Network slicing plays a critical role in enabling multiple virtualized and independent network services to be created on top of a common physical network infrastructure. In this paper, we introduce a deep reinforcement learning (DRL)-based radio resource management (RRM) solution for radio access network (RAN) slicing under service-level agreement (SLA) guarantees. The objective of this solution is to minimize the SLA violation. Our method is designed with a two-level scheduling structure that works seamlessly under Open Radio Access Network (O-RAN) architecture. Specifically, at an upper level, a DRL-based inter-slice scheduler is working on a coarse time granularity to allocate resources to network slices. And at a lower level, an existing intra-slice scheduler such as proportional fair (PF) is working on a fine time granularity to allocate slice dedicated resources to slice users. This setting makes our solution O-RAN compliant and ready to be deployed as an ‘xApp’ on the RAN Intelligent Controller (RIC). For performance evaluation and proof of concept purposes, we develop two platforms, one industry-level simulator and one O-RAN compliant testbed; evaluation on both platforms demonstrates our solution’s superior performance over conventional methods.

In this paper, we introduce StructNet-CE, a novel real-time online learning framework for MIMO-OFDM channel estimation, which only utilizes over-the-air (OTA) reference signals (RS) for online channel estimation on a slot basis without assuming the availability of any channel knowledge. To achieve real-time and efficient channel learning, the design of StructNet-CE leverages the structural information inherent in the MIMO-OFDM system: the repetitive structure of modulation constellation and the invariant property of symbol classification to inter-stream interference. The embedded structural information enables StructNet-CE to conduct channel estimation through the underlying symbol detection task and accurately learn MIMO channels through the limited RS with the scattered RS configuration adopted in 5G/5G-Advanced slots. Numerical experiments demonstrate that the channel estimation performance is significantly improved by incorporating the structural knowledge, achieving a mean square error (MSE) reduction ranging from around 44.41% to 95.54% compared to existing methods. Furthermore, StructNet-CE is compatible and readily applicable to current and future wireless networks, demonstrating the effectiveness, importance, and relevance of combining machine learning techniques with domain knowledge for wireless systems.

The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for detecting transmitted data symbols at the receiver, especially for machine learning-based approaches. While it is crucial to explore effective ways to exploit pilots, one can also take advantage of the data symbols to improve detection performance. Thus, this paper introduces an online attention-based approach, namely RC-AttStructNet-DF, that can efficiently utilize pilot symbols and be dynamically updated with the detected payload data using the decision feedback (DF) mechanism. Reservoir computing (RC) is employed in the time domain network to facilitate efficient online training. The frequency domain network adopts the novel 2D multi-head attention (MHA) module to capture the time and frequency correlations, and the structural-based StructNet to facilitate the DF mechanism. The attention loss is designed to learn the frequency domain network. The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols. The effectiveness of the RC-AttStructNet-DF approach is demonstrated through extensive experiments in MIMO-OFDM and massive MIMO-OFDM systems with different modulation orders and under various scenarios.

In this paper, we develop a learning-based symbol detection algorithm for massive MIMO-OFDM systems. To exploit the structure information inherited in the received signals from massive antenna array, multi-mode reservoir computing is adopted as the building block to facilitate over-the-air training in time domain. In addition, alternating recursive least square optimization method, and decision feedback mechanism are utilized in our algorithm to achieve the real-time learning capability. That is, the neural network is trained purely online with its weights updated on an OFDM symbol basis to promptly and adaptively track the dynamic environment. Furthermore, an online learning-based module is devised to compensate the nonlinear distortion caused by RF circuit components. On top of that, a learning-efficient classifier named StructNet is introduced in frequency domain to further improve the symbol detection performance by utilizing the QAM constellation structural pattern. Evaluation results demonstrate that our algorithm achieves substantial gain over traditional model-based approach and state-of-the-art learning-based techniques under dynamic channel environment and RF circuit nonlinear distortion. Moreover, empirical result reveals our NN model is robust to training label error, which benefits the decision feedback mechanism.

In this paper, we develop a learning-based symbol detection algorithm for massive MIMO systems. To exploit the structural information inherited in the received signals from massive antenna array, multi-mode reservoir computing is adopted as the building block to facilitate over-the-air training. In addition, alternating recursive least square optimization method, and decision feedback mechanism are utilized in our algorithm to achieve the real-time learning capability. That is, the neural network is trained purely online with its weights updated on an OFDM symbol basis to promptly and adaptively track the dynamic environment. Evaluation results demonstrate that our algorithm achieves substantial gain over traditional model-based approach and state-of-the-art learning-based techniques in dynamic channel environment. Moreover, empirical result reveals our NN model is robust to training label error, which benefits the decision feedback mechanism.

