| CPC H04W 28/0967 (2020.05) [H04L 41/5009 (2013.01); H04L 43/16 (2013.01); H04W 28/0236 (2013.01); H04W 28/24 (2013.01)] | 4 Claims |
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1. A method of optimizing one of a 5G New Radio (NR) network or a 4G Long Term Evolution (LTE) network operation, comprising:
defining a set of observation time windows TW, w≥1, at a gNB as follows:
 and
 such that
 wherein:
TIA_max is a maximum initial access (IA) latency stipulated by a network operator, defined as
 NSS is a number of synchronization signal (SS) blocks in an SS Burst Set: TSS is a SS burst set time: Tlast is a time to transmit the SS blocks in a last SS Burst Set: TPRACH is a time taken after a user equipment (UE) detects a beam to transmit Physical Random Access Channel (PRACH) in uplink (U): SD represents a maximum number of SS blocks needed for one
transmission/reception point (TRP) deployed in a target scenario: η1, . . . , ηj, . . . ηSSBeamldk are SS beam index at the gNB: vnj is inferred UE mobility indicator in a direction of beam index ηj;
NUEConnected_Tw is a number of connected UEs at the observation time window TW: Nth is the minimum number of PRACH instance detected in a received beam direction at the gNB: NSSsGPPmax is a upper bound for a number of SS blocks in the SS Burst Set:
establishing, by an artificial intelligence (AI) engine through a learning process, a functional relationship between angular spans {Δθ, Δø}, wherein Δθ and Δø are respectively azimuth and elevation angles of the beams of both the gNB and the UE, and wherein SD is a function of Δθ and Δø;
selecting, by the AI engine through the learning process, a set of receive beam directions where detected PRACH transmissions exceed Nth:
indirectly measuring the UE mobility in a cell by measuring a rate of change and number of PRACH detections over the set of observation time window TW, thus providing a UE mobility information:
finding, by the AI engine, a functional relationship among the UE mobility information, number of connected UEs in the gNB with NSS, and number of SS blocks in the SS Burst Set, upper bounded NSSsGPPmax;
learning, by the AI engine, from observations available at the gNB, wherein a list of observations available at the gNB comprises at least one of:
a) receive beam index for PRACH and the statistics for a detected PRACH transmissions set:
b) cell-specific and beam-specific Reference Signal Received Power (RSRP) of the received PRACH signals at the gNB and statistics of SNR threshold, measurement of PRACH correction power;
c) number of received PRACH signals for a receive beam index at sample time instant; and
d) UE mobility or geolocation information:
deriving, by the AI engine, a set of values {SD, NSS} to maximize the value of TIA with restriction TIA≤TIA_max; and
wherein a training phase is further carried out to train the AI engine for the gNB, using at least one of the following inputs: the receive beam index ηj over which PRACH is received: the UE mobility information; and the number of connected UEs.
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2. The method according to claim 1, wherein the list of observations available at the gNB further comprises at least one of:
e) number of detected PRACH transmission in every UL receive beams at the gNB; and
f) RSRP reported by the UJE for any other DL reference signal transmissions.
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3. The method according to claim 1, wherein the finding of the functional relationship by the AI engine is performed using deep reinforcement learning (DRL).
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4. The method according to claim 1, further comprising:
successfully detecting the U)E by the gNB when a received signal-to-noise ratio (SNR) of the received PRACH signal achieves a specified SNR threshold and the gNB detects the PRACH signal.
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