Network capacity optimisation in millimetre wave band using fractional frequency reuse

Inter Cell Interference (ICI) is a major challenge that degrades the performance of mobile systems, particularly for cell-edge users. This problem arises significantly in the next generation system, as the trend of deployment is with high densification, which yields an ultra-dense network (UDN). One of the challenges in UDN is the dramatic increase of ICI from surrounding cells. A common technique to minimise ICI is interference coordination techniques. In this context, the most efficient ICI coordination is fractional frequency reuse (FFR). This paper investigates the FFR in UDN millimetre wave network at 26GHz band. The focus is on dense network with short inter site distance (ISD), and higher order sectorisation (HOS). The metrics used in frequency reuse is the signal to interference plus noise ratio (SINR) rather than the distance, as the line of sight in millimetre wave can be easily blocked by obstacles even if they are in close proximity to the serving base station. The work shows that FFR can improve the network performance in terms of per user cell-edge data throughput and average cell throughput, and maintain the peak data throughput at a certain threshold. Furthermore, HOS has a potential gain over default sectored cells when the interference is carefully coordinated. The results show optimal values for bandwidth split per each scenario in FFR scheme to give the best trade-off between inner and outer zone users performance

An efficient scalable scheduling mac protocol for underwater sensor networks

Underwater Sensor Networks (UWSNs) utilise acoustic waves with comparatively lower loss and longer range than those of electromagnetic waves. However, energy remains a challenging issue in addition to long latency, high bit error rate, and limited bandwidth. Thus, collision and retransmission should be efficiently handled at Medium Access Control (MAC) layer in order to reduce the energy cost and also to improve the throughput and fairness across the network. In this paper, we propose a new reservation-based distributed MAC protocol called ED-MAC, which employs a duty cycle mechanism to address the spatial-temporal uncertainty and the hidden node problem to effectively avoid collisions and retransmissions. ED-MAC is a conflict-free protocol, where each sensor schedules itself independently using local information. Hence, ED-MAC can guarantee conflict-free transmissions and receptions of data packets. Compared with other conflict-free MAC protocols, ED-MAC is distributed and more reliable, i.e., it schedules according to the priority of sensor nodes which based on their depth in the network. We then evaluate design choices and protocol performance through extensive simulation to study the load effects and network scalability in each protocol. The results show that ED-MAC outperforms the contention-based MAC protocols and achieves a significant improvement in terms of successful delivery ratio, throughput, energy consumption, and fairness under varying offered traffic and number of nodes

Experimental Evaluation of Hybrid Fibre−Wireless System for 5G Networks

This article describes a novel experimental study considering a multiband fibre–wireless system for constructing the transport network for fifth-generation (5G) networks. This study describes the development and testing of a 5G new radio (NR) multi-input multi-output (MIMO) hybrid fibre–wireless (FiWi) system for enhanced mobile broadband (eMBB) using digital pre-distortion (DPD). Analog radio over fibre (A-RoF) technology was used to create the optical fronthaul (OFH) that includes a 3 GHz supercell in a long-range scenario as well as a femtocell scenario using the 20 GHz band. As a proof of concept, a Mach Zehnder modulator with two independent radio frequency waveforms modifies a 1310 nm optical carrier using a distributed feedback laser across 10 km of conventional standard single-mode fibre. It may be inferred that a hybrid FiWi-based MIMO-enabled 5G NR system based on OFH could be a strong competitor for future mobile haul applications. Moreover, a convolutional neural network (CNN)-based DPD is used to improve the performance of the link. The error vector magnitude (EVM) performance for 5G NR bands is predicted to fulfil the Third Generation Partnership Project’s (3GPP) Release 17 standards

Device-Free Localization for Human Activity Monitoring

Over the past few decades, human activity monitoring has grabbed considerable research attentions due to greater demand for human-centric applications in healthcare and assisted living. For instance, human activity monitoring can be adopted in smart building system to improve the building management as well as the quality of life, especially for the elderly people who are facing health deterioration due to aging factor, without neglecting the important aspects such as safety and energy consumption. The existing human monitoring technology requires additional sensors, such as GPS, PIR sensors, video camera, etc., which incur cost and have several drawbacks. There exist various solutions of using other technologies for human activity monitoring in a smartly controlled environment, either device-assisted or device-free. A radio frequency (RF)-based device-free indoor localization, known as device-free localization (DFL), has attracted a lot of research effort in recent years due its simplicity, low cost, and compatibility with the existing hardware equipped with RF interface. This chapter introduces the potential of RF signals, commonly adopted for wireless communications, as sensing tools for DFL system in human activity monitoring. DFL is based on the concept of radio irregularity where human existence in wireless communication field may interfere and change the wireless characteristics

