Abstract: The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by a user equipment (UE) in a communication system is provided. The method includes receiving, from a base station via higher layer signaling, a configuration for a channel state information (CSI) report, receiving, from the base station, a monitoring measurement resource, generating the CSI report based on the monitoring measurement resource; and transmitting, to the base station, the CSI report including a monitoring outcome report.

Abstract: In an embodiment, a method of a base station (BS) for managing an ISI in a cellular network is disclosed. The method includes receiving at least one UE-Capability information element from a UE comprising a list of CP lengths and a list of SCSs, determining a plurality of parameters associated with the UE based on the list of CP lengths and the list of SCSs, calculating at least one of a first custom CP length and a first SCS based on the plurality of parameters from the list of CP lengths and the list of SCSs, and transmitting, to the UE, a response message indicating that at least one of the first custom CP length and the first SCS is selected for managing the ISI.

Abstract: Disclosed is a method performed by a user equipment (UE) for Artificial Intelligence (AI) based Channel State Information (CSI) encoding. The method comprises receiving, a signaling message including at least one encoding parameter from a Base Station (BS), wherein the encoding parameter comprises an AI model. The method comprises segmenting a precoder data based on the signaling message into a plurality of data segments. The method comprises selecting at least one AI model from a plurality of AI models based on the at least one encoding parameter received from the BS. The method comprises encoding each data segment of the plurality of data segments of the precoder data using the at least one selected AI model. The method comprises sending at least one encoded data segment of the precoder data to the BS for decoding.

Abstract: The disclosure relates to a 5th generation (5G) or 6th generation (6G) communication system for supporting a higher data transmission rate. A method and a Base Station (BS) for determining an optimal equalizer for managing interference in a communication network are provided. The method includes estimating channel coefficients of each slot of a plurality of slots based on received Demodulation Reference Signal (DM-RS) symbols, determining a covariance of interference-and-noise (Rz) matrix for at least one Resource Block (RB) of a plurality of RBs of each slot based on the channel coefficients, determining a noise variance (?2) based on noise measurements performed for one or more sub-carriers without the interference, and determining an optimal equalizer from a plurality of equalizers for managing the interference, based on diagonal elements of the Rz matrix and ?2 of the at least one RB using a machine learning model.

Abstract: A pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). Methods and systems for adapting beamwidth of beams on NR physical channels are provided. Alignment between beams of a UE and a gNB is created by refining beam codebooks. Phase shifters and PAs/LNAs of antenna elements are tuned for refining the beam codebooks. Strength of a signal, received through different RX beams, is determined based on RSRP/SINR associated with the different RX beams. A direction is determined, along which RSRPs/SINRs associated with consecutive RX beams is increasing. A pair of RX beams is determined, the RSRP/SINR associated with a first beam being greater than e RSRP/SINR associated with a second beam, and the RSRP/SINR associated with the first beam is the greatest along the determined direction.

Abstract: A method is provided. The method includes receiving, by a User Equipment (UE), a plurality of transmit (Tx) beams from a Base Station (BS), on a plurality of receive (Rx) beams. The method includes determining the beam parameters associated with a plurality of Tx and Rx beam pairs changing at a frequency above a first threshold. The method includes deriving a beam selection metric for the plurality of Tx and Rx beam pairs using the associated beam parameters. The method includes identifying a subset of Tx and Rx beam pairs from the plurality of Tx and Rx beam pairs with the beam selection metric above a second threshold. The method includes prioritizing scanning of the identified subset of Tx and Rx beam pairs to select a Tx and Rx beam pair for communication.

