The mmWave WiGig frequency band can support high throughput and low latency emerging applications. In this context, accurate prediction of channel gain enables seamless connectivity with user mobility via proactive handover and beamforming. Machine learning techniques have been widely adopted in literature for mmWave channel prediction. However, the existing techniques assume that the indoor mmWave channel follows a stationary stochastic process. This paper demonstrates that indoor WiGig mmWave channels are non-stationary where the channel’s cumulative distribution function (CDF) changes with the user’s spatio-temporal mobility. Specifically, we show significant differences in the empirical CDF of the channel gain based on the user’s mobility stage, namely, room entering, wandering, and exiting. Thus, the dynamic WiGig mmWave indoor channel suffers from concept drift that impedes the generalization ability of deep learning-based channel prediction models. Our results demonstrate that a state-of-the-art deep learning channel prediction model based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) recurrent neural network suffers from a deterioration in the prediction accuracy by 11–68% depending on the user’s mobility stage and the model’s training. To mitigate the negative effect of concept drift and improve the generalization ability of the channel prediction model, we develop a robust deep learning model based on an ensemble strategy. Our results show that the weight average ensemble-based model maintains a stable prediction that keeps the performance deterioration below 4%.

Robust Deep Learning-based Indoor mmWave Channel Prediction Under Concept Drift

QoS-Aware User Selection and Resource Assignment for Coexistence of NR-U and Wi-Fi-Enabled IoT Networks

The 5G New Radio-based in unlicensed spectrum (NR-U) has been proposed to harmoniously coexist with the Wireless Gigabyte (WiGig) network in the 60 GHz unlicensed spectrum. It employs the directional listen-before-talk (dirLBT) mechanism to improve the throughput of the coexisting NR-U and WiGig networks (CNWNs). In this paper, we are the first to leverage the stochastic geometry to analyze the performance of the large-scale CNWNs. The medium access probabilities (MAPs) of NR-U base station (NBS) and WiGig access point (WAP) are both derived in closed-form. Based on these MAPs, the downlink successful transmission probabilities (STPs) of NR-U and WiGig networks, which is determined by the retaining probability of the serving NBS/WAP and the downlink coverage probability of NR-U/WiGig network, are given in analytical expressions. All these MAPs and STPs are validated by Monte Carlo simulations to verify the correctness of our proposed model. Moreover, the effect of NBS and WAP density on the mean STP of the large-scale CNWNs are analyzed numerically. The results show that the dirLBT adopted by NR-U outperforms omnidirectional LBT in terms of the mean STP, especially in ultra-densely deployed CNWNs scenario. Furthermore, there exists an optimal NBS density to maximize the mean STP. The results indicate that the dirLBT mechanism should be adopted in the densely deployed CNWNs with proper chosen of NBS density. 

Modelling and Performance Analysis of the Coexisting NR-U and WiGig Networks

Performance analysis of user power consumption in NR-unlicensed DRX

The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios.

AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work

Deploying the 5 G new radio network on the unlicensed mmWave band (NR-U) is a promising solution to meet the constantly increasing traffic demand of the cellular network. However, the coexistence issue on the unlicensed mmWave spectrum between NR-U and Wireless Gigabit (WiGig) networks becomes a significant concern. The coexistence schemes between LTE and Wi-Fi networks cannot be readily applied to support the coexistence between NR-U and WiGig networks due to the directional beamforming and propagation characteristic of the mmWave band. The directional transmission complicates the coexistence problem but opens up possibilities for increased system capacity via concurrent multi-user MIMO (MU-MIMO) transmission at the expense of interference management. To this end, we consider an NR-U and WiGig coexisting scenario and aim to maximize the total NR-U throughput on the unlicensed spectrum via resource allocation and inter-beam interference mitigation. The optimization problem is formulated into an integer nonlinear programming (INLP) problem and is further transformed into a many-to-one matching game for tractability. We propose a matching-based concurrent algorithm (MBCA) to solve it and the inter-beam interference is mitigated via the forbidden pairs (FPs). In comparison to the exhaustive search-based algorithm (ESA), simulation results show that FP effectively mitigates inter-beam interference and achieves near-optimal performance. The proposed algorithm also outperforms the state-of-art algorithms in existing research. 

Fair and Efficiency Coexistence Between NR-U and WiGig Networks Enabled by a Matching-Based Framework With Forbidden Pairs

We propose a novel matching-based pilot assignment scheme to reduce the pilot contamination effect in cell-free massive multiple-input-multiple-output (MIMO) systems with low computational complexity. We use a many-to-many matching scheme to solve the AP selection problem so that the maximum number of UEs in a UE group equals the number of orthogonal pilots. Furthermore, orthogonal pilots are assigned to each UE group in descending order of the common serving AP ratio of UE groups. Simulation results demonstrate that the proposed algorithm achieves near-optimum performance and outperforms the existing pilot assignment schemes in terms of 95%-likely per-user throughput and computational complexity, especially in scenarios with a limited number of pilots.

A Matching-Based Pilot Assignment Algorithm for Cell-Free Massive MIMO Networks

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xiaoyong Wang

Author Tools

Chat about Author

Papers

Fair and Efficiency Coexistence Between NR-U and WiGig Networks Enabled by a Matching-Based Framework With Forbidden Pairs

A Matching-Based Pilot Assignment Algorithm for Cell-Free Massive MIMO Networks