-Noise Ratio (SNR), and so forth. The process of adapting the payload as a function of the CSI is called Link Adaptation (LA). Several LA works have already been published in VANETs, but almost without serious consideration of the effect of the relative mobility amongst the nodes. Hence, while taking into consideration the Doppler Shift induced by the relative velocity, the current work presents a link adaptation strategy using a Neural Network (NN) and the Levenberg-Marquardt algorithm in VANETs. The simulation results definitively demonstrate that the NN approach outperforms its counterparts by a significant margin. It achieves a performance of 1075% in transmission duration, 180% in transmitted bit, and 115% in model efficiency when compared to the Cte, ARF, and AMC algorithms, respectively.

In Narrowband Internet of Things (NB-IoT), the control of uplink transmissions is a complex task involving device scheduling, resource allocation in the carrier, and the configuration of link-adaptation parameters. Existing heuristic proposals partially address the problem, but reinforcement learning (RL) seems to be the most effective approach a priori, given its success in similar control problems. However, the low sample efficiency of conventional (model-free) RL algorithms is an important limitation for their deployment in real systems. During their initial learning stages, RL agents need to explore the policy space selecting actions that are, in general, highly ineffective. In an NB-IoT access network this implies a disproportionate increase in transmission delays. In this paper, we make two contributions to enable the adoption of RL in NB-IoT: first, we present a multi-agent architecture based on the principle of task division. Second, we propose a new model-based RL algorithm for link adaptation characterized by its high sample efficiency. The combination of these two strategies results in an algorithm that, during the learning phase, is able to maintain the transmission delay in the order of hundreds of milliseconds, whereas model-free RL algorithms cause delays of up to several seconds. This allows our approach to be deployed, without prior training, in an operating NB-IoT network and learn to control it efficiently without degrading its performance.

Link adaptation (LA) matches transmission parameters to conditions on the radio link, and therefore plays a major role in telecommunications. Improving LA is within the requirements for next-generation mobile telecommunication systems, and by refining link adaptation, a higher channel efficiency can be achieved (i.e., an increased data rate thanks to lower required bandwidth). Furthermore, by replacing traditional LA algorithms, radio transmission systems can better adapt themselves to a dynamic environment. There are several drawbacks to current state-of-the-art approaches, including predefined and static decision boundaries or relying on a single, low-dimensional metric. Nowadays, a broadly used approach to handle a variety of related input variables is a neural network (NN). NNs are able to make use of multiple inputs, and when combined with reinforcement learning (RL), the so-called deep reinforcement learning (DRL) approach emerges. Using DRL, more complex parameter relationships can be considered in order to recommend the modulation and coding scheme (MCS) used in LA. Hence, this work examines the potential of DRL and includes experiments on different channels. The main contribution of this work lies in using DRL algorithms for LA, optimized for throughput based on a subcarrier observation matrix and a packet success rate feedback system. We apply Natural Actor-Critic (NAC) and Proximal Policy Optimization (PPO) algorithms on simulated channels with a subsequent feasibility study on a prerecorded real-world channel. Empirical results produced by experiments on the examined channels hint that Deep Reinforcement Learning for Link Adaptation (DRLLA) offers good performance indicated by a promising data rate on the additive white Gaussian noise (AWGN) channel, the non-line-of-sight (NLOS) channel, and a prerecorded real-world channel. No matter the channel impairment, the agent is able to respond to changing signal-to-interference-plus-noise-ratio (SINR) levels, as exhibited by expected changes in the effective data rate.

Abstract One of the current (6G) wireless networks research’s trends is to investigate short distance and dense scenarios, where users are locally connected in sub-networks. Such use case is critical to support the advances of industrial internet of things or Industry 4.0 (I4.0), e.g. connecting an entire group of sensors and actuators of a robot. Therefore, schemes that can properly manage the interference must be deployed in practical systems to allow the promised performance advances of 6G. Targeting these high density scenarios, we describe the Power Optimization for Low Interference and Throughput Enhancement (POLITE) paradigm for link adaptation and power allocation, which leverages available radio resources to stabilize and reduce the interference. The baseline link adaptation schemes are compared with POLITE in their performance in a 3rd generation partnership project (3GPP) calibrated system level simulator for industrial scenarios. As services in industrial environments require high reliability under constrained delays, we propose different delay-aware formulations in the POLITE design. In this work, we provide solutions both for relaxed delay requirements and for latency critical traffic, whose delay must be minimized. In particular, in the latter case, we propose also modifications of user selection and resource allocation procedures to further improve the reliability and latency. Simulation results prove the benefits of POLITE in terms of increased throughput, fulfillment of relaxed and delay-critical requirements, with an overall reduced transmit power compared to the current baseline link adaptation schemes.

