Method and system for improving Wi-Fi performance in co-existing communication networks

Patent metadata

Field	Value
Publication number	US-12096248-B2
Application number	US-202217662053-A
Country	US
Kind code	B2
Filing date	May 4, 2022
Priority date	Jul 23, 2021
Publication date	Sep 17, 2024
Grant date	Sep 17, 2024

Abstract

This disclosure relates to method and system for improving Wi-Fi performance in co-existing communication networks using learning methodologies. In recent times, most of telecom operators have expressed interest in deploying LTE (Long-Term Evolution) over the unlicensed spectrum. However, simultaneous use of unlicensed band (by operators using LTE and other Wi-Fi) presents coexistence challenges in terms of network performance especially for the Wi-Fi. The disclosed techniques enable improving the Wi-Fi performance in the co-existing communication networks based on learning methodologies. The disclosed techniques improve Wi-Fi performance based on several steps that includes detecting an interfering channel, and further identifying an optimal channel to mitigate the interference caused by the detected interfering channel. The optimal channel is identified based on an optimization technique, wherein the optimization technique is a reinforcement learning technique based on a Q-learning.

First claim

What is claimed is:

1. A processor-implemented method for improving Wi-Fi performance in a co-existing communication network comprising:
sensing a plurality of inputs associated with a wireless signal in a co-existing communication network via one or more hardware processors, the plurality of inputs comprising a current transmission channel for the wireless signal, a list of all available channels in the co-existing communication network, the signal strength of the wireless signal, a throughput tolerance range (ζ), a classification energy threshold (γ), an energy detection threshold, a carrier sensitivity threshold and a target throughput (ThrTargetWiFi);
detecting an interfering channel for the wireless signal, via the one or more hardware processors, based on the classification energy threshold (γ), the energy detection threshold and the carrier sensitivity threshold, wherein the interfering channel causes an interference in the performance of the wireless signal;
classifying the interfering channel, via the one or more hardware processors, based on the classification energy threshold (γ), the energy detection threshold and the carrier sensitivity threshold, wherein the classification includes a high interference channel, a moderate interference channel and a low interference channel;
assigning a channel state to the classified interfering channel, via the one or more hardware processors, wherein the channel state includes either a busy channel state or an idle channel state;
identifying an optimal channel based on the channel state, via the one or more hardware processors, using an optimization technique, wherein the optimal channel is identified for the channel state that is assigned the busy channel state; and
switching transmission of the wireless signal from the current transmission channel to the optimal channel via the one or more hardware processors, using a frequency hopping technique, wherein the optimal channel enables improved Wi-Fi performance in the co-existing communication network by offering interference free transmission in the co-existing communication network.

2. The method of claim 1 , wherein the co-existing communication network comprises a telecom communication network deployed with at least one Wi-Fi network and a Long-Term Evolution Unlicensed (LTE-U) based 3GPP Communication in an unlicensed spectrum comprising an LTE-U eNB serving one or more LTE User Equipment nodes (UE) over unlicensed spectrum co-existing with Wi-Fi network consisting of one a single or more Access Point(s) (AP) serving one or more users (STAs).

3. The method of claim 1 , wherein the energy detection threshold and the carrier sensitivity threshold are standard values pre-defined by IEEE 802.11 specification and the throughput tolerance range (ζ), and the classification energy threshold (γ) and a target throughput (ThrTargetWiFi) are defined based on a user requirement.

4. The method of claim 1 , wherein the busy channel state is assigned to the high interfering channel and the moderate interfering channel, and the idle channel state-value is assigned to the low interfering channel.

5. The method of claim 1 , wherein the optimization technique is a reinforcement learning technique based on a Q-learning, that comprises:
receiving the list of all available channels in the co-existing communication network, the signal strength of the wireless signal, the channel state, the target throughput, and the throughput tolerance range (ζ);
initializing a Q-value table by identifying one or more states and an action (a) from an action set a i (t), wherein the state for the wireless signal is defined based on interaction of the wireless signal with the co-existing communication network and the action is selection of the best available channel that offers interference free transmission in the co-existing communication network;
designing a reward (r) for a current state of the one or more states based on a next state (s+1) and the action (a); and
selecting the optimal channel by solving an optimization problem defined based on the target throughput, an instantaneous action throughput and the throughput tolerance range (ζ), wherein the instantaneous action throughput is achieved after performing the action (a).

6. The method of claim 1 , wherein the frequency hopping technique includes an interference mitigation strategy which comprises interference-aware channel assignment.

7. A system comprising:
an input/output interface;
one or more memories; and
one or more hardware processors, the one or more memories being coupled to the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the one or more memories, to:
sense a plurality of inputs associated with a wireless signal in a co-existing communication network, wherein the plurality of inputs comprise a current transmission channel for the wireless signal, a list of all available channels in the co-existing communication network, the signal strength of the wireless signal, a throughput tolerance range (ζ), a classification energy threshold (γ), an energy detection threshold, a carrier sensitivity threshold and a target throughput (ThrTargetWiFi);
detect an interfering channel for the wireless signal based on the classification energy threshold (γ), the energy detection threshold and the carrier sensitivity threshold, wherein the interfering channel causes an interference in the performance of the wireless signal;
classify the interfering channel based on the classification energy threshold (γ), the energy detection threshold and the carrier sensitivity threshold, wherein the classification includes a high interference channel, a moderate interference channel and a low interference channel;
assign a channel state to the classified interfering channel, wherein the channel state includes either a busy channel state or an idle channel state;
identify an optimal channel based on the channel state using an optimization technique, wherein the optimal channel is identified for the channel state that includes one of the busy channel and the idle channel state; and
switch transmission of the wireless signal from the current transmission channel to the optimal channel via the one or more hardware processors, using a frequency hopping technique, wherein the optimal channel enables improved Wi-Fi performance in the co-existing communication network by offering interference free transmission in the co-existing communication network.

8. The system of claim 7 , wherein the one or more hardware processors are configured by the instructions to assign the busy channel state to the high interfering channel and the moderate interfering channel, and assign the idle channel state value to the low interfering channel.

9. The system of claim 7 , wherein the one or more hardware processors are configured by the instructions to perform the optimization technique, wherein the optimization technique is a reinforcement learning technique based on a Q-learning that comprises:
receiving the list of all available channels based on the co-existing communication network, the signal strength of the wireless signal, the target throughput, the channel state, and the throughput tolerance range (ζ);
initializing a Q-value table by identifying one or more states and an action (a) from an action set a i (t), wherein the state for the wireless signal is defined based on interaction of the wireless signal with the co-existing communication network and the action is selection of the best available channel that offers interference free transmission in the co-existing communication network;
designing a reward (r) for a current state of the one or more states b

Assignees

• Tata Consultancy Services Ltd

Inventors

• Mishra Garima
• Trivedi Nikita
• Rath Hemant Kumar
• Panigrahi Bighnaraj
• Nadaf Shameemraj

Classifications

• H04W84/12

  WLAN [Wireless Local Area Networks] · CPC title

• H04W16/14

  Spectrum sharing arrangements {between different networks} · CPC title

• H04B1/715

  Interference-related aspects · CPC title

• H04W72/541

  using the level of interference · CPC title

• H04W24/02 Primary

  Arrangements for optimising operational condition · CPC title