Abstract
With an increasing demand of mobile data traffic in fifth-generation (5G) wireless communication systems, new radio-unlicensed (NR-U) technology has been regarded as a promising technology to address the exponential growth of data traffic by offloading the traffic to unlicensed bands. Nevertheless, how to efficiently share the unlicensed spectrum resource among the NR and Wi-Fi systems is a key challenge to be addressed, especially in a dynamic network environment. In this article, we investigate a distributed channel access mechanism and focus on the channel selection for NR-U users to decide the optimal unlicensed channel for uplink traffic offloading. We formulate the selection problem as a non-cooperative game, which is proven to be an exact potential game. However, the Nash equilibrium (NE) point is hard to achieve, due to the unknown dynamic environment. Based on multi-armed bandit learning techniques, an online learning distributed channel selection algorithm (OLDCSA) is proposed and proven to have similar performance to the NE point. Finally, simulation results reveal that our proposed algorithm outperforms the existing random selection by 16.45% on average and is close to the exhaustive search in the dynamic unknown environment.
Highlights
With the continuous rapid growth of the mobile Internet and the Internet of Things, the huge traffic volumes pose a major challenge to network capacity [1], which has motivated the innovation to improve spectrum utilization by offloading traffic to unlicensed bands
We present a new user decision channel access mechanism for uplink traffic offloading in a dynamic heterogeneous networks and formulate a new optimization problem to maximize the sum-rate of New Radio (NR) Unlicensed (NR-U) users
With the help of adaptive modulation and coding (ACM), the channel transmission rate can be classified into several states according to the received average signal-to-noise ratio (SNR)
Summary
With the continuous rapid growth of the mobile Internet and the Internet of Things, the huge traffic volumes pose a major challenge to network capacity [1], which has motivated the innovation to improve spectrum utilization by offloading traffic to unlicensed bands. The authors in [23] jointly considered channel selection, carrier aggregation and fractional spectrum access for unlicensed LTE (U-LTE) networks in a centralized management and the work [24] exploited deep learning based on long short-term memory (LSTM) in resource allocation. We present a new user decision channel access mechanism for uplink traffic offloading in a dynamic heterogeneous networks and formulate a new optimization problem to maximize the sum-rate of NR-U users. The design objective of the coexistence system is to maximize the long-term sum rates of the NUEs. As typically done in practical systems, we divide the time domain into mini-slots, referred to as ‘‘Decision Mini-slot (DMS)’’ to perform channel selection and data transmission. We suppose that the network and channel condition are unchanged within a DMS
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