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Despite its high computational complexity, SIC can potentially decode and remove strong interfering signals from the aggregate received signal, which can significantly boost the user’s performance.
By redirecting uplink traffic from Wi-Fi to LTE, Boost avoids resource waste due to Wi-Fi contention in the uplink.
As a result, how to further boost signal level is the key to achieving a larger imaging depth.
Proceedings ArticleDOI
Julang Ying, Kaveh Pahlavan, Xinrong Li 
01 Oct 2017
9 Citations
The radiated Received-Signal-Strength (RSS) from these devices can be used to improve the precision of the commonly used RSS-based Wi-Fi localization.
First, improved high boost filter is proposed to enhance the high frequency signal where the target may appear and suppress the low frequency signal.
Open accessProceedings ArticleDOI
04 Apr 2017
19 Citations
We demonstrate that superoptimization can dramatically improve the performance of Google Native Client, a SFI system that ships inside the Google Chrome Browser.
We show how these techniques can boost and enhance wireless networking operation in the 60 GHz band.
In this paper, we present a new feature of Wi-Fi Boost, its radio link management, which allows to smartly steer the downlink traffic between both LTE and Wi-Fi upon congestion detection.

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What are the latest advances in massive MIMO?
5 answers
The latest advances in massive MIMO include the use of a large number of antenna elements to achieve high throughput, low latency, and high energy efficiency in wireless communication systems. Different architectures such as digital, analog, and hybrid are being explored to optimize performance and complexity tradeoffs. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) techniques is being leveraged to monitor and optimize the massive MIMO sub-system, enabling programmability and flexibility through techniques like network slicing and network function virtualization. In the context of full-duplex (FD) massive MIMO, unified architectures comprising analog and digital transmit and receive beamforming, as well as analog and digital self-interference cancellation, are being developed to improve spectral efficiency and enable simultaneous uplink and downlink operations. Furthermore, massive MIMO antenna systems are being explored for location estimation of user equipment, particularly in indoor and cluttered urban environments, due to their high angular resolution and low-cost implementation.
What are the common network forensics tools that could be used by the attacker and the investigator?
5 answers
Network forensic tools are crucial for both attackers and investigators. Attackers can use these tools to monitor network performance and carry out internal and external network attacks. On the other hand, investigators rely on network forensic tools to perform forensic analysis after a security breach or cyber-attack. These tools help in evidence collection, analysis, and tracking of network packets and events. There are various open source and commercial network forensic tools available in the market, which offer advantages such as monitoring network compromises and providing comprehensive analysis of network data. Some commonly used tools include Snort, Wireshark, and memory forensics tools. These tools enable investigators to extract digital evidence related to network crimes and conduct in-depth analysis of network traffic, IO graphs, flow graphs, and operating systems.
How to predict MOS for Video Quality of Experience Anika?
5 answers
To predict the Mean Opinion Score (MOS) for Video Quality of Experience (QoE), several methods have been proposed. One approach is to use machine learning models that are continuously trained using both quantitative video metrics and qualitative user surveys. Another method involves the use of ML-classifiers to predict MOS based on performance metrics calculated from subjective evaluations. Additionally, a novel approach estimates intervals of video quality instead of a single MOS value, by fusing well-known video quality estimators. Another framework involves the construction of a k-dimensional QoE space, where instantaneous parameter values are matched with indices to infer QoE. Finally, a linear approach predicts MOS from obtained dimensional ratings.
How do 2G, 3G, 4G and 5G compare in terms of speed, latency, coverage, and cost?
5 answers
2G, 3G, 4G, and 5G networks differ in terms of speed, latency, coverage, and cost. 5G networks, such as those launched by major US carriers, offer higher speeds and lower latency compared to 4G networks. They are designed to accommodate a larger number of devices and provide improved security. However, severe weather conditions like dust or sand can significantly degrade the performance of 5G networks. On the other hand, 4G networks favor lower-band spectrum for better coverage. In terms of cost, the transition from 4G to 5G involves advancements in hardware integration and packaging technologies, which may result in higher costs. Overall, 5G networks offer higher speeds, lower latency, and improved security, but their performance can be more affected by severe weather conditions compared to 4G networks.
How can the data flow in a network be optimized for advanced machine learning applications?