This work provides a general review of Adaptive Modulation and Coding (AMC) techniques that optimize the utilization of available resources and parameters in wireless communication systems. AMC or link adaptation empowers communication systems to employ appropriate modulation and coding schemes in order to enhance link spectrum utilization over time-varying channels. In addition, the proper selection of Modulation and Coding Schemes (MCS) diminishes the Bit Error Rate (BER) of systems and reduces the required transmit power through which the interference to other users is minimized. This paper conducts a state-of-the-art review of the existing AMC technologies. The reviews provided in this paper shed light on the trends of research in AMC and bring focus to related techniques. First, the existing constraints and the related phenomena in advanced wireless communication, including channel capacity, path loss, fading, and interference, are thoroughly investigated to understand further and improve the AMC systems. Moreover, several scenarios of utilizing the AMC methods are provided in order to demonstrate the extent of the effectiveness and importance of accurate AMC. Lastly, the challenges, the likely problems, the research gaps, and the potential avenues for future work are declared. [ABSTRACT FROM AUTHOR]

Link adaptation (LA) matches transmission parameters to conditions on the radio link, and therefore plays a major role in telecommunications. Improving LA is within the requirements for next-generation mobile telecommunication systems, and by refining link adaptation, a higher channel efficiency can be achieved (i.e., an increased data rate thanks to lower required bandwidth). Furthermore, by replacing traditional LA algorithms, radio transmission systems can better adapt themselves to a dynamic environment. There are several drawbacks to current state-of-the-art approaches, including predefined and static decision boundaries or relying on a single, low-dimensional metric. Nowadays, a broadly used approach to handle a variety of related input variables is a neural network (NN). NNs are able to make use of multiple inputs, and when combined with reinforcement learning (RL), the so-called deep reinforcement learning (DRL) approach emerges. Using DRL, more complex parameter relationships can be considered in order to recommend the modulation and coding scheme (MCS) used in LA. Hence, this work examines the potential of DRL and includes experiments on different channels. The main contribution of this work lies in using DRL algorithms for LA, optimized for throughput based on a subcarrier observation matrix and a packet success rate feedback system. We apply Natural Actor-Critic (NAC) and Proximal Policy Optimization (PPO) algorithms on simulated channels with a subsequent feasibility study on a prerecorded real-world channel. Empirical results produced by experiments on the examined channels hint that Deep Reinforcement Learning for Link Adaptation (DRLLA) offers good performance indicated by a promising data rate on the additive white Gaussian noise (AWGN) channel, the non-line-of-sight (NLOS) channel, and a prerecorded real-world channel. No matter the channel impairment, the agent is able to respond to changing signal-to-interference-plus-noise-ratio (SINR) levels, as exhibited by expected changes in the effective data rate. [ABSTRACT FROM AUTHOR]

Adaptive Modulation and Coding (AMC) is a promising technique to increase the average spectral efficiency of communication links. This research proposes a novel AMC method based on a supervised deep learning approach to maximize the average spectral efficiency of OFDM wireless systems while the bit error rate (BER) remains under a predefined threshold. The proposed method consists of a one-dimensional convolutional network that performs feature extraction and a long short-term memory network that learns the behavior of the channel. Input features are the magnitudes and phases of the estimated channel frequency response in the pilot subcarriers and signal-to-noise ratio. Datasets of various fading channel responses were generated using WINNER II. The proposed method was compared with previous methods based on different criteria, including average spectral efficiency, BER, the accuracy of predictions, the average delay of each prediction, and model complexity. The simulation results confirmed the superiority of the proposed AMC method. [ABSTRACT FROM AUTHOR]

Abstract Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK $$\rightarrow$$ → 16-QAM) and from 0 to 43.3% for (16-QAM $$\rightarrow$$ → 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5–88.6% and by 0–43.2% for the (QPSK $$\rightarrow$$ → 16-QAM)and (16-QAM $$\rightarrow$$ → 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system.

One of the current (6G) wireless networks research's trends is to investigate short distance and dense scenarios, where users are locally connected in sub-networks. Such use case is critical to support the advances of industrial internet of things or Industry 4.0 (I4.0), e.g. connecting an entire group of sensors and actuators of a robot. Therefore, schemes that can properly manage the interference must be deployed in practical systems to allow the promised performance advances of 6G. Targeting these high density scenarios, we describe the Power Optimization for Low Interference and Throughput Enhancement (POLITE) paradigm for link adaptation and power allocation, which leverages available radio resources to stabilize and reduce the interference. The baseline link adaptation schemes are compared with POLITE in their performance in a 3rd generation partnership project (3GPP) calibrated system level simulator for industrial scenarios. As services in industrial environments require high reliability under constrained delays, we propose different delay-aware formulations in the POLITE design. In this work, we provide solutions both for relaxed delay requirements and for latency critical traffic, whose delay must be minimized. In particular, in the latter case, we propose also modifications of user selection and resource allocation procedures to further improve the reliability and latency. Simulation results prove the benefits of POLITE in terms of increased throughput, fulfillment of relaxed and delay-critical requirements, with an overall reduced transmit power compared to the current baseline link adaptation schemes. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study of uplink scheduling in narrowband internet of things (NB-IoT) networks. As NB-IoT devices need a long battery lifetime, we aim to maximize energy efficiency while satisfying the main requirements for NB-IoT devices. Also, as the NB-IoT scheduling problem is divided into link adaptation problem and resource allocation problem, this paper investigates the correlation between these two problems. Accordingly, we propose two scheduling schemes: the joint scheduling scheme, where the two problems are combined as one optimization problem, and the successive scheduling scheme that manages each problem separately but successively. Each scheme aims to maximize energy efficiency while achieving reliable transmission, satisfying delay requirements, and guaranteeing resource allocation specifications. Also, we investigate the impact of the selected devices to be served on the total energy efficiency. Accordingly, we propose two device selection techniques to maximize the total energy efficiency. The first technique exhaustively searches for the optimal devices, while the second sorts the devices based on a proposed priority score. The simulation results compare the successive and the joint scheduling schemes. The results show that the joint scheme outperforms the successive scheme in terms of energy efficiency and the number of served devices but with higher complexity. Also, the results highlight the impact of each proposed selection technique on the scheduling schemes' performance. [ABSTRACT FROM AUTHOR]

