Modulation And Coding Scheme Selection Research Articles

Joint power allocation and MCS selection for energy-efficient link adaptation: A deep reinforcement learning approach

Link adaptation is a promising tool of modern networks to combat the time-variant quality of channels. Modulation and Coding Scheme (MCS) selection is essentially used for link adaptation with channel dynamism. However, future generation networks need flexible link adaptation schemes that consider more parameters to improve the network performance. This paper proposes an energy-efficient link adaptation algorithm, in which a Deep Reinforcement Learning (DRL) agent is used to find the best match between the channel condition and the link parameters. Also, the downlink transmission power has been considered as a link parameter in addition to the modulation order and coding rate to make the link adaptation more flexible and efficient. Simulation results show that the proposed algorithm outperforms the benchmark algorithms regarding energy efficiency and link throughput.

A Hybrid Scheme of MCS Selection and Spectrum Allocation for URLLC Traffic under Delay and Reliability Constraints.

The Ultra-Reliable Low-Latency Communication (URLLC) is expected to be an important feature of 5G and beyond networks. Supporting URLLC in a resource-efficient manner demands optimal Modulation and Coding Scheme (MCS) selection and spectrum allocation. This paper presents a study on MCS selection and spectrum allocation to support URLLC. The essential idea is to establish an analytical connection between the delay and reliability requirements of URLLC data transmission and the underlying MCS selection and spectrum allocation. In particular, the connection factors in fundamental aspects of wireless data communication include channel quality, coding and modulation, spectrum allocation and data traffic characteristics. With this connection, MCS selection and spectrum allocation can be efficiently performed based on the delay and reliability requirements of URLLC. Theoretical results in the scenario of a 5G New Radio system are presented, where the Signal-to-Noise Ratio (SNR) thresholds for adaptive MCS selection, data-transmission rate and delay, as well as spectrum allocation under different configurations, including data duplication, are discussed. Simulation results are also obtained and compared with the theoretical results, which validate the analysis and its efficiency.

DELUXE: A DL-Based Link Adaptation for URLLC/eMBB Multiplexing in 5G NR

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 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt; 125 \mu \text{s}$ </tex-math></inline-formula> ) in 5G NR.

Multi-Task Learning for Efficient Management of Beyond 5G Radio Access Network Architectures

Automation of Radio Access Network (RAN) operation is a fundamental feature to manage sustainable and efficient Beyond Fifth-generation wireless (5G) networks, in the context of the Next Generation Self-Organizing Network (NG-SON) vision. Machine Learning (ML) is already identified as the key ingredient of this vision, with new standardized and open architectures, like Open-RAN (O-RAN), taking momentum. In this paper, we propose models based on single-task and Multi-Task Learning (MTL) paradigms to address two RAN use cases, handover management and initial Modulation and Coding Scheme (MCS) selection. Traditional handover schemes have the drawback of taking into account the quality of the signals from the serving, and the target cell, before the handover. Also, initial MCS at the start of the session and after a handover usually is handled conservatively. The proposed ML solutions allow to address these drawbacks by 1) considering the expected Quality of Experience (QoE) resulting from the decision of a target cell to handover, as the driving principle of the handover decision and 2) using the experience extracted from network data to make smarter initial MCS allocations. In this line, we implement a realistic cellular simulation scenario by incorporating coverage holes to build an extensive database to train and test the proposed models. The results show that the ML-based models outperform the 3rd Generation Partnership Project (3GPP) standardized handover and initial MCS selection approaches by improving the QoE of users resulting from a handover and the throughput obtained upon establishing a new connection with a network. Besides that, using the obtained results, this paper extensively discusses the merits of leveraging the MTL model to address different, but related multiple RAN functions because it allows reusing a common learning architecture for multiple RAN use cases, which provides significant implementation advantages.

Joint selection of the MCS’s and power allocation coefficients in the two-user downlink PD-NOMA system

Power Domain Non-Orthogonal Multiple Access (PD-NOMA) is a perspective multiplexing technique for future cellular networks. Nevertheless, it is poorly studied and not applied in the existing systems due to the complexity of PD-NOMA signal processing, resource scheduling, and power allocation. The issue is that a modulation and coding scheme (MCS) selection, including power allocation, is a cooperative procedure considering the channel state information of each multiplexed user. It can be solved by enumerating all possible multiplexing combinations but at the expense of the high computational complexity. In our work, we propose a composed table with the joint MCS’s, which can be selected by the base station (BS) for the user multiplexing in a downlink PD-NOMA system based on their signal-to-noise (SNR) ratios. It allows selecting two MCS’s with two power allocation coefficients for both users and guarantees the 10% block error rate (BLER) performance in the additive white Gaussian noise (AWGN) channel. The joint MCS selection method is based on a max-rate scheduling strategy and provides system capacity maximization ignoring fairness between users. The proposed table is given in the Appendix.

