METHOD AND APPARATUS FOR MANAGING INTERFERENCE IN A WIRELESS COMMUNICATION SYSTEM

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 (σ2) 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 σ2 of the at least one RB using a machine learning model.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. § 119(a) of an Indian Provisional patent application number 202341032188, filed on May 5, 2023, in the Indian Patent Office, and of an Indian Complete patent application number 202341032188, filed on Apr. 16, 2024, in the Indian Patent Office, the disclosure of each of which is incorporated by reference herein in its entirety.

BACKGROUND

The disclosure relates to wireless networks. More particularly, the disclosure relates to a method, an apparatus and system for determining an optimal equalizer for managing interference in a wireless communication system.

2. Description of Related Art

Considering the development of wireless communication from generation to generation, the technologies have been developed mainly for services targeting humans, such as voice calls, multimedia services, and data services. Following the commercialization of 5th generation (5G) communication systems, it is expected that the number of connected devices will exponentially grow. Increasingly, these will be connected to communication networks. Examples of connected things may include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machines, and factory equipment. Mobile devices are expected to evolve in various form-factors, such as augmented reality glasses, virtual reality headsets, and hologram devices. In order to provide various services by connecting hundreds of billions of devices and things in the 6th generation (6G) era, there have been ongoing efforts to develop improved 6G communication systems. For these reasons, 6G communication systems are referred to as beyond-5G systems.

6G communication systems, which are expected to be commercialized around 2030, will have a peak data rate of tera (1,000 giga)-level bit per second (bps) and a radio latency less than 100 μsec, and thus will be 50 times as fast as 5G communication systems and have the 1/10 radio latency thereof.

In order to accomplish such a high data rate and an ultra-low latency, it has been considered to implement 6G communication systems in a terahertz (THz) band (for example, 95 gigahertz (GHz) to 3 THz bands). It is expected that, due to severer path loss and atmospheric absorption in the terahertz bands than those in millimeter wave (mmWave) bands introduced in 5G, technologies capable of securing the signal transmission distance (that is, coverage) will become more crucial. It is necessary to develop, as major technologies for securing the coverage, Radio Frequency (RF) elements, antennas, novel waveforms having a better coverage than Orthogonal Frequency Division Multiplexing (OFDM), beamforming and massive Multiple-input Multiple-Output (MIMO), Full Dimensional MIMO (FD-MIMO), array antennas, and multiantenna transmission technologies such as large-scale antennas. In addition, there has been ongoing discussion on new technologies for improving the coverage of terahertz-band signals, such as metamaterial-based lenses and antennas, Orbital Angular Momentum (OAM), and Reconfigurable Intelligent Surface (RIS).

Moreover, in order to improve the spectral efficiency and the overall network performances, the following technologies have been developed for 6G communication systems: a full-duplex technology for enabling an uplink transmission and a downlink transmission to simultaneously use the same frequency resource at the same time, a network technology for utilizing satellites, High-Altitude Platform Stations (HAPS), and the like in an integrated manner, an improved network structure for supporting mobile base stations and the like and enabling network operation optimization and automation and the like, a dynamic spectrum sharing technology via collision avoidance based on a prediction of spectrum usage, an use of Artificial Intelligence (AI) in wireless communication for improvement of overall network operation by utilizing AI from a designing phase for developing 6G and internalizing end-to-end AI support functions, and a next-generation distributed computing technology for overcoming the limit of user equipment (UE) computing ability through reachable super-high-performance communication and computing resources (such as Mobile Edge Computing (MEC), clouds, and the like) over the network. In addition, through designing new protocols to be used in 6G communication systems, developing mechanisms for implementing a hardware-based security environment and safe use of data, and developing technologies for maintaining privacy, attempts to strengthen the connectivity between devices, optimize the network, promote softwarization of network entities, and increase the openness of wireless communications are continuing.

It is expected that research and development of 6G communication systems in hyper-connectivity, including person to machine (P2M) as well as machine to machine (M2M), will allow the next hyper-connected experience. Particularly, it is expected that services such as truly immersive eXtended Reality (XR), high-fidelity mobile hologram, and digital replica could be provided through 6G communication systems. In addition, services such as remote surgery for security and reliability enhancement, industrial automation, and emergency response will be provided through the 6G communication system such that the technologies could be applied in various fields such as industry, medical care, automobiles, and home appliances.