This paper presents a detailed framework for adaptive low-complexity and power-efficient resource allocation in decentralized device-to-device (D2D) networks. The adopted system model considers that active devices can directly communicate via specified signaling channels. Each D2D receiver attempts to allocate its D2D resources by selecting a D2D transmitter and one of its spectral channels that can meet its performance target. The process is performed adaptively over successive packet durations with the objective of limiting the transmit power on D2D links while reducing the processing complexity. The proposed D2D link adaptation scheme is modeled and analyzed under generalized channel conditions. It considers the random impact of potential D2D transmitters as well as the random number of co-channel interference sources on each D2D link. Interference cancelation schemes are also addressed to alleviate co-channel interference, which can ease the D2D resource allocation process. Generalized formulations for the statistics of the resulting signal-to-interference plus noise ratio (SINR) of the proposed adaptation scheme are presented. Moreover, generic analytical results were developed for some important performance measures as well as processing load measures. They facilitate tradeoff studies between the achieved performance and the processing complexity of the proposed scheme. Insightful results for the distributions of SINRs on individual D2D links under specific fading models are shown in this paper. The results herein add enhancements to some previous contributions and can handle various practical constraints.

The fifth-generation (5G) and beyond standards are being challenged by the diverse requirements of modern use cases. Multicarrier modulation techniques are one of the key components of the physical layer (PHY) design, which has immense potential to improve efficiency and reliability. In current state-of-the-art wireless technologies (i.e., NR and IEEE 802.11ax) orthogonal frequency division multiplexing (OFDM) is used which has many disadvantages such as peak-to-average power ratio (PAPR), out-of-band emission (OOBE), and sensitivity to carrier frequency offset (CFO). To overcome these drawbacks several alternate multicarrier modulation techniques are being considered, such as discrete Fourier transform-spread-OFDM (DFT-s-OFDM), generalized frequency division multiplexing (GFDM), and orthogonal time-frequency space (OTFS). In this paper, we develop the physical layer abstraction (PLA) of these candidate multicarrier techniques to evaluate their performance under various use cases and scenarios. The PLA is a commonly used technique to avoid time-consuming PHY simulations in system-level simulators. To improve the accuracy of PLA in different fading conditions, we derive a fitting parameter as a function of the received signal-to-interference-plus-noise ratio (SINR) variance. The validation results show that performance can be accurately estimated through the proposed multicarrier PLA. Moreover, PLA techniques are at least thousands of times faster compared to PHY simulations. [ABSTRACT FROM AUTHOR]

Abstract In order to guarantee a robust transmission of JPWL (JPEG Wireless: Joint Photographic Experts Group Wireless) images through time and frequency selective wireless channels, an efficient adaptive communication strategy is proposed. It is based on an optimization of a closed-loop adaptive multiple-input multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) scheme associated with a shaping BICM (bit-interleaved coded modulation) technique composed of a duo binary turbo code (DBTC), high-order modulations such as 64–256 QAM (Quadrature Amplitude Modulation) and a shaping code. According to the CSI (channel state information) knowledge at the transmitter side, an algorithm based on unequal error protection (UEP) and unequal power allocation (UPA) is used to select the transmitter key parameters (source/channel encoder rate, modulation order, power, number of quality layers and number of iterations of the Turbo decoder) to achieve the target Quality of Service (QoS). The proposed DBTC-shaping BICM scheme reaches a shaping gain of 1.2 dB for a 256 QAM modulation over a SISO Gaussian channel, whereas only 0.7 dB of shaping gain can be achieved in a scheme that uses the LDPC shaping BICM scheme for the same modulation order. Based on a DBTC shaping BICM scheme and an adaptive algorithm, the proposed MIMO-OFDM strategy achieves better performance compared to a strategy using an iterative process between an RS (Reed-Solomon) and arithmetic decoders. As a result, and on the one hand, a gain of 5.38 dB can be achieved in terms of PSNR (peak signal-to-noise ratio). On the other hand, a gain of 78% in terms of power consumption is obtained for the same QoS level. Moreover, the adaptive number of iterations in the proposed strategy can minimize the computational complexity of the turbo decoding compared to a scheme using four iterations whatever the channel conditions.

