Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G

Millimeter-wave (mm-wave) communication is the spectral frontier to meet the anticipated significant volume of high data traffic processing in next-generation systems. The primary challenges in mm-wave can be overcome by reducing complexity and power consumption by large antenna arrays for massive multiple-input multiple-output (mMIMO) systems. However, the circuit power consumption is expected to increase rapidly. The precoding in mm-wave mMIMO systems cannot be successfully achieved at baseband using digital precoders, owing to the high cost and power consumption of signal mixers and analog-to-digital converters. Nevertheless, hybrid analog–digital precoders are considered a cost-effective solution. In this work, we introduce a novel method for optimizing energy efficiency (EE) in the upper-bound multiuser (MU) - mMIMO system and the cost efficiency of quantized hybrid precoding (HP) design. We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). In the alternating minimization algorithms, low complexity is proposed by enforcing an orthogonal constraint on the digital precoders to realize the joint optimization of computational complexity and communication power. Therefore, the alternating minimization algorithm enhances HP by improving the performance of the FCS through advanced phase extraction, which involves high complexity. Meanwhile, the alternating minimization algorithm develops a PCS to achieve low complexity using HP. The simulation results demonstrate that the proposed algorithm for MU - mMIMO systems improves EE. The power-saving ratio is also enhanced for PCS and FCS by 48.3% and 17.12%, respectively

Long-baseline, sub-decimeter kinematic GPS positioning of moving object, with potential application to monitor ocean surface wave

Precise relative kinematic positioning of moving platforms using GPS carrier phase observables has numerous applications. One prominent application is utilization of highly stabilized GPS technology mounted on the buoy, which is specially designed for detecting tsunami wave at open sea. The essential point of this research is to investigate a potential use of a GPS tsunami buoy for the purpose of tsunami early warning system with long-baseline kinematic GPS processing method. The rule of thumb GPS positioning concept, GPS position results are affected by. baseline length mostly due to de-correlation of atmospheric errors. As baseline lengths increase, position results degrade due to the difficulty to correctly fix the cariier phase ambiguity to its integer value. carrier phase fixed ambiguity solutions are more accurate that float arnbiguify solutions. It is generally accepted that carrier phase can be successfUlly fixed for baselines of up to 10 km. After that, fixing ambiguities becomes more difficult and risky. It would be certainty more advantageous to have a reliable float solution rather than an unreliable fixed solution. In this study, we have developed a new quasi-real time long-baseline kinematic analysis method using dual-frequency carrier phase with floated ambiguities, implemented in the Bernese GPS Software Version 5.0. We demonstrate that early detection of a damaging tsunami can be achieved by tracking the anomalous changes in sea surface height. The movements of a GPS buoy relative to a base station with baseline length of 500 km have been monitored in quasi-real time mode, and the tsunami waves caused by the 5th September 2004 Off Kii Peninsula earthquake, Japan, have been successful detected as they went by, even though these were only 15 cm high. The filtered record of the solution closely resembles that of short baseline, with RMS of 3.4 cm over 2.5 hours. To test the robustness of our Iong-baseline kinematic GPS method under various meteorological, we conducted the GPS tsunami buoy data analysis continuously for 8 days to monitor the motion of the buoy. The average scatterings of GPS buoy heights by the low-pass filtered 1 -Hz positioning result after tidal correction are about 3.4 cm and 1.2 cm under both typhoon and calm weather conditions. This accuracy is precise enough to be applicable to a tsunami early warning system. Since our long-baseline kinematic GPS analysis is effective to a long baseline up to 500 km, we can place a GPS buoy far offshore, which ensures an adequate evacuation time even, for people living on the coast

An Efficient Scheme for Acoustic Echo Canceller Implementation Using Offset Binary Coding

This article presents an efficient design and implementation scheme for a low-area and low-power acoustic echo canceller. The design employs the block least mean square algorithm-based adaptive filter (ADF) using offset binary coding. The proposed approach first formulates the ADF by splitting the matrix–vector multiplication into smaller ones. Each of them is then realized with lookup tables and shift accumulate units with offset terms. An efficient scheme is suggested to update the offset terms from the corresponding lookup tables. In addition, a novel optimization scheme is proposed based on the grouping of partial products (PPs) and moving windows. The PPs are generated in two parallel styles using adders, multiplexers, and registers. The optimized architecture is shared to compute both the filter output and coefficient increment terms in every iteration. The fixed-point quantization model for the architecture is also discussed. Accuracy measure is defined to characterize the proposed design and compare it with the Cramer–Rao lower bound. Simulations are carried out to evaluate the performance of the proposed design. Field-programmable gate array implementation results and application-specific integrated circuit synthesis show that the proposed design outperforms the state-of-the-art architectures

Two Distributed Arithmetic Based High Throughput Architectures of Non-Pipelined LMS Adaptive Filters