Abstract: Presented herein is a pre-5th-Generation (5G) or 5G communication system that supports higher data rates beyond 4th-Generation (4G) communication systems. In particular, methods and systems for performing cell search in a millimeter wave (mmWave) based communication network are presented. A method disclosed herein includes selecting a subset of receive (Rx) beams from a plurality of Rx beams and scheduling a scan order for the selected subset of Rx beams, upon receiving a plurality of signals from a Base Station. The method also includes performing a cell search using the selected subset of Rx beams individually in the determined scan order. The method further includes combining two or more of the selected Rx beams, upon failing the cell search using the selected subset of Rx beams individually. The method further includes performing the cell search using the combined Rx beams.

Abstract: A method for performing a channel state information (CSI) prediction by a user equipment (UE) is provided. The method includes receiving a plurality of reference signals from a base station, obtaining a channel quality information (CQI) estimation for an interval based on the received plurality of reference signals, wherein obtaining the CQI estimation includes obtaining at least one of mean mutual information per bit (MMIB) or effective exponential signal to noise ratio mapping (EESM), predicting the CSI based on the CQI estimation, and reporting the predicted CSI to the base station.

Abstract: Methods and systems for tracking frequency offset in NR are provided. A user equipment (UE) can compute the frequency offset comprising of crystal frequency drift and Doppler shift. Drift in frequencies generated by crystal oscillators in the UE and a base station are detected and nullified. Doppler shift of a serving beam is estimated using either data collected by sensors in the UE or reference signals received from the base station. Values of Doppler shift for a plurality of beams are estimated using the Doppler shift of the serving beam and sensor data, wherein the serving beam and the plurality of beams correspond to a same transmitter beam or different transmitter beams, wherein the type of QCL of the beams is either A, B, or C.

Abstract: Embodiments herein disclose a method for selecting a pilot pattern for an optimal channel estimation in a wireless network by a UE. The method includes: receiving a specified pilot pattern from a base station (BS); determining at least one channel parameter from the received specified pilot pattern, wherein the at least one channel parameter comprises a delay, a Doppler, a Demodulation Reference Signal (DMRS), a sounding reference signal (SRS), and a signal to noise ratio (SNR); estimating a minimum number of pilots required using the at least one determined channel parameter; and determining an optimal pilot pattern using a channel coefficient and the estimated minimum number of pilots, wherein the optimal pilot pattern is used for the optimal channel estimation.

Abstract: Methods and systems for reporting CSI and selecting optimal beams using ML. The CSI report is sent to a gNB, which includes feedback parameters, computed and predicted using ML. The feedback parameters are computed using measurements performed using CSI-RS. Values of the feedback parameters likely at future, based on channel variation and the measurements, are pre-dieted using ML. The computed and predicted feedback parameters are included in the CSI report. Optimal CSI-RS resource allocation and optimal CSI reporting periodicity are determined using ML and sent to the gNB. The CSI report is encoded using the ML based model. The RSRP of the beams are predicted using ML for beam selection.

Abstract: A method of resource selection by a user equipment (UE) is provided. The method includes detecting a need of an uplink transmission having a plurality of transmission parameters for at least one application from a plurality of applications having an associated transmission requirement. The method includes determining a set of configured grant resources available to the UE along with the associated plurality of grant resource parameters. The method includes determining transmission capability for the set of configured grant resources by analyzing the grant resource parameters. The method includes selecting at least one configured grant resource from the set of configured grant resources satisfying at least one transmission requirement of the at least one application.

Abstract: The disclosure relates to a method and system for adaptive antenna configuration in a user equipment comprising one or more antenna modules, each of the one or more antenna modules comprising a plurality of patch elements. The method comprises: determining whether a signal strength of the UE is above a signal threshold, determining whether a mobility of the UE is below a mobility threshold based on determining that the signal strength is above the signal threshold, determining at least one active antenna module based on determining that the mobility of the UE is below the mobility threshold, and deactivating at least one patch element in the at least one antenna module.