Fifth generation (5G) and beyond systems are required to support much heavier uplink traffic due to an increased use of data intensive applications such as social networking and point-to-point video sharing. Achieving higher spectral efficiency is therefore an important requirement in these systems. One of the key factors that limit uplink performance is presence of co-channel interference i.e., interference arising from multiple users/nodes using same frequency resources. In homogeneous networks, co-channel interference arises from neighboring cell users, that can significantly affect the achievable rate for primary cell users. In heterogeneous networks, due to the co-channel deployment of the macro base station (BS) and a large number of low power BSs, the interference is even higher. Therefore, an effective mechanism to alleviate interference at the BS is necessary.

One of the key approaches to mitigate interference in a physical (PHY) layer is via an effective equalization technique. Several uplink detection/equalization algorithms have been proposed over the last two decades for massive MIMO systems, but linear receivers still remain a preferred choice in practical systems due to their simplicity and tractability. In fact, zero-forcing (ZF) and minimum mean squared error (MMSE) equalizers are known to produce near-optimal performance under some regimes, however their performance degrade in the presence of interference.

MMSE with interference rejection combining (MMSE-IRC) has been widely adopted for suppressing the inter-cell and intra-cell interference in spatial domain, and thus achieve higher cell-edge/average spectral efficiency. Although successful in alleviating interference, the MMSE-IRC receiver suffers from a much higher complexity compared with traditional MMSE receiver. Specifically, the complexity of MMSE-IRC is cubic in number of BS antennas, whereas the complexity of MMSE is cubic in number of users. This is particularly concerning given the large number of BS antennas used in 5G and beyond systems, wherein managing the complexity of MMSE-IRC receiver within tolerable limits is one of the serious issues that industry is working on. Currently though there have been some existing systems working in this direction, these methods either require non-trivial changes in existing receiver structure or suffer from significant performance loss in realistic channels due to approximations, both of which are undesirable.

SUMMARY

Aspects of the disclosure are to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below. Accordingly, an aspect of the disclosure is to provide a method and system for determining an optimal equalizer for managing interference in a communication network.

In accordance with an aspect of the disclosure, a method performed by a base station (BS) of determining an optimal equalizer for managing interference in a wireless communication system is provided. The method includes estimating, by the BS, 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 (σ2) 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 σ2 of the at least one RB using a machine learning model (an artificial intelligence model).

In accordance with another aspect of the disclosure, a method performed by a base station (BS) of determining an optimal equalizer for managing interference in a communication network is provided. The method includes estimating, by the BS, channel coefficients of each slot of a plurality of slots with respect to time based on received demodulation reference signal (DM-RS) symbols, determining, by the BS, 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, by the BS, a noise variance (σ2) based on noise measurements performed on one or more sub-carriers without the interference, estimating, by the BS, an interference proportion for the at least one RB based on the covariance of interference-and-noise (Rz) matrix and the noise variance (σ2), and determining, by the BS, an optimal equalizer from a plurality of equalizers based on a comparison of the interference proportion with a predetermined interference threshold for the at least one RB.

In accordance with another aspect of the disclosure, a Base Station (BS) for determining an optimal equalizer for managing interference in a communication network is provided. The BS includes memory storing one or more computer programs and one or more processors communicatively coupled to the memory. The one or more computer programs include computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to estimate channel coefficients of each slot of a plurality of slots based on received Demodulation Reference Signal (DM-RS) symbols, determine 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 coefficient, determine a noise variance (σ2) based on noise measurements performed for one or more sub-carriers without the interference, and determine an optimal equalizer from a plurality of equalizers for managing the interference, based on diagonal elements of the Rz matrix and σ2 of the at least one RB using a machine learning model (an artificial intelligence model).

In accordance with another aspect of the disclosure, one or more non-transitory computer-readable storage media storing one or more computer programs including computer-executable instructions that, when executed by one or more processors of a base station (BS), cause the BS to perform operations are provided. The operations include estimating, by the BS, channel coefficients of each slot of a plurality of slots based on received demodulation reference signal (DM-RS) symbols, determining, by the BS, 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, by the BS, a noise variance (σ2) based on noise measurements performed for one or more sub-carriers without the interference, and determining, by the BS, an optimal equalizer from a plurality of equalizers for managing the interference, based on diagonal elements of the Rz matrix and σ2 of the at least one RB using a machine learning model (an artificial intelligence model).