A Joint Modulation-Coding Scheme and Resource Allocation in LTE Uplink

In Long Term Evolution (LTE) Resource Allocation Algorithms (RAAs) are an area of work where researchers are seeking to optimize the efficient use of scarce radio resources. The selection of an optimal Modulation and Coding Scheme (MCS) that allows LTE to adapt to channel conditions is a second area of ongoing work. In the wireless part of LTE, these two factors, RAA and MCS selection, are the most critical in optimization. In this paper, the performance of three resource allocation schemes is compared, and a new allocation scheme, Average MCS (AMCS) allocation, is proposed. AMCS is seen to outperform both “Minimum MCS (MMCS)” and “Average Signal to Interference and Noise Ratio MCS (SINR AMCS)” in terms of improvements to LTE Uplink (UL) performance. The three algorithms were implemented in the Vienna LTE-A Uplink Simulator v1.5.

Link-Level Throughput Maximization Using Deep Reinforcement Learning

A multi-agent deep reinforcement learning framework is proposed to address link level throughput maximization by power allocation and modulation and coding scheme (MCS) selection. Given the complex problem space, reward shaping is utilized instead of classical training procedures. The time-frame utilities are decomposed into subframe rewards, and a stepwise training procedure is proposed, starting from a simplified power allocation setup without MCS selection, incorporating MCS selection gradually, as the agents learn optimal power allocation. The proposed method outperforms both weighted minimum mean squared error (WMMSE) and Fractional Programming (FP) with idealized MCS selections.

Subband CQI Feedback-Based Multicast Resource Allocation in MIMO-OFDMA Networks

Multicast transmission is one of the key enablers toward a more spectral- and energy-efficient distribution of multimedia content in current and envisaged cellular networks. In order to ensure that all users in a multicast group are able to correctly decode the received data, most multicast techniques adopt rather conservative strategies that select a very robust modulation and coding scheme (MCS) whose characteristics are determined by the propagation conditions experienced by the worst user in the group. Obviously, this robustness comes at the prize of a low spectral efficiency. Moreover, in the specific context of wideband communication systems, the selection of the multicast MCS has often relied on the use of wideband channel quality indicators (CQIs) providing rather imprecise information regarding the potential capacity of the multicast channel. Only recently has the per-subband CQI been used to improve the spectral efficiency of the system without compromising the link robustness. However, most subband-CQI multicast schemes proposed so far rely on overpessimistic assumptions that preclude the achievement of high data rates. In this paper, novel subband CQI-based multicast strategies are proposed that, relying on the selection of more spectrally efficient transmission modes, lead to increased data rates while still being able to fulfill prescribed quality of service metrics. To this end, a constrained optimization problem is posed that seeks to maximize the data rate of the whole multicast group while ensuring that the average block error rate for all users remains below a threshold and that a minimum data rate is guaranteed for all users in the group.

Playback continuity and video quality driven optimisation for dynamic adaptive streaming over HTTP clients over wireless networks

In this study, the authors focus on segment characteristic analysis and joint optimisation of modulation and coding scheme (MCS) selection, resource block (RB) assignment and the block error ratio (BLER) determination and segment adaptation scheme to satisfy dynamic adaptive streaming over HTTP (DASH) clients over wireless networks. First, the authors define a utility function as the continuous playback time that the packets scheduled with the allocated resource can support. Then, the authors formulate the MCS selection, RB assignment and BLER determination into a mathematical model. The relationship among the above three factors can be explored instead of performing MCS selection and RB assignment with the fixed BLER. By decomposing the original problem into some sub-problems, the authors can get a solution to the original problem with low complexity. At the client level, the authors develop an adaptive segment request strategy based on the playback information, the segments' characteristics and the estimated transmission rate. To decrease the influence of the inaccurate estimations, an adaptive guard time interval based on the real playback information and transmission information of the previous segments is introduced. Simulation results show that the proposed algorithm can efficiently improve playback continuity and video quality over the existing algorithms.