5 answers
To optimize the data flow in a network for advanced machine learning applications, several approaches have been proposed. One approach is to distribute the machine learning computation across a cluster of servers, which reduces training time but is often limited by network bottlenecks. Another approach is to compress important algorithm information to bits for communication over a digital channel, while still preserving the convergence properties of the algorithm. Additionally, a host-based communication layer can be introduced to improve the network performance of distributed machine learning systems, through traffic reduction techniques and traffic management. Furthermore, a declarative machine learning system can optimize workflow execution end-to-end and across iterations, minimizing runtime per iteration and facilitating iterative development. These approaches highlight the importance of network optimization and communication protocols in enabling efficient machine learning over networks.
Power Resource Control analysis theory?
3 answers
Power resource control theory posits that individuals use prosocial and/or coercive strategies to access social resources. The theory has utility for understanding adolescents' engagement in bullying role behaviors. In the field of ultra-dense networks (UDNs), a distributed joint spectrum resource allocation strategy with power control is proposed to reduce inter-cell interference (ICI) caused by small base stations (SBSs). Power control theory, developed by Hagan, focuses on power relations within a family system through patriarchy and its impact on gender inequalities. This theory suggests that positions of power within the work environment translate to power relations within the home, leading to gender differences in delinquency rates. Power-control theory combines elements from traditional control theories with measures of household power to explain the gender gap in offending.
What are the implications of latency delay in analyzing network block data for anomaly detection?
5 answers
Latency delay in analyzing network block data has implications for anomaly detection. The race between adversarial and honest chains in Nakamoto consensus is analyzed to improve security-latency bounds. The service quality of data service business directly influences mobile user perception and satisfaction to the network. Evaluation and comparison of anomaly detection algorithms are hindered by lack of publicly available implementations and annotated data sets. Anomaly detection among multiple stochastic processes is addressed by finding a sequential inference strategy that minimizes expected cumulative cost under error constraints. Anomaly detection in real-time systems is achieved through a supervised machine learning model that processes unbounded streams of data into time series.
How do QoS metrics in SDN compare to QoS metrics in traditional networks?
5 answers
QoS metrics in SDN and traditional networks differ in terms of their management and flexibility. SDN provides a simpler way to develop QoS provisioning mechanisms, allowing network operators to easily specify QoS levels for individual data flows. The SDN model also enables continuous monitoring of the network environment, resource allocation, and traffic prioritization, resulting in improved network performance. In contrast, traditional networks face challenges in specifying QoS requirements due to high administrative costs. Additionally, SDN offers the advantage of reducing the overhead of network-wide measurements, making it more efficient for one-way delay measurement. However, both SDN and traditional networks focus on improving QoS through load balancing algorithms and routing mechanisms. Overall, SDN provides a more flexible and efficient approach to QoS provisioning compared to traditional networks.
Doppler effect for LEO communication efficiency
5 answers
Doppler effects in LEO satellite communication can be compensated at the LEO satellites themselves, without the need for intersatellite link (ISL) communication. The significant Doppler effect caused by the interference between satellite networks and the high-speed movement of the satellite relative to the ground is an unavoidable technical problem in LEO satellite communication systems. The use of blind separation technology and orthogonal Time-Frequency space (OTFS) modulation can improve the target detection efficiency and system security in LEO satellite communication systems. Doppler in inter-satellite laser communications can degrade the performance of the optical space network, and the routing selection and system compensation need to be considered. In LEO satellite communication systems, the Doppler effect and non-linear distortion caused by power amplifiers can be mitigated through modulation techniques such as Gaussian Minimum Shift Keying (GMSK) and continuous phase modulation (CPM).
What is the highest speed of a user device within a 5G network?
5 answers
The highest speed of a user device within a 5G network is expected to be up to 10 Gbps.
What is the critical mass for electric vehicles?
4 answers
The critical mass for electric vehicles refers to the point at which the number of vehicles plugging into the network to charge causes congestion on the low voltage networks. This critical point depends on the choice of congestion protocol, with different protocols leading to different levels of congestion. The increasing penetration of electric vehicles, along with consumer demand for home charging, highlights the need to manage congestion on these networks. The study also shows that charging times are more equitable in proportional fairness compared to max-flow. Additionally, it is assumed that battery-electric vehicles will replace conventionally fuelled vehicles, and the weight of electric vehicles may affect non-exhaust emissions of particles. Furthermore, the impact of an increased number of electric vehicles on the residential distribution network is analyzed, with home charging leading to a drastic increase in power demand. Finally, electric vehicles can be used as energy storage units to meet high energy demands in a variable electricity tariff setting, reducing load shedding in both urban and rural areas.