Unmanned aerial vehicles (UAVs) control information delivery is a critical communication with stringent requirements in terms of reliability and latency. In this context, link adaptation plays an essential role in the fulfillment of the required performance in terms of decode error probability and delay. Link adaptation is usually based on channel quality indicator (CQI) feedback information from the user equipment that should represent the current state of the channel. However, measurement, scheduling and processing delays introduce a CQI aging effect, that is a mismatch between the current channel state and its CQI representation. Using outdated CQI values may lead to the selection of a wrong modulation and coding scheme, with a detrimental effect on performance. This is particularly relevant in ultra reliable and low latency communications (URLLC), where the control of the reliability can be negatively impacted, and it is more evident when the channel is fast varying as the case of UAVs. This paper analyzes the effects of CQI aging on URLLCs, considering transmissions under the finite blocklength regime, that characterizes such communications type. A deep learning approach is investigated to predict the next CQI from the knowledge of past reports, and performance in terms of decode error probability and throughput is given. The results show the benefit of CQI proposed prediction mechanism also in comparison with previously proposed methods. [ABSTRACT FROM AUTHOR]

The letter describes an online deep learning algorithm (ODL) for adaptive modulation and coding in massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA, the algorithm shows a 10% to 20% improvement in user throughput in the full-buffer case. [ABSTRACT FROM AUTHOR]

Link adaptation (LA) is an adaptive transmission technique that determines the modulation and coding scheme (MCS) based on channel-state information. In LA, an accurate estimation of the link performance is required to optimally determine the MCS level. In this correspondence, a high-accuracy machine-learning (ML)-aided link-level performance-prediction method for coded multiple-input–multiple-output (MIMO) systems is proposed. The basic concept of this scheme is to apply the ML model to train the relation between the inputs, such as the channel matrix and signal-to-noise ratio, and the output of the block-error rate (BLER). Specifically, we predict the index of the quantized BLER value using a random forest classifier. The simulation results show that the proposed scheme is able to accurately predict the link performance of MIMO systems and outperforms the conventional link performance-prediction schemes. [ABSTRACT FROM AUTHOR]

The multi-connectivity is a key enabler for ultra-reliable low-latency communications. To evaluate its performance and suitability to various use cases system-level studies are essential, where the physical layer (PHY) plays an important role. Therefore, PHY modeling is required which is time-intensive and requires highly complex computations. For this purpose, the PHY performance is usually abstracted in terms of signal-to-interference-plus-noise ratio (SINR), also known as physical layer abstraction (PLA). However, due to fading, the symbols inside a packet could have different SINRs which require effective SINR mapping to compute an equivalent SINR. In the context of multi-connectivity, the received SINR depends on all connected links where each link experiences independent fading and on the used link combining technique. As a result, the computation of effective SINR also depends on the combining technique and fading experienced by individual links. To model PHY performance by considering all these effects, we develop PLA for multi-connectivity communications. This includes the computation of received symbols SINR for various link combining techniques and mapping them to effective SINR using enhanced exponential effective SINR mapping (eEESM). Furthermore, a new optimization method is introduced for eEESM to reduce its optimization complexity. Simulation results show that the proposed PLA accurately estimate the performance of different order of multi-connectivity communications under various fading conditions. [ABSTRACT FROM AUTHOR]

Given the ever-increasing interest in the Internet of Underwater Things (IoUT), various studies are ongoing to solve some of the practical problems affecting the development of underwater wireless communication. The main problems are related to the use of acoustic waves in a water medium, in which extremely high propagation loss and drastic channel fluctuation are common. On the basis of hands-on experience and measurements made in real underwater environments, the conventional Adaptive Modulation and Coding (AMC), which uses the high correlation between SNR (Signal to Noise Ratio) and BER (Bit Error Rate), might not be affordable in underwater environments because the normal correlation between SNR and BER almost disappears altogether. This work therefore collectively takes into account multiple quality factors of communication at the same time by creating, analysing and validating the machine learning model to predict the most adequate communication parameters to solve the problem. The dataset of underwater wireless communication used in the learning models was obtained from measurements made in a real underwater environment near the Gulf of Incheon, South Korea, using a practical testbed designed and implemented by the authors. The estimated network throughput based on the communications parameters predicted using the machine learning models was enhanced by up to 25% compared with the conventional handcraft method.