DETAILED DESCRIPTION

Generally, 5G and beyond systems employ a large number of antennas at a Base Station (BS) to achieve high spectral efficiency. Combining signals from large number of antennas significantly increases equalization complexity. These systems are also required to alleviate co-channel interference arising from ever increasing number of wireless devices in a network. Thus, limiting the equalization complexity within tolerable limits, while also effectively alleviating co-channel interference is an important challenge in current systems. Existing systems accept MMSE-IRC as a preferred equalizer in the face of interference and try to approximate/simplify an inverse calculation to reduce the complexity. To do so, existing methods either require non-trivial changes in architecture of receivers or have inferior performance due to inaccurate approximations.

Thus, the disclosure provides a method and a Base Station (BS) for determining an optimal equalizer for managing interference in a communication network. In Multiple-Input and Multiple-Output systems (MIMO), identifying an optimal equalizer plays a crucial role for managing co-channel interferences due to multiple transmissions. The disclosure focuses on adaptive switching between Minimum Mean Squared Error (MMSE) and MMSE-Interference Rejection Combiner (IRC) Receivers for 5G and Beyond systems.

The disclosure realizes that conventional MMSE equalizer achieves either the same or even better performance compared with MMSE-IRC under low-to-moderate interference proportions, while also having much lesser complexity than MMSE-IRC. Based on this observation, the disclosure provides adaptively switching between various equalizers such as, MMSE and MMSE-IRC, based on interference conditions which can reduce equalization complexity of the BS while also improving the performance. The disclosure provides an Artificial Intelligence (AI) based solution to achieve adaptive switching and is shown to reduce equalization complexity for example, up to 66%, while also improving performance compared with MMSE-IRC. Further, the disclosure also discloses determining the optimal equalizer based on a threshold based technique.

Thus, the disclosure provides a solution to an important bottleneck in the 5G and beyond systems. Specifically, the disclosure provides an AI-based methods for significantly reducing the equalization complexity in 5G and beyond BSs, while also improving the equalization performance in the presence of co-channel interference. An embodiment of the disclosure saves significant computational resources and power. The disclosure is also extended to the open radio access network (O-RAN) architecture. An embodiment of the disclosure enables to provide reliable communication to its users even under dense networks with co-channel interference.

Various embodiments disclosed herein relate to 5G and beyond systems, and more particularly to performing adaptive switching between MMSE and MMSE-IRC Receivers in the Uplink for 5G and Beyond.

The fifth generation (5G) and beyond systems are required to support much heavier uplink traffic due to the increased use of data intensive applications such as social networking and point-to-point video sharing. Achieving higher spectral efficiency is therefore an important requirement in these systems. One of the key factors that limit the uplink performance is the presence of co-channel interference. In homogeneous networks with frequency reuse factor one, inter-cell interference can significantly affect the achievable rate. In heterogeneous networks, due to the co-channel deployment of the macro base station (BS) and a large number of low power BSs, the interference is even higher. Therefore, an effective mechanism to alleviate interference at the BS is necessary.

One of the key approaches to mitigate interference in the physical layer is via an effective equalization technique. Several uplink detection/equalization algorithms have been proposed over the last two decades for massive MIMO systems, but linear receivers still remain the preferred choice in practical systems due to their simplicity and tractability. In fact, zero-forcing (ZF) and minimum mean squared error (MMSE) equalizers are known to produce near-optimal performance under some regimes, however their performance degrade in the presence of interference. MMSE with interference rejection combining (MMSE-IRC) has been widely adopted for suppressing the inter-cell and intra-cell interference in the spatial domain, and thus achieve higher cell-edge/average spectral efficiency.

Although successful in alleviating interference, the MMSE-IRC receiver suffers from a much higher complexity compared with traditional MMSE receiver. Specifically, the complexity of MMSE-IRC is cubic in number of BS antennas, whereas the complexity of MMSE is cubic in number of users. This is particularly concerning given the large number of BS antennas used in 5G and beyond systems, wherein managing the complexity of MMSE-IRC receiver within tolerable limits is one of the serious issues that industry is working on. While there have been some prior works in this direction, the proposed methods either require non-trivial changes in the existing receiver structure, or suffer from significant performance loss in realistic channels due to approximations, both of which are undesirable.