Abstract: Embodiments herein provide a method for channel state information (CSI) prediction by a user equipment (UE) in a wireless network. The method comprises determining a CSI; inputting the determined CSI to at least one machine learning (ML) based CSI prediction model to obtain at least one predicted precoder; encoding the at least one predicted precoder into at least one bit stream using at least one ML based CSI encoding model or at least one non-ML based CSI encoding model; and transmitting the at least one encoded bit stream to a network apparatus in the wireless network for the ML based CSI prediction at the network apparatus.

Abstract: A method of beam failure recovery managed by a transmitter of a communication system, includes: detecting a first occurrence of a beam failure between the transmitter and a receiver; based on the detection of the first occurrence of the beam failure, identifying at least one first reconfigurable intelligent surface (RIS) for transmitting at least one reference signal; transmitting the at least one reference signal to the at least one first RIS; receiving, from the at least one first RIS, a receiver feedback for the at least one of reference signal; generating an RIS candidate beam list based on the receiver feedback; and transmitting, to the receiver via the at least one first RIS, a radio resource control (RRC) message including the RIS candidate beam list as a beam failure recovery configuration.

Abstract: A method for managing beam alignment by a user equipment is provided. The method comprises receiving beam width information and base station location information from a base station, calculating a displacement of location of the UE based on location coordinates of the UE, measuring a change in a signal strength of the UE due to the displacement, and determining whether the UE is moving towards or away from the base station using at least one of the beam width information, base station location information, the displacement, and the measured change in the signal strength, determining whether a beam re-alignment is required based on the determination of the movement of UE, and transmitting a realignment request to the base station for a re-aligned beam width and a new transmission beam based on determining that the beam re-alignment is required.

Abstract: Embodiments herein provide a method for predicting iterations for decoding an encoded data at an electronic device. The method includes: receiving, by the electronic device, the encoded data; detecting, by the electronic device, signal parameters associated with the encoded data; predicting, by the electronic device, one of a cyclic redundancy check (CRC) failure, CRC success, and a CRC uncertainty in iterations for decoding the encoded data based on the signal parameters using a Neural Network (NN) model.

Abstract: Disclosed is a method and device for beam training for an IRS assisted cellular system. The method performed by BS in the IRS assisted cellular system includes configuring the IRS in a retro-reflection mode to reflect a received beam in same direction of reception of the beam and determining a BS optimal Tx-Rx beam pair for a BS-IRS link. The method includes: configuring the IRS to receive a beam from the UE; configuring the IRS to determine an IRS optimal Tx-Rx beam pair for an IRS-UE link and receiving signalling information from the UE indicating the IRS optimal Tx-Rx beam pair; and transmitting to the UE, a beam through the BS optimal Tx-Rx beam pair which is received at the UE through the UE optimal Tx-Rx beam pair, wherein the IRS reflects the beam transmitted by the BS to the UE through the IRS optimal Tx-Rx beam pair.

Abstract: A method of distributing throughput intelligently amongst a plurality of applications residing at a User Equipment (UE) is provided. The method includes receiving, at the UE, recommended bit rate (RBR) information from a network node, the RBR information indicating a throughput value allocated to the UE, allocating a codec rate from the allocated throughput value to at least one voice over internet protocol (VoIP) application from the plurality of applications, and allocating, from remaining throughput value of the allocated throughput value, a bit rate to each of a plurality of non-VoIP applications from the plurality of applications, based on corresponding throughput requirement associated with the plurality of non-VoIP applications.

Abstract: Embodiments herein disclose a method for controlling a non-linear effect of a power amplifier by an apparatus. The method includes acquiring an input data of the power amplifier of the apparatus and an output data of the power amplifier. Further, the method includes determining an inverse function using a neural network. The inverse function maps normalized output data of the PA to the input data of the PA, where the neural network comprises at least one sub-network for at least one memory tap from a plurality of memory taps in the neural network. Further, the method includes modifying the input data based on the determined inverse function value by dynamically changing a usage of the at least one memory tap from the plurality of memory taps. Further, the method includes compensating the non-linear effect in the output data of the power amplifier.