We propose an artificial intelligence-based channel prediction scheme that can potentially facilitate link adaptation in customized communication systems. Link adaptation is a key process for wireless communication that requires accurate channel state information (CSI). However, the CSI may be outdated because of computational and propagation delays. In addition, a subframe with no CSI reference signal cannot provide CSI feedback. The proposed scheme solves these problems by predicting future channels. Although traditional stochastic methods suffer from marginal prediction accuracy or unacceptable computational complexity, neural networks allow time series prediction for channels even considering constraints for practical application. We introduce a hybrid architecture for improving the prediction accuracy of the neural network when extracting meaningful features. The proposed scheme uses a single hybrid network that can predict channels in different environments. Simulations were performed using a spatial channel model to evaluate the performance at the system-level, and the results indicated that the proposed scheme effectively increases the prediction accuracy for the channel quality indicator and spectral efficiency.

With the speeding up of the fifth generation (5G) new radio (NR) worldwide commercialization, one of the paramount questions for operators and vendors is how to optimize the radio links, considering the widely diverse scenarios envisioned. One of the key pillars of 5G has been an unprecedented flexibility on the configuration of the radio access network (RAN) on scenarios that include cellular, vehicular, and industrial networks among others. This flexibility has its main exponent on link adaptation (LA), which has evolved into a multi-domain technique where a plethora of parameters, like numerology, bandwidth part, radio frequency beam, power, modulation and coding scheme (MCS) or multiple antenna precoding can be adapted to the instantaneous link conditions. Although such enhancements open the door to a significant performance improvement, they also pose many challenges to LA optimization. In this article, we first present the signaling aspects of NR technology for multi-domain LA and the challenges that need to be faced. Then, we explore the latest advances on LA for wireless networks. We envision a combination of machine learning (ML) tools with multi-domain LA as a key enabler for 5G and beyond networks. Finally, we investigate emerging ML approaches for LA and present a promising application of ML for LA that is assessed with simulations. With this scheme, the training is performed at the network side to relieve the user equipment (UE) to do such a complex task. It is shown with simulations that our ML approach outperforms the well-known outer loop link adaptation (OLLA) algorithm in terms of instantaneous block error rate (BLER), while reaching the same average spectral efficiency (SE). Interestingly, it is shown that the proposed scheme only requires 4 bits to represent the features used to train the model, which makes it suitable for implementation in real systems with limited feedback.

Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK → 16-QAM) and from 0 to 43.3% for (16-QAM → 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5–88.6% and by 0–43.2% for the (QPSK → 16-QAM)and (16-QAM → 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system. [ABSTRACT FROM AUTHOR]

Summary: The application of linear precoding at the gateway side enables broadband multibeam satellite systems to use more aggressive frequency reuse patterns increasing the overall capacity of future high‐throughput satellites (HTS). In any practical precoded system, receivers can estimate only a few coefficients of the channel state information (CSI), while the others, in what is known as nullification, are set to zero. In this paper, the impact of the CSI nullification to the SINR estimation is analyzed statistically and geographically for a multicast multibeam satellite system. The errors in the SINR calculated by the gateway affect to the modulation and coding schemes (MCSs) selection, increasing the rate of erroneous frames or leading to an underutilization of the available resources. Therefore, as countermeasure, a link adaptation algorithm based on an adaptive margin per user is proposed, helping to achieve the error rate constraints of DVB‐S2X systems without compromising the throughput, even under severe CSI degradation due to nullification and Rician fading. [ABSTRACT FROM AUTHOR]

Ultra-Reliable and Low Latency Communications (URLLC) is an important use case in 5G NR that targets at 1-ms level delay sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by enhanced Mobile BroadBand (eMBB) service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through modulation and coding scheme (MCS) selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE – the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implemented DELUXE through a link-level 5G NR simulator with GPU and MathWorks 5G toolbox. Through extensive experiments, we show that DELUXE can successfully choose MCS for eMBB transmissions to maintain the desired link reliability while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement ($< 125 \mu \text{s}$) in 5G NR. [ABSTRACT FROM AUTHOR]

The fast evolution in wireless communication standards and enhancement in cellular applications has created an exponential rise in the data rate requirement over the past few decades. The next generation wireless standards, therefore, need not only to provide ultra high data rates with minimum latency, but also to support diverse quality-of-service (QoS) requirements. Filterbank multicarrier (FBMC) scheme provides quite a few advantages over the conventional orthogonal frequency division multiplexing (OFDM) for future wireless networks. In order to achieve adaptive throughput and diverse service quality requirements under varying channel conditions, a link adaptation algorithm for FBMC scheme is proposed in this paper. The aim is to provide the dynamic selection of optimum parameters resulting in diverse modes of operation to ensure the desired and/or best available communication service quality. The proposed link adaptation algorithm incorporates the parameters of required data rate, available channel condition and QoS demand, and dynamically allocates the appropriate FBMC transmission parameters. The proposed scheme has been evaluated for AWGN as well as multipath fading SUI channel models. Simulation results indicate that the proposed algorithm successfully achieves the desired service quality subject to the availability of suitable channel conditions and is also superior to the existing algorithms in terms of block error rate and effective throughput. [ABSTRACT FROM AUTHOR]