Optimal DASH-Multicasting Over LTE

Dynamic Adaptive Streaming over HTTP (DASH) is a fast growing video streaming platform, which enables adaptive rate selection based on channel conditions. File Delivery over Unidirectional Transport (FLUTE) further enables multicasting of the DASH segments over LTE eMBMS systems. In this paper, an optimal DASH-multicasting solution is proposed to allow more DASH clients in an LTE network to receive better videos by optimizing the resource allocation, application-layer forward error correction (FEC) code rate and modulation and coding scheme (MCS) selection of each multicasting group, which corresponds to a FLUTE session. Multiple FLUTE sessions are considered to deliver multiple videos, each with different video rates, for enhancing the overall utility. We have applied the convex optimization method to find the optimal resource allocation in terms of utility for multiple FLUTE sessions. We also find the optimal FEC code rates to add redundancies to protect the video segments for each FLUTE session. Moreover, an efficient MCS selection is introduced to reduce the complexity of the algorithm. Simulation results, with realistic LTE parameters, are shown to prove the proposed scheme is optimal, with more DASH clients receiving better video representations within limited resources when compared to other existing algorithms.

Enhanced PSO based energy-efficient resource allocation and CQI based MCS selection in LTE-A heterogeneous system

In order to maximize system energy efficiency (EE) under user quality of service (QoS) restraints in Long Term Evolution-Advanced (LTE-A) networks, a constrained joint resource optimization allocation scheme is presented, which is NP-hard. Hence, we divide it into three sub-problems to reduce computation complexity, i.e., the resource block (RB) allocation, the power distribution, and the modulation and coding scheme (MCS) assignment for user codewords. Then an enhanced heuristic approach GAPSO is proposed and is adopted in the RB and power allocation respectively to reduce computational complexity further on. Moreover, a novel MCS allocation scheme is put forward, which could make a good balance between the system reliability and availability under different channel conditions. Simulation results show that the proposed GAPSO could achieve better performance in convergence speed and global optimum searching, and that the joint resource allocation scheme could improve energy efficiency effectively under user QoS requirements.

Ant colony learning method for joint MCS and resource block allocation in LTE Femtocell downlink for multimedia applications with QoS guarantees

With the emergence of bandwidth-intensive online mobile multimedia applications in wireless networks, in order to make mobile users enjoy better Quality of Service (QoS) under the conditions of limited resources, efficient radio spectrum resource allocation schemes are always desirable. This paper addresses the problem of joint Resource Block (RB) allocation and Modulation-and-Coding Scheme (MCS) selection in LTE femtocell DownLink (DL) for mobile multimedia applications. We first formulate the problem as an Integer Linear Program (ILP) whose objective is to minimize the number of allocated RBs of a closed femtocell, while guaranteeing minimum throughput for each user. In view of the NP-hardness of the ILP, we then propose an intelligent optimization learning algorithm called ACO-HM algorithm with reduced polynomial time complexity. The Ant Colony Optimization (ACO) learning algorithm exhibits better performance in machine learning and supports parallel search for the RB allocation, while the Harmonic Mean (HM) method is to select a more appropriate MCS than the MINimum/MAXimum MCS selection schemes (MIN/MAX). Simulation results show that compared with the ACO-MIN algorithm and the ACO-MAX algorithm, the proposed ACO-HM learning algorithm achieves better performance with fewer RBs and better QoS guarantees.

High throughput relay policy in wireless cooperative relaying networks based on stochastic control theory

This paper proposes a distributed relay and modulation and coding scheme (MCS) selection in wireless cooperative relaying networks where the adaptive modulation and coding (AMC) scheme is applied. First-order finite-state Markov channels (FSMCs) are used to model the wireless channels and make prediction. The objective of the relay policy is to select one relay and MCS among different alternatives in each time-slot according to their channel state information (CSI) with the goal of maximizing the throughput of the whole transmission period. The procedure of relay and MCS selection can be formulated as a discounted Markov decision chain, and the relay policy can be obtained with recent advances in stochastic control algorithms. Simulation results are presented to show the effectiveness of the proposed scheme.