Distributed arithmetic (DA) is an efficient look-up table (LUT) based approach. The throughput of DA based implementation is limited by the LUT size. This paper presents two high-throughput architectures (Type I and II) of non-pipelined DA based least-mean-square (LMS) adaptive filters (ADFs) using two's complement (TC) and offset-binary coding (OBC) respectively. We formulate the LMS algorithm using the steepest descent approach with possible extension to its power-normalized LMS version and followed by its convergence properties. The coefficient update equation of LMS algorithm is then transformed via TC DA and OBC DA to design and develop non-pipelined architectures of ADFs. The proposed structures employ the LUT pre-decomposition technique to increase the throughput performance. It enables the same mapping scheme for concurrent update of the decomposed LUTs. An efficient fixed-point quantization model for the evaluation of proposed structures from a realistic point-of-view is also presented. It is found that Type II structure provides higher throughput than Type I structure at the expense of slow convergence rate with almost the same steady-state mean square error. Unlike existing non-pipelined LMS ADFs, the proposed structures offer very high throughput performance, especially with large order DA base units. Furthermore, they are capable of performing less number of additions in every filter cycle. Based on the simulation results, it is found that 256th order filter with 8th order DA base unit using Type I structure provides 9.41 × higher throughput while Type II structure provides 16.68 × higher throughput as compared to the best existing design. Synthesis results show that 32nd order filter with 8th order DA base unit using Type I structure achieves 38.76% less minimum sampling period (MSP), occupies 28.62% more area, consumes 67.18% more power, utilizes 49.06% more slice LUTs and 3.31% more flip-flops (FFs), whereas Type II structure achieves 51.25% less MSP, occupies 21.42% more area, consumes 47.84% more power, utilizes 29.10% more slice LUTs and 1.47% fewer FFs as compared to the best existing design.</p

Two Distributed Arithmetic Based High Throughput Architectures of Non-Pipelined LMS Adaptive Filters

Distributed arithmetic (DA) is an efficient look-up table (LUT) based approach. The throughput of DA based implementation is limited by the LUT size. This paper presents two high-throughput architectures (Type I and II) of non-pipelined DA based least-mean-square (LMS) adaptive filters (ADFs) using twos complement (TC) and offset-binary coding (OBC) respectively. We formulate the LMS algorithm using the steepest descent approach with possible extension to its power-normalized LMS version and followed by its convergence properties. The coefficient update equation of LMS algorithm is then transformed via TC DA and OBC DA to design and develop non-pipelined architectures of ADFs. The proposed structures employ the LUT pre-decomposition technique to increase the throughput performance. It enables the same mapping scheme for concurrent update of the decomposed LUTs. An efficient fixed-point quantization model for the evaluation of proposed structures from a realistic point-of-view is also presented. It is found that Type II structure provides higher throughput than Type I structure at the expense of slow convergence rate with almost the same steady-state mean square error. Unlike existing non-pipelined LMS ADFs, the proposed structures offer very high throughput performance, especially with large order DA base units. Furthermore, they are capable of performing less number of additions in every filter cycle. Based on the simulation results, it is found that 256th order filter with 8th order DA base unit using Type I structure provides 9 :41 x higher throughput while Type II structure provides 16 :68 x higher throughput as compared to the best existing design. Synthesis results show that 32nd order filter with 8th order DA base unit using Type I structure achieves 38 :76% less minimum sampling period (MSP), occupies 28 :62% more area, consumes 67 :18% more power, utilizes 49 :06% more slice LUTs and 3 :31% more flip-flops (FFs), whereas Type II structure achieves 51 :25% less MSP, occupies 21 :42% more area, consumes 47 :84% more power, utilizes 29 :10% more slice LUTs and 1 :47% fewer FFs as compared to the best existing design.Funding Agencies|Umm Al-Qura University [22UQU4350362DSR01]; University of Tabuk [S-1442-0151]</p

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research

Low-Area and Low-Power VLSI Architectures for Long Short-Term Memory Networks

Long short-term memory (LSTM) networks are extensively used in various sequential learning tasks, including speech recognition. Their significance in real-world applications has prompted the demand for cost-effective and power-efficient designs. This paper introduces LSTM architectures based on distributed arithmetic (DA), utilizing circulant and block-circulant matrix-vector multiplications (MVMs) for network compression. The quantized weights-oriented approach for training circulant and block-circulant matrices is considered. By formulating fixed-point circulant/block-circulant MVMs, we explore the impact of kernel size on accuracy. Our DA-based approach employs shared full and partial methods of add-store/store-add followed by a select unit to realize an MVM. It is then coupled with a multi-partial strategy to reduce complexity for larger kernel sizes. Further complexity reduction is achieved by optimizing decoders of multiple select units. Pipelining in add-store enhances speed at the expense of a few pipelined registers. The results of the field-programmable gate array showcase the superiority of our proposed architectures based on the partial store-add method, delivering reductions of 98.71% in DSP slices, 33.59% in slice look-up tables, 13.43% in flip-flops, and 29.76% in power compared to the state-of-the-art.</p

UAV Computing-Assisted Search and Rescue Mission Framework for Disaster and Harsh Environment Mitigation

Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments

Computing in the sky: A survey on intelligent ubiquitous computing for UAV-assisted 6G networks and industry 4.0/5.0

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research