Advanced driver-assistance system features in current vehicles (as precursor to fully autonomous, connected vehicular systems) will rely on 5G network links. Specifically, enabling various real-time operational aspects (e.g. emergency messaging), performance of ultra-reliable low-latency communications (URLLC) specified by 3GPP Release 16 is of direct relevance. A key challenge in achieving URLLC goals is the natural tension between simultaneously achieving high reliability (low block error rate or BLER) and low latency. When operating in a dynamic (i.e. significant time-varying channels due to vehicular mobility) environment, link adaptation is fundamental to URLLC implementation. In this paper, we introduce a new Markov Chain model for link adaptation that predicts the three key performance indicators (KPIs): end-to-end link latency, throughput and BLER as a function of the modulation and coding scheme used. The predictions from the Markov Chain model are supported by Monte Carlo simulations for various mobility scenarios, to explore how 5G network parameters may be adapted to achieve desired benchmarks, e.g. maximizing link throughput while providing strict latency and BLER bounds. [ABSTRACT FROM AUTHOR]

Interest in the study of next-generation underwater sensor networks for ocean investigations has increased owing to developing concerns over their utilization in areas such as oceanography, commercial operations in maritime areas, and military surveillance. Underwater acoustic communications (UAC) network channels are fast-varying (spatially and temporally) according to environmental conditions. It is tempting to use adaptive modulation and coding (AMC) for UAC networks to improve the system efficiency by matching transmission parameters to channel variations. This paper focuses on analyzing a measured sea trial dataset by using a rule-based strategy (i.e., three-dimensional analysis, modulation-wise analysis, and a fixed-SNR strategy) to find the suitable link adaptation procedure depending on the channel quality. Hence, we plot a scenario of the measured UAC network data rate vs. Signal to Noise Ratio (SNR) and/or Bit Error Rate (BER) to pick the best AMC combinations in the context of adaptivity to the channel. Due to non-reversibility limitation of rule-based strategy, the work further extends to use machine learning (ML) algorithms to classify the MCS levels by investigating the channel characteristics. Boosted regression tree, from among the four ML algorithms we adopted for the analysis, shows formidable accuracy of 99.97% in classifying MCS levels. This ensemble of trees learns from the uplink data of the buoy and the base station and relates the MCS levels to channel metrics and signal characteristics especially subject to SNR and BER constraints.

Limited energy resources of sensors and stringent quality-of-service (QoS) constraints in biomedical applications raise serious concerns when utilized in Wireless Body Area Networks (WBANs). The European Telecommunications Standards Institute (ETSI) Smart Body Area Network (SmartBAN) represents a standardized communication interface and protocol between a hub coordinator and a set of sensors, that has been designed with simplicity and low power in mind. This work presents an ETSI SmartBAN PHY and MAC configuration framework that remarkably lengthens sensors battery lifespan through reducing transceivers consumed energy. To that end, and taking into account the channel quality and packet error rate requisites of sensors, a mechanism to select between the different PHY transmission modes of the standard is proposed. This link adaptation scheme is combined with a resource allocation algorithm that derives the duration of the inter-beacon intervals and the transmission periods of sensors, while fulfilling traffic delay constraints and minimizing sensors transceivers energy consumption. Analytical expressions for packet error rate of all available PHY transmission modes, as well as for traffic delay, transceivers energy savings of hub and sensor nodes, and battery duration, are derived. Computer simulation results substantiate the efficacy of both, the presented QoS-aware adaptive transmission scheme and the resource allocation algorithm, in fulfilling the target packet error rate and delay requirements, while significantly expanding the battery duration, specially for sensors with long elapsed times between successive sensing intervals (up to a 515% increase and a 960% increase are obtained for the two considered simulation scenarios).

This research classifies the modulation and coding rate for link adaptation in Underwater Acoustic Communications Networks (UACNs). Recently, the UACN has become a promising technology for military, commercial, and civilian applications, as well as scientific research. However, we should minimize the dataset dimension for real-time implementation due to the sensor nodes’ energy limitations in the underwater environment. We used an Incheon sea trial’s measured dataset of 18 features, applying Principal Component Analysis (PCA) to select the dominant eigenvalue components in order to reduce the curse of dimensionality, and then selected 11 parameters. After that, we applied Machine Learning (ML) algorithms with different combinations of the parameters to separately classify the modulation and the coding rate and measured both individual and overall classification accuracy. The findings are compared with two Taean sea trial datasets with 11 features to finalize the selected parameters for link adaptation. For modulation classification, we observed 96.83% accuracy with the K-nearest Neighbors (KNN) algorithm in three-parameter and two-parameter cases. In coding rate classification, we found 100% accuracy with the KNN algorithm using the same three-parameter case. However, for the best fit among the three datasets, we finalized another three parameters at the expense of accuracy. To find the optimum threshold values for all modulation and coding rate labels, we used Rule-based (RB) 2D and 3D analysis. However, with a hard limit on non-overlapping data, at best, 35.51% classification accuracy was found for a 1/3 coding rate (Turbo code) with QPSK modulation, which showed much less reliability for RB analysis in a UACN, so it is not useful in this regard. Besides, our analysis shows data independence in the Doppler Spread (DS) and the Frequency Shift (FS), mitigating the time-variability channel’s challenge. We use the Gaussian distribution plot, a confusion matrix, multi-dimensional scatter plots, interpolated plots to analyze the data.

