Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8 user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than 2.6x in terms of throughput per FPGA look-up tables.

High-Throughput Data Detection for Massive MU-MIMO-OFDM using Coordinate Descent

Mobile Backhauling provides an interface between radio controller and base stations, mostly realized with a physical medium such as optical fibers or microwave radio links. With the huge mobile traffic due to an increase in mobile subscribers as well as deployment of 4G and 5G cellular network technologies, better solutions for capacity and coverage should be provided in order to enhance spectral efficiency. For 4G cellular networks, mobile backhaul networks deal with capacity, availability, deployment cost, and long‐distance reaches. In addition, mobile backhaul networks based on the 5G network incurs additional challenges that include 1 ms or less ultralow latency time requirements and ultra‐dense nature of the network capabilities. Therefore, for 5G technologies, latency delay, QoS, packet efficiency, noise suppression, and mitigation techniques, efficient modulation schemes, and packet network timing synchronization are some aspects that are to be dealt with while designing efficient backhaul approaches (wired/wireless). Current backhaul systems typically use cost‐effective solutions (eg, ‐Wi‐Fi and WiMAX)‐based packet‐switched technologies, especially Ethernet/Internet technologies and high‐speed optical fiber links.

Review of optical and wireless backhaul networks and emerging trends of next generation 5G and 6G technologies

Today, ensuring work safety is considered to be one of the top priorities for various industries. Workplace injuries, illnesses, and deaths often entail substantial production and financial losses, governmental checks, series of dismissals, and loss of reputation. Wearable devices are one of the technologies that flourished with the fourth industrial revolution or Industry 4.0, allowing employers to monitor and maintain safety at workplaces. The purpose of this article is to systematize knowledge in the field of industrial wearables’ safety to assess the relevance of their use in enterprises as the technology maintaining occupational safety, to correlate the benefits and costs of their implementation, and, by identifying research gaps, to outline promising directions for future work in this area. We categorize industrial wearable functions into four classes (monitoring, supporting, training, and tracking) and provide a classification of the metrics collected by wearables to better understand the potential role of wearable technology in preserving workplace safety. Furthermore, we discuss key communication technologies and localization techniques utilized in wearable-based work safety solutions. Finally, we analyze the main challenges that need to be addressed to further enable and support the use of wearable devices for industrial work safety.

https://www.mdpi.com/1424-8220/21/11/3844/pdf

Wearables for Industrial Work Safety: A Survey.

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

/pdf/optimized-cnns-to-indoor-localization-through-ble-sensors-2vhglzvso0.pdf

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO.

As a classical and effective method for solving various time-varying problems, the zeroing neural network (ZNN) is widely applied in the scientific and industrial realms. In plentiful studies on the ZNN model, its robustness and convergence have been two essential criteria to evaluate the quality of the model. Improvements in the ZNN model have been focused on its convergence speed; however, the adjustability of its convergence speed has been neglected in most prior works, which restricts its extensive promotion in practical application. Considering the above-mentioned issue, a well-designed activation function (WDAF) is designed. Based on the WDAF, a robust predefined-time convergence ZNN (RPTCZNN) model with adjustable convergence speed is proposed to solve the dynamic matrix inversion problem. In addition, the upper bound of the RPTCZNN model&#x2019;s convergence time is theoretically validated by strict mathematical analysis in a noiseless and noisy environment. Finally, several simulation experiments of the proposed model are conducted to find solutions of dynamic matrix inversion with different dimensions. Moreover, the realization of the tracking control of the robotic manipulator further illustrates the model&#x2019;s superior convergence and robustness. 