Optimal Selection Criterion of the Modulation and Coding Scheme in Consideration of the Signaling Overhead of Mobile WiMAX Systems

An optimal selection criterion of the modulation and coding scheme (MCS) for maximizing spectral efficiency is proposed in consideration of the signaling overhead of mobile WiMAX systems with a hybrid automatic repeat request mechanism. A base station informs users about the resource assignments in each frame, and the allocation process generates a substantial signaling overhead, which influences the system throughput. However, the signaling overhead was ignored in previous MCS selection criteria. In this letter, the spectral efficiency is estimated on the basis of the signaling overhead and the number of transmissions. The performance of the proposed MCS selection criterion is evaluated in terms of the spectral efficiency in the mobile WiMAX system, with and without persistent allocation.

Optimal modulation and coding scheme selection in cellular networks with hybrid-ARQ error control

We propose an optimal modulation and coding scheme (MCS) selection criterion for maximizing user throughput in cellular networks. The proposed criterion adopts both the Chase combining and incremental redundancy based hybrid automatic repeat request (HARQ) mechanisms and it selects an MCS level that maximizes the expected throughput which is estimated by considering both the number of transmissions and successful decoding probability in HARQ operation. We also prove that the conventional MCS selection rule is not optimized with mathematical analysis. Through link-level and system-level simulations, we show that the proposed MCS selection criterion yields higher average cell throughput than the conventional MCS selection schemes for slowly varying channels.

A Study on Link Adaptation Scheme with Multiple Code Words for Spectral Efficiency Improvement on OFDM-MIMO Systems

In this paper, we propose the stream multiplexing scheme to achieve the high spectral efficiency on OFDM-MIMO system considering (a) the effects of the inter-stream interference suppression techniques, (b) the influence of the quality measurement in the receiver to select the modulation and coding scheme (MCS) and (c) the influence of the signaling overheads to the spectral efficiency. Two kinds of schemes for stream multiplexing, namely Multiple CodeWord (MCW) and Single CodeWord (SCW) are investigated and compared. In the simulations, we assume real channel estimation for SINR measurement for MCS selection and we compare the schemes with the spectral efficiency taking into account overheads. In consequence, it is presented that MCW can achieve the higher efficiency than SCW in the middle to high SINR region. Moreover, in 4 × 4 antenna configuration, a 2CW-MCW scheme where 2 codewords mapped onto 4 streams according to SINR of each streams is proposed. The scheme gives higher throughput by improving the MCS selection in the low SINR region due to increase of data length per a codeword and by reducing the signaling overhead due to the decrease of the number of codewords. As a result, the proposed scheme achieves spectral efficiency improvement in whole SINR region.

Evaluation of Asymmetric TDD Systems Employing AMC and HARQ by Considering MCS Selection Errors

In this paper, we evaluate the performance of asymmetric Time Division Duplex (TDD) system that employs Adaptive Modulation and Coding (AMC) and Hybrid ARQ, with consideration of the effect of control delays in TDD. Channel reciprocity characteristic in TDD allows utilization of open loop channel estimation to choose appropriate modulation and coding scheme (MCS) level for AMC. However, control delay in AMC and HARQ depends on TDD time slot allocation formats. Large control delay in AMC will result in false MCS selection due to the poor channel correlation between measured channel state from the received signals and instantaneous channel state of actual transmission with the MCS selected based on the measured channel state. We present an analytical approach to calculate the probability of MCS level selection error in different channel conditions for different asymmetric time slot allocations. From the theoretical and simulation results, it is shown that the instantaneous throughput per slot depends not only on maximum Doppler frequency but also on asymmetric slot allocations. Average delay time that yields error free packet reception in the downlink increases as the number of continuous downlink slots increases.

A Velocity Estimation-Based Channel Estimator for WCDMA Forward Link Receiver

In this letter, we propose a new practical architecture of channel estimator that can compensate for the signal distortion due to variable mobile station velocity in WCDMA forward link. The proposed Channel Estimator consists of IIR filter for channel estimation and Velocity Estimator for selection of IIR filter coefficients matched to mobile station velocity. The combination of IIR filter and Velocity Estimator can overcome the divergence problem of IIR filter due to the mobile station velocity. The Velocity Estimator estimates the speed of mobile station velocity by observing power spectrum of the received signal and exhibits stable operation in low SNR environment. To improve the resolution of velocity estimation without additional complexity due to large FFT size, an interpolator is adopted in the velocity estimator. The proposed channel estimation architecture can not only be used for WCDMA forward link but also is applicable for CDMA-2000 system without major modifications. Also, the Velocity Estimator can be applied in the channel quality measurement for the selection of MCS (Modulation and Coding Scheme) level in HSDPA transmission.