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges. One of these is how to optimize the channelization, resource allocation, beamforming design, and MCS selection jointly for IEEE 802.11ax-based WLANs. In this paper, this technical requirement is formulated as a mixed-integer non-linear programming problem maximizing the total system throughput for the WLANs consisting of unicast users with multicast groups. A novel two-stage solution approach is proposed to solve this challenging problem. The first stage aims to determine the precoding vectors under unit-power constraints. These temporary precoders help re-form the main problem into a joint power and radio resource allocation one. Then, two low-complexity algorithms are proposed to cope with the new problem in stage two. The first is developed based on the well-known compressed sensing method while the second seeks to optimize each of the optimizing variables alternatively until reaching converged outcomes. The outcomes corresponding to the two stages are then integrated to achieve the complete solution. Numerical results are provided to confirm the superior performance of the proposed algorithms over benchmarks. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study of uplink scheduling in narrowband internet of things (NB-IoT) networks. As NB-IoT devices need a long battery lifetime, we aim to maximize energy efficiency while satisfying the main requirements for NB-IoT devices. Also, as the NB-IoT scheduling problem is divided into link adaptation problem and resource allocation problem, this paper investigates the correlation between these two problems. Accordingly, we propose two scheduling schemes: the joint scheduling scheme, where the two problems are combined as one optimization problem, and the successive scheduling scheme that manages each problem separately but successively. Each scheme aims to maximize energy efficiency while achieving reliable transmission, satisfying delay requirements, and guaranteeing resource allocation specifications. Also, we investigate the impact of the selected devices to be served on the total energy efficiency. Accordingly, we propose two device selection techniques to maximize the total energy efficiency. The first technique exhaustively searches for the optimal devices, while the second sorts the devices based on a proposed priority score. The simulation results compare the successive and the joint scheduling schemes. The results show that the joint scheme outperforms the successive scheme in terms of energy efficiency and the number of served devices but with higher complexity. Also, the results highlight the impact of each proposed selection technique on the scheduling schemes’ performance.

To improve the robustness and reliability of wireless transmissions, two complementary link adaptation techniques are employed: adaptive modulation and coding (AMC) at the physical layer and hybrid automatic retransmission request (HARQ) at the medium access control layer. Because of their effectiveness in combating errors induced by the wireless channel, AMC and HARQ are now integral components of most emerging broadband wireless system standards, for example, LTE and WiMAX. Spectral efficiency (SE) as measured in bit per second per Hertz is one important parameter used to characterize a wireless system for comparison between different systems or between different configurations of the same system. This work provides a holistic approach of cross-layer optimizations with the intent of maximizing SE by combining AMC and HARQ. It formulates closed-form equations for calculating the average SE for wireless systems with the Rayleigh fading channel model. A new online algorithm is developed to optimize SE for both Rayleigh and non-Rayleigh fading channel. Simulations using proven LTE model are performed to compare SE obtained from closed-form equations and the developed algorithm for different system configurations. With the developed algorithm to determine how many retransmissions required in addition to the initial transmission in advance depending on the current wireless channel condition, the latency can be reduced up to 24 ms when sending the initial transmission and all of its retransmissions sooner than waiting for retransmission requests as is done previously.

Interest in the study of next-generation underwater sensor networks for ocean investigation has increased owing to developing concerns over its utilization in areas, such as oceanography, commercial operations in maritime areas, and military surveillance. The underwater base station controller, in the form of a surface buoy, communicates with underwater base stations (UBSs) while the UBSs transceiver information with underwater sensor nodes via acoustic communications. This paper provides a link-level and system-level study of downlinks using an orthogonal frequency-division multiple access techniques for underwater acoustic communications (UAC) networks. We present an approach to link-level-to-system-level (L2S) mapping at the link level that provides an abstraction model of the link-level performance to be accessed by system-level simulation (SLS). In this paper, an exponential effective signal-to-noise ratio (SNR) mapping (EESM) method is adopted, which elaborates on how multi-state channels are integrated into a single state in an SLS, and why an effective SNR can represent the characteristics of a multiple subcarrier SNR. Moreover, we explain the beta calibration procedure in detail for a UAC network. The simulation results are provided to verify the beta calibration of the UAC network. Furthermore, we employ a link adaptation strategy by evaluating system throughput based on a proportional fair (PF) scheduler at the system level. Hence, the simulation results confirm the effectiveness of link adaptation strategy for UAC networks.

