Abstract
Millimeter wave, especially the high frequency millimeter wave near 100 GHz, is one of the key spectrum resources for the sixth generation (6G) mobile communication, which can be used for precise positioning, imaging and large capacity data transmission. Therefore, high frequency millimeter wave channel sounding is the first step to better understand 6G signal propagation. Because indoor wireless deployment is critical to 6G and different scenes classification can make future radio network optimization easy, we built a 6G indoor millimeter wave channel sounding system using just commercial instruments based on time-domain correlation method. Taking transmission and reception of a typical 93 GHz millimeter wave signal in the W-band as an example, four indoor millimeter wave communication scenes were modeled. Furthermore, we proposed a data-driven supervised machine learning method to extract fingerprint features from different scenes. Then we trained the scene classification model based on these features. Baseband data from receiver was transformed to channel Power Delay Profile (PDP), and then six fingerprint features were extracted for each scene. The decision tree, Support Vector Machine (SVM) and the optimal bagging channel scene classification algorithms were used to train machine learning model, with test accuracies of 94.3%, 86.4% and 96.5% respectively. The results show that the channel fingerprint classification model trained by machine learning method is effective. This method can be used in 6G channel sounding and scene classification to THz in the future.
Highlights
With limited spectrum resources for the fifth generation (5G) mobile networks, industries urgently need more millimeter wave band [1]
The main contributions of this article are summarized as follows, (1) We proposed and built the industry’s first commercial off-the-shelf (COTS) hardwarebased high-frequency indoor millimeter wave channel sounding system based on time-domain correlation method, which could measure millimeter wave signals in the W-band
This paper proposes and builds a 6G indoor millimeter wave channel sounding system based on the time-domain correlation method
Summary
With limited spectrum resources for the fifth generation (5G) mobile networks, industries urgently need more millimeter wave band [1]. High frequency millimeter wave near 100 GHz to THz is one of the key spectrum resources for the sixth generation (6G) mobile communications system [2]. The wireless signal transmission performance between base stations and mobile stations is mainly determined by wireless channel. 6G will face serious signal occlusion problems, because high-frequency millimeter wave transmits in a straight-line way. Modeling and classifying millimeter wave channel can facilitate cellular communication network design, which is premise of 6G’s actual deployment. The key to solving this problem is to sound the new high-frequency millimeter wave wireless channel, and obtain accurate channel impulse response. The classification of different wireless transmission scenes is conducive to wireless network optimization [3]
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