A Robust Predefined-Time Convergence Zeroing Neural Network for Dynamic Matrix Inversion

Centralized Massive Multiple Input Multiple Output (MIMO) uplink detection techniques for baseband processing possess severe bottleneck in terms of interconnect bandwidth and computational complexity. This problem has been addressed in the current work by adapting the centralized Newton method for decentralized MIMO uplink detection leveraging several Base Station antenna clusters. The proposed decentralized Newton (DN) method achieves error-rate performance close to centralized Zero Forcing detector as compared to other decentralized techniques. Two hardware topologies, namely the ring and the star topologies, are proposed to assess and discuss the trade-off among interconnect bandwidth and throughput, in comparison with contemporary decentralized MIMO uplink detection techniques. As such the following findings are elaborated. On BS antenna cluster scaling for different MIMO system configurations, the ring topology provides high throughput at constant interconnect bandwidth, while the star topology provides lower latency with a deterministic variation in the hardware resource consumption. Due to strategic optimizations on the hardware implementation, additional user equipment can be allotted at a fractional increase in Field Programmable Gate Array resource consumption, improved energy efficiency, and increased transaction of bits per Joule. The ring topology can process additional subcarrier at a fractional increase in latency and improved system throughput.

Hardware Topologies for Decentralized Large-Scale MIMO Detection Using Newton Method

In this paper, we propose a framework for the indoor localization in non-line-of-sight (NLoS) conditions using partial knowledge of the channel state information (CSI) obtained from low power wide area (LPWA) radios. The framework is based on NLoS CSI classification using machine learning (ML) and deep learning (DL) models that leverage measurements using end-to-end LoRaWAN network. The measurement set-up provides access to not only the sensor data but also to the physical layer metrics such as the receiver signal strength (RSS), spreading factor (SF), and the frequency hoping signature to name a few. Since LoRa is based on narrow band spread spectrum modulation techniques derived from chirp spread spectrum technology, the CSI are partial in nature. We demonstrate that the partial CSI with frequency hopping signature can be efficiently exploited to predict indoor location with accuracy of more than 98% using a multilayer neural network (MNN).

Machine learning enabled tools and methods for indoor localization using low power wireless network

This paper investigates real-time DSP and FPGA implementations of a low complexity technique for asynchronous multiuser delay acquisition and time varying channel tracking for multipath channels in WCDMA and cdma2000 systems. A multiuser-LMS-like structure along with smoothing/prediction filters to improve tracking quality is reviewed. We investigate an efficient implementation based on FFT/IFFT technique, under fixed-point data representation and computation constraint. The measured BER reveals that fixed-point implementation is feasible at possibly no performance degradation. Based on real time execution made on a fixed-point high performance DSP, the maximum number of users is 15 and 17 for the proposed method and correlator, respectively. Due to the inherent parallelism and regular data flow FPGA implementation is suggested wherein a maximum number of users more than 80 can be afforded in Xilinx Virtex? II Pro device.

Real-time DSP and FPGA Implementation of Wiener LMS Based Multipath Channel Estimation in 3G CDMA Systems

In large scale multiple-input multiple-output (MIMO), high spectral and energy efficiencies comes at the expense of a high computational complexity baseband processing. Many contributions have been proposed to reduce such complexity using matrix inversion approximation techniques for instance. On the other hand, to reduce the constraint on the interconnects’ bandwidth, fewer decentralized processing techniques have emerged. Here, we propose a computationally efficient technique based on embedding one single Gauss-Seidel iteration within every ADMM based detection iteration. The simulations are performed using an LTE-like TDD-OFDM frame structure and waveform, under perfect and non-perfect channel state information (CSI). Early results reveal that the proposed ADMM-GS algorithm can outperform the centralised GS based technique processing in a high SNR region and high load regime. In addition ADMM-GS’ performance exhibits relatively less sensitivity to channel estimation error; a characteristic inherited from the centralised GS technique.

Efficient Distributed Processing for Large Scale MIMO Detection

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/cmu2.12076

Messaoud Ahmed Ouameur

Papers

Hardware Topologies for Decentralized Large-Scale MIMO Detection Using Newton Method

Machine learning enabled tools and methods for indoor localization using low power wireless network

Real-time DSP and FPGA Implementation of Wiener LMS Based Multipath Channel Estimation in 3G CDMA Systems

Efficient Distributed Processing for Large Scale MIMO Detection

Early results on deep unfolded conjugate gradient-based large-scale MIMO detection