In order to guarantee a robust transmission of JPWL (JPEG Wireless: Joint Photographic Experts Group Wireless) images through time and frequency selective wireless channels, an efficient adaptive communication strategy is proposed. It is based on an optimization of a closed-loop adaptive multiple-input multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) scheme associated with a shaping BICM (bit-interleaved coded modulation) technique composed of a duo binary turbo code (DBTC), high-order modulations such as 64–256 QAM (Quadrature Amplitude Modulation) and a shaping code. According to the CSI (channel state information) knowledge at the transmitter side, an algorithm based on unequal error protection (UEP) and unequal power allocation (UPA) is used to select the transmitter key parameters (source/channel encoder rate, modulation order, power, number of quality layers and number of iterations of the Turbo decoder) to achieve the target Quality of Service (QoS). The proposed DBTC-shaping BICM scheme reaches a shaping gain of 1.2 dB for a 256 QAM modulation over a SISO Gaussian channel, whereas only 0.7 dB of shaping gain can be achieved in a scheme that uses the LDPC shaping BICM scheme for the same modulation order. Based on a DBTC shaping BICM scheme and an adaptive algorithm, the proposed MIMO-OFDM strategy achieves better performance compared to a strategy using an iterative process between an RS (Reed-Solomon) and arithmetic decoders. As a result, and on the one hand, a gain of 5.38 dB can be achieved in terms of PSNR (peak signal-to-noise ratio). On the other hand, a gain of 78% in terms of power consumption is obtained for the same QoS level. Moreover, the adaptive number of iterations in the proposed strategy can minimize the computational complexity of the turbo decoding compared to a scheme using four iterations whatever the channel conditions. [ABSTRACT FROM AUTHOR]

IEEE 802.11ac high throughput extension for wireless local area network comes with a large number of link layer configuration parameters, such as 4 different channel bonding levels, 10 different modulation and coding schemes, frame aggregation setup etc. However, the optimal combination of link configuration parameters, which maximizes the link layer performance, depends on the perceived channel quality based on the signal strength, channel noise and external interference. Considering the highly dynamic, nonlinear and time-varying nature of wireless channel quality, a dynamic adaptation of link configuration parameters gives a stable and optimized link layer performance. Nevertheless, the existing literature fails to design a robust mechanism for handling all the parameters simultaneously. In this article, we develop a control theoretic approach governed by a deep probabilistic machinery to design a robust and scalable dynamic link parameter adaptation mechanism. We apply deep neural network based Gaussian process regression to predict the link layer throughput and model predictive control based approach to find out the link configuration parameter that optimizes the overall link layer performance. The proposed mechanism is implemented and tested over a testbed setup, and we observe that it can significantly boost up the link layer performance compared to various baseline mechanisms. [ABSTRACT FROM AUTHOR]

This paper presents solutions for efficient multiplexing of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic on a shared channel. This scenario presents multiple challenges in terms of radio resource scheduling, link adaptation, and inter-cell interference, which are identified and addressed throughout this paper. We propose a joint link adaptation and resource allocation policy that dynamically adjusts the block error probability of URLLC small payload transmissions in accordance with the instantaneous experienced load per cell. Extensive system-level simulations of the downlink performance show promising gains of this technique, reducing the URLLC latency from 1.3 to 1 ms at the 99.999% percentile, with less than 10% degradation of the eMBB throughput performance as compared with conventional scheduling policies. Moreover, an exhaustive sensitivity analysis is conducted to determine the URLLC and eMBB performance under different offered loads, URLLC payload sizes, and link adaptation and scheduling strategies. The presented results give valuable insights on the maximum URLLC offered traffic load that can be tolerated while still satisfying the URLLC requirements, as well as what conditions are more appropriate for dynamic multiplexing of URLLC and eMBB traffic in the upcoming 5G systems.

Abstract This work proposes a novel cross-layer link performance prediction (LPP) model and link adaptation (LA) strategy for soft-decoded multiple-input multiple-output (MIMO) bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) systems employing hybrid automatic repeat request (HARQ) protocols. The derived LPP, exploiting the concept of effective signal-to-noise ratio mapping (ESM) to model system performance over frequency-selective channels, does not only account for the actual channel state information at the transmitter and the adoption of practical modulation and coding schemes (MCSs), but also for the effect of the HARQ mechanism with bit-level combining at the receiver. Such method, named aggregated ESM, or αESM for short, exhibits an accurate performance prediction combined with a closed-form solution, enabling a flexible LA strategy, that selects at every protocol round the MCS maximizing the expected goodput (EGP), i.e., the number of correctly received bits per unit of time. The analytical expression of the EGP is derived capitalizing on the αESM and resorting to the renewal theory. Simulation results carried out in realistic wireless scenarios corroborate our theoretical claims and show the performance gain obtained by the proposed αESM-based LA strategy when compared with the best LA algorithms proposed so far for the same kind of systems.