Recently, there have been renewed interests in applying machine learning (ML) techniques to wireless systems. Nevertheless, ML-based approaches often require a large amount of data in training, and prior ML-based MIMO symbol detectors usually adopt offline learning approaches, which are not applicable to real-time signal processing. This paper adopts echo state network (ESN), a prominent type of reservoir computing (RC), to the real-time symbol detection task in MIMO-OFDM systems. Two novel ESN training methods, namely recursive-least-square and generalized adaptive weighted recursive-least-square, are introduced to enhance the performance of ESN training. Furthermore, a decision feedback mechanism is adopted to improve training efficiency and BER performance. Simulation studies show that the proposed methods perform better than previous conventional and ML-based MIMO symbol detectors. Finally, the effectiveness of our RC-based approach is validated with a software-defined radio (SDR) transceiver and extensive field tests in various real-world scenarios. To the best of our knowledge, this is the first real-time SDR implementation for ML-based MIMO-OFDM symbol detectors. Our work strongly indicates that ML-based signal processing could be a promising and critical approach for future wireless networks.

In this paper, we consider a real-time deep learning-based symbol detection approach for MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the time-frequency structure of OFDM signals, a recurrent neural network (RNN) with deep feedforward output layers is introduced, where the recurrent layers and feedforward output layers are designed to process time-domain and frequency-domain information respectively. Reservoir computing (RC), a special type of RNN, and extreme learning machine (ELM), a special type of feedforward neural network, are chosen as the corresponding building blocks to facilitate over-the-air training. An online training loss objective is introduced to recursively update the neural weights in real-time. We believe this is the first work in the literature to realize real-time machine learning for MIMO-OFDM symbol detection, i.e., conducting NN-based symbol detection on an OFDM symbol basis. We demonstrate that (1) theIEEEstandardized WiFi training sequence can be directly applied as the real-time training sequence (2) the symbol detection performance can be further improved by using our theoretically derived pilot pattern. Evaluation results show that our RC-ELM-based symbol detection method outperforms traditional model-based techniques as well as state-of-the-art learning-based approaches in highly dynamic channel environments for real-time symbol detection.

In this paper, we introduce a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A binary classifier leverages the repetitive constellation structure in the system to perform multi-class detection. The incorporation of RC allows the RC-Struct to be learned in a purely online fashion with extremely limited pilot symbols in each OFDM subframe. The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity. Experiments show that the introduced RC-Struct outperforms both the conventional model-based symbol detection approaches and the state-of-the-art learning-based strategies in terms of bit error rate (BER). The advantages of RC-Struct over existing methods become more significant when rank and link adaptation are adopted. The introduced RC-Struct sheds light on combining communication domain knowledge and learning-based receive processing for 5G/5G-Advanced and Beyond.

The echo state network (ESN) is a recently developed machine-learning paradigm whose processing capabilities rely on the dynamical behavior of recurrent neural networks. Its performance outperforms traditional recurrent neural networks in nonlinear system identification and temporal information processing applications. We design and implement a cost-efficient ESN architecture on field-programmable gate array (FPGA) that explores the full capacity of digital signal processor blocks on low-cost and low-power FPGA hardware. Specifically, our scalable ESN architecture on FPGA exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks. The proposed architecture includes a linear combination processor with negligible deployment of configurable logic blocks and a high-accuracy nonlinear function approximator. Our work is verified with the prediction task on the classical NARMA dataset and a symbol detection task for orthogonal frequency division multiplexing systems using a wireless communication testbed built on a software-defined radio platform. Experiments and performance measurement show that the new ESN architecture is capable of processing real-world data efficiently for low-cost and low-power applications.

Current studies that apply reinforcement learning (RL) to dynamic spectrum access (DSA) problems in wireless communications systems mainly focus on model-free RL (MFRL). However, in practice, MFRL requires a large number of samples to achieve good performance making it impractical in real-time applications such as DSA. Combining model-free and model-based RL can potentially reduce the sample complexity while achieving a similar level of performance as MFRL as long as the learned model is accurate enough. However, in a complex environment, the learned model is never perfect. In this article, we combine model-free and model-based RL, and introduce an algorithm that can work with an imperfectly learned model to accelerate the MFRL. Results show our algorithm achieves higher sample efficiency than the standard MFRL algorithm and the Dyna algorithm (a standard algorithm integrating model-based RL and MFRL) with much lower computation complexity than the Dyna algorithm. For the extreme case where the learned model is highly inaccurate, the Dyna algorithm performs even worse than the MFRL algorithm while our algorithm can still outperform the MFRL algorithm.

Mobile edge and vehicle-based depth sending and real-time point cloud communication is an essential subtask enabling autonomous driving. In this paper, we propose a framework for point cloud multicast in VANETs using vehicle to infrastructure (V2I) communication. We employ a scalable Binary Tree embedded Quad Tree (BTQT) point cloud source encoder with bitrate elasticity to match with an adaptive random network coding (ARNC) to multicast different layers to the vehicles. The scalability of our BTQT encoded point cloud provides a trade-off in the received voxel size/quality vs channel condition whereas the ARNC helps maximize the throughput under a hard delay constraint. The solution is tested with the outdoor 3D point cloud dataset from MERL for autonomous driving. The users with good channel conditions receive a near lossless point cloud whereas users with bad channel conditions are still able to receive at least the base layer point cloud.