The principal object of the embodiments herein is to disclose methods and systems for adaptive switching between MMSE and MMSE-IRC Receivers in the Uplink for 5G and Beyond.

Another object of the embodiments herein is to disclose an opportunistic receiver that switches to MMSE under favorable conditions, such that it achieves both uniformly superior performance as well as reduced average complexity compared with MMSE-IRC.

The embodiments herein achieve methods and systems for adaptive switching between MMSE and MMSE-IRC Receivers in the Uplink for 5G and Beyond. Referring now to the drawings, and more particularly toFIGS.1Ato IC,2,3A,3B,4A,4B,5A, and5B, where similar reference characters denote corresponding features consistently throughout the figures, there are shown at least one embodiment.

Embodiments herein disclose methods and systems for adaptive switching between MMSE and MMSE-IRC Receivers in the Uplink for 5G and Beyond. Embodiments herein disclose an opportunistic receiver that switches to MMSE under favorable conditions, such that it achieves both uniformly superior performance as well as reduced average complexity compared with MMSE-IRC.

Consider an uplink scenario with K single antenna users communicating with M antenna BS. The data transmission takes place in time-units called transmission time interval (TTI), with the basic TTI length being one slot made up of 14 OFDM symbols. The basic time-frequency unit that carries modulation symbols is called a resource element (RE), and a group of 12 consecutive REs in frequency domain form a resource block (RB). Embodiments herein consider the first and the eleventh OFDM symbols of a slot to contain pilot signals, which are referred in NR standards as demodulation reference signals (DMRS).

Let dj(k, l) denote the frequency domain symbol transmit-ted by user j on sub-carrier k of the OFDM symbol l, and hj (k, l) be the corresponding M×1 channel from user j to BS antennas. Let NI denote the number of interferes, with the signal from interferer i denoted by Ii(k, l)∈CM×1. The received signal on sub-carrier k of symbol l at the BS is given by

y⁡(k,ℓ)=∑j=1Khj(k,ℓ)⁢dj(k,ℓ)+∑i-1NIIi(k,ℓ)+n⁡(k,ℓ),(1)where n(k, l) is the M×1 additive white Gaussian noise vector with zero mean and variance σ2. Let

H⁡(k,ℓ)=[h1(k,l)⁢…⁢hK(k,ℓ)]denote the multiuser channel matrix,

d⁡(k,ℓ)=[(d1(k,ℓ)⁢…⁢dK(k,ℓ)]Tdenote the vector of transmitted symbols from all the users, and

I⁡(k,ℓ)=∑i=1NIIi(k,ℓ)denotes the overall interference term. Then, the system model in Equation 1 is equivalent to

In MMSE equalization, the term I(k, l)+n(k, l) is treated as effective noise at the receiver and the equalization is carried using the following Equation:

d^(k,ℓ)=(H⁡(k,ℓ)H⁢H⁡(k,ℓ)+σ~2⁢IK)-1⁢HH(k,ℓ)⁢y⁡(k,ℓ),(3)where {tilde over (σ)}2is the variance of the effective noise. Let Rz denote the covariance of I(k, l)+n(k, l). Then, the MMSE-IRC equalization is given by:

Due to the Rz term in Equation 4, the interference is effectively suppressed by MMSE-IRC equalizer, leading to superior performance. On the other hand, while MMSE equalization requires computing inverse of K×K matrix, MMSE-IRC requires inverting M×M matrix. Typically, K<M (e.g., K=12 and M=64), and hence the complexity of MMSE-IRC is significantly higher compared with MMSE.

Although MMSE-IRC theoretically achieves superior error performance compared with MMSE, its success in practice requires accurate estimation of the covariance matrix Rz of interference plus noise. The covariance matrix is estimated in practical systems using the DMRS symbols, and hence the accuracy of estimation is limited by the number of available DMRS symbols. Embodiments herein estimate Rz on a per RB basis in a given slot and use the same covariance matrix for equalization in all the REs of the RB in that slot. Embodiments herein present the performance comparison between MMSE and MMSE-IRC under the realistic simulation setting shown in Table 1, and draw some important insights.

