Handling Coexistence of LPWAN with Other Networks
Abstract
With its capability to enable low-power (milliwatts) wireless communication at low data rates (kbps) over long distances (kilometers), the Low-Power Wide-Area Network (LPWAN) technology represents the next frontier of communication in various military applications (e.g., enemy tracking, battlefield monitoring, and surveillance systems) that can extend over large areas. However, the rapid growth of LPWANs in the limited spectrum brings forth the challenge of coexistence of many networks and devices in the same band. The immediate effect of such coexistence is degraded network performance in terms of throughput, latency, and energy. Some networks or devices may even suffer from spectrum starvation. Repeated attempts to access the spectrum will drain their batteries. In Naval applications, wireless monitoring operations can be severely disrupted if coexistence is not handled properly. Today, LPWANs are not equipped to handle the impending challenge of coexistence. Their nodes have verylow computation power, memory, and energy typically supplied from small batteries, making it difficult to employ sophisticated protocols. Coexistence handling for WiFi or traditional short-range wireless sensor network will not work for LPWANs. In massive crowds of coexisting networks, the interference pattern can be hard to detect for LPWAN nodes. Due to long range, they are subject to an unprecedented number of hidden nodes, requiring new techniques for combating such interference. In this project, we specifically address the coexistence problem for SNOW (Sensor Network OverWhite spaces) so that it can operate in the presence of many other coexisting networks/devices. To avoid the cost of the licensed band and the crowd of the limited ISM band, we designed SNOW as an LPWAN architecture to support scalable wide-area Internet-of-Things applications over the widely available TV white spaces (unused TV band). Our preliminary work showed advantages of SNOW over other LPWANs in scalability and energy. It allows a base station (BS) to receive concurrent transmissions made by the nodes asynchronously. It also allows concurrent downlink communication. This is done through splitting a wide spectrum into many narrowband orthogonalsubcarriers enabling parallel data streams to/from numerous distributed nodes from/to the BS. Following are the key contributionswe shall make through this project. (1) First, we propose the design of a novel embedded learning agent at SNOW nodes based on a lightweight Reinforcement Learning (RL) to improve the performance of a SNOW under coexistence with many independent networks. An RL agent (i.e., a node) is effective in an unknown environment where it learns through its experience. We shall specifically adopt Q-learning which is well-suited for coexistence handling at the SNOW nodes as it entails relatively low computation requirement comparedto other machine learning approaches. (2) To handle a more severe scenario of interference such as jamming, we propose a game-theoretic approach for frequency hopping at the BS. The uniqueness of this approach lies in designing the players and their actions with respect to the LPWAN characteristics as well as being comprehensive and flexible enough to be applicable for various jamming scenarios. We propose to play a static game but repeatedly with belief update and/or reinforcement learning to find the Nash equilibrium(s), thus identifying the effective strategies against jamming. (3) We also propose to integrate the orthogonal variable spreading factor technology in SNOW to facilitate the coexistence of multiple SNOWs. Furthermore, we propose an adaptive subcarrier bandwidth approach that enables the nodes to dynamically adjust their bandwidths and select some interference-free part of their subcarriers for communication under persistent interference. We will implement the proposed ideas on SNOW hardware and evaluate through experiment under various coexistence scenarios.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 12, 2023
- Source ID
- N000142312151
Entities
People
- Abusayeed Saifullah
Organizations
- Office of Naval Research
- United States Navy
- Wayne State University