Detection and Tracking of Pedestrians Using Doppler LiDAR

Pedestrian detection and tracking is necessary for autonomous vehicles and traffic manage- ment. This paper presents a novel solution to pedestrian detection and tracking for urban scenarios based on Doppler LiDAR that records both the position and velocity of the targets. The workflow consists of two stages. In the detection stage, the input point cloud is first segmented to form clus- ters, frame by frame. A subsequent multiple pedestrian separation process is introduced to further segment pedestrians close to each other. While a simple speed classifier is capable of extracting most of the moving pedestrians, a supervised machine learning-based classifier is adopted to detect pedestrians with insignificant radial velocity. In the tracking stage, the pedestrian’s state is estimated by a Kalman filter, which uses the speed information to estimate the pedestrian’s dynamics. Based on the similarity between the predicted and detected states of pedestrians, a greedy algorithm is adopted to associate the trajectories with the detection results. The presented detection and tracking methods are tested on two data sets collected in San Francisco, California by a mobile Doppler LiDAR system. The results of the pedestrian detection demonstrate that the proposed two-step classifier can improve the detection performance, particularly for detecting pedestrians far from the sensor. For both data sets, the use of Doppler speed information improves the F1-score and the recall by 15% to 20%. The subsequent tracking from the Kalman filter can achieve 83.9–55.3% for the multiple object tracking accuracy (MOTA), where the contribution of the speed measurements is secondary and insignificant

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

[DE] World Wide Web is the greatest boon towards the technological advancement of modern era. Using the benefits of Internet globally, anywhere and anytime, users can avail the benefits of accessing live and on demand video services. The streaming media systems such as YouTube, Netflix, and Apple Music are reining the multimedia world with frequent popularity among users. A key concern of quality perceived for video streaming applications over Internet is the Quality of Experience (QoE) that users go through. Due to changing network conditions, bit rate and initial delay and the multimedia file freezes or provide poor video quality to the end users, researchers across industry and academia are explored HTTP Adaptive Streaming (HAS), which split the video content into multiple segments and offer the clients at varying qualities. The video player at the client side plays a vital role in buffer management and choosing the appropriate bit rate for each such segment of video to be transmitted. A higher bit rate transmitted video pauses in between whereas, a lower bit rate video lacks in quality, requiring a tradeoff between them. The need of the hour was to adaptively varying the bit rate and video quality to match the transmission media conditions. Further, The main aim of this paper is to give an overview on the state of the art HAS techniques across multimedia and networking domains. A detailed survey was conducted to analyze challenges and solutions in adaptive streaming algorithms, QoE, network protocols, buffering and etc. It also focuses on various challenges on QoE influence factors in a fluctuating network condition, which are often ignored in present HAS methodologies. Furthermore, this survey will enable network and multimedia researchers a fair amount of understanding about the latest happenings of adaptive streaming and the necessary improvements that can be incorporated in future developments.Abdullah, MTA.; Lloret, J.; Canovas Solbes, A.; García-García, L. (2017). Survey of Transportation of Adaptive Multimedia Streaming service in Internet. Network Protocols and Algorithms. 9(1-2):85-125. doi:10.5296/npa.v9i1-2.12412S8512591-

Improved capacity and fairness of massive machine type communications in millimetre wave 5G network

In the Fifth Generation (5G) wireless standard, the Internet of Things (IoT) will interconnect billions of Machine Type Communications (MTC) devices. Fixed and mobile wearable devices and sensors are expected to contribute to the majority of IoT traffic. MTC device mobility has been considered with three speeds, namely zero (fixed) and medium and high speeds of 30 and 100 kmph. Different values for device mobility are used to simulate the impact of device mobility on MTC traffic. This work demonstrates the gain of using distributed antennas on MTC traffic in terms of spectral efficiency and fairness among MTC devices, which affects the number of devices that can be successfully connected. The mutual use of Distributed Base Stations (DBS) with Remote Radio Units (RRU) and the adoption of the millimetre wave band, particularly in the 26 GHz range, have been considered the key enabling technologies for addressing MTC traffic growth. An algorithm has been set to schedule this type of traffic and to show whether MTC devices completed their traffic upload or failed to reach the margin. The gains of the new architecture have been demonstrated in terms of spectral efficiency, data throughput and the fairness index