Embodiments herein consider two interferers whose interference is modeled following the third generation partnership project (3GPP) reference document, which defines a metric called the dominant interferer proportion (DIP) ratio. DIP of an interferer i is defined as the ratio of the power of interferer i over the total power of all interferers along with the white noise. In Table 1, the DIP values are shown in the dB scale. The interference proportion, denoted by η, is the sum of the DIP values of all interferers after conversion to a linear scale. It gives the ratio of total interference power to interference plus noise power. By definition, η lies between 0 and 1. If η is close to 1, the proportion of interference is much higher than noise, and hence it is an interference dominant regime. On the other hand, if η is close to 0, the proportion of interference is insignificant compared with noise, and hence it is a noise dominant regime. In the simulations, three regimes are considered, corresponding to low, moderate, and high interference proportions whose DIP values are shown in Table 1.

The performance is studied under tapped delay line (TDL) C and D channel models. In TDL-C model, the channel contains 24 taps, all of which are distributed Rayleigh with specified delays and powers. In TDL-D model, the channel contains 13 taps, wherein the first tap follows Rician distribution (with LOS and NLOS paths) and the remaining taps are distributed Rayleigh.

Regarding bit error rate (BER) performance of MMSE and MMSE-IRC receivers in TDL-C channel under low, moderate, and high interference regimes under low interference, the performance of MMSE is the same as that of MMSE-IRC. In moderate interference, there is a small performance gain by using MMSE-IRC. Finally, in high interference regime, the performance of MMSE-IRC is significantly better than MMSE. These observations can be explained as follows. For interference proportion of 0.2053, the degradation in performance of MMSE is insignificant as this is a noise dominant scenario with less interference. The performance of MMSE-IRC is however limited by the accuracy of the estimated covariance matrix Rz, which is caused by the limited number of available pilots for its estimation (12 pilots in the present setup using two instances of Type-1 pilots in a slot with per RB estimation), as well as the higher noise level. On the other hand, at moderate to high interference proportions, even though the performance of MMSE-IRC is degraded due to inaccurate covariance estimation when compared with using ideal/perfect Rz8, the MMSE performance is significantly degraded due to higher interference, making MMSE-IRC more favorable in this regime.

In a performance comparison between MMSE and MMSE-IRC in TDL-D channels, the MMSE performs either better than or almost the same as that of MMSE-IRC in low-to-moderate interference regimes. The performance of MMSE-IRC is superior in high interference regime.

As discussed in the previous section, MMSE is almost as good as MMSE-IRC under low interference regime in TDL-C channels, and under low-to-moderate interference regimes in TDL-D channels. The complexity of MMSE is however much less than MMSE-IRC since MMSE requires inverting a 12×12 matrix, whereas MMSE-IRC requires inverting a 64×64 matrix. Therefore, instead of using MMSE-IRC in all regimes, the BS receiver can opportunistically switch to MMSE under favorable conditions. This will reduce the average complexity compared with MMSE-IRC without compromising in performance.

For realistic simulations, embodiments herein average the performance and complexity over 1000 realizations of randomly generated DIPs, with the DIPs of interferer 1 and 2 distributed uniformly in the range [−1 dB, −16 dB] such that η≤1.

The opportunistic receiver in TDL-C channel under randomized interferer DIPs according to various embodiments of the disclosure, achieves the best performance on an average, which is due to the fact that it selects the best receiver for each realization of DIPs.

In terms of complexity, as shown in Table 2, the opportunistic receiver achieves about 40% reduction in complexity com-pared with MMSE-IRC.

The opportunistic receiver in TDL-D channel under randomized interferer DIPs according to various embodiments of the disclosure, as shown in Table 3, achieves 62% reduction in complexity compared with MMSE-IRC.

FIG.1Aillustrates an environment for determining an optimal equalizer for managing interference in a communication network, according to an embodiment of the disclosure.

The environment100comprises a Base Station (BS)101connected with one or more User Equipment UE103(UE1031, UE1032, . . . UE103n) in a cell102. The one or more UEs103may connect with the BS101during uplink transmission. Both, the BS101and the one or more UEs103may comprise a plurality of antennas respectively for uplink and downlink transmission. As shown, the BS101may include a plurality of antenna (antenna1051, antenna1052, . . . antenna105n, collectively referred as plurality of antenna105). Each of the one or more UEs103may include a plurality of antennas107, respectively for uplink data transmission. Examples of the one or more UEs103may include, but not limited to, any device used by a user to communicate and/or access content such as, but not limited to, mobile phones, smartphones, laptops, and the like. The BS101may be one of, a distributed base station and a centralized base station.

FIG.1Billustrates a block of a base station, according to an embodiment of the disclosure.

Referring toFIG.1B, the BS101may include, an input/output (I/O) interface109, memory111and a processor113. The I/O interface109is coupled with the processor113through which an input signal or/and an output signal is communicated. For example, the BS101may receive DMRS signals, using the I/O interface109.

Returning toFIG.1A, during data transmission, two or more transmitters (UE1031,1032, . . .103N) may initiate uplink transmission through respective antennas107which may sometime lead to improper frequency coordination leading to interference. In such situation, the BS101may manage such interferences. In an embodiment, the interference comprises at least one of, a co-channel interference and Inter-Layer Interference (ILI). The BS101may perform adaptive switching between a plurality of equalizers. The plurality of equalizers may include at least one of, a Minimum Mean Squared Error (MMSE) equalizer, MMSE with Interference Rejection Combiner (MMSE-IRC) equalizer, and a MMSE with Successive Interference Cancellation (MMSE-SIC) equalizer. In one implementation, the BS101may determine an optimal equalizer for managing the interference based on a trained model using machine learning technique. In another implementation, the BS101may determine the optimal equalizer using threshold based adaptive switching.

FIGS.3A and3Bshow flowcharts for determining an optimal equalizer for managing the interference based on a trained model using machine learning technique according to various embodiments of the disclosure.

Referring toFIG.3A, initially at operation301, during data transmission, the BS101may estimate channel coefficients of each slot of a plurality of slots based on received Demodulation Reference Signal (DM-RS) symbols. In an embodiment estimating of the channel coefficients may be performed as per any known techniques in the art. The BS101at operation302determines 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. Typically, Rzis estimated on a per-B basis for a given slot as follows:

Letdbe the number of DMRS REs per RB in a slot,

Let (d,d) be the set of RE positions in a slot where DMRS is transmitted;

Let p(k,) be the pilot transmitted in RE (k,)∈(d,d);

Then, z(k,)=y(k,)−H(k,)p(k,) is the estimate of interference-plus-noise vector;

Further, the BS101may determine a noise variance (σ2) based on noise measurements performed for one or more sub-carriers without the interference.

Generally, one slot is constituted by 14 OFDM symbol while one RB is constituted by 12 consecutive sub-carriers. That is, the slot is across time and the RB is across frequency. Typically, co-channel interference arises from cell edge users. These users may transmit in only a subset of carriers compared with what may be available with the BS101to serve its users. The remaining sub-carriers may be interference free. To determine interference free sub-carriers, the BS101may observe noise power on each sub-carrier and identify interference free subcarriers as those with least noise power.

Further, at operations303,304,305, and306, the BS101may retrieve diagonal elements of the Rzmatrix and σ2of the at least one RB. The retrieved diagonal elements of the Rzmatrix and σ2of the at least one RB are inputted to a machine learning model for determining an optimal equalizer from the plurality of equalizers for managing the interference. The machine learning model may be pretrained by generating input features comprising a plurality of training diagonal elements of Rzand σ2of each RB. The training diagonal elements are obtained based on training channel coefficients of each slot based on training dataset of DMRS symbols. The BS101may perform equalization on each slot using each of the plurality of equalizers, on the training dataset. Further, decoded bits for each of the equalizers are obtained by performing a predefined decoding technique. In an embodiment, the predefined decoding technique may be low-density parity check code (LDPC). The BS101determines numbers of error bits for each slot generated by each of the plurality of equalizers during equalization based on the respective decoded bits. Then, the BS101determines output labels for the machine learning model for each slot based on a comparison of the number of error bits corresponding to each of the plurality of equalizers with respect to each other. In an embodiment, the training may be performed such that neural network architecture may comprises M+1 input layers and one or more output layers, wherein the “M” indicates antennas at the BS. The BS101may train the machine learning model based on a correlation between interference proportion and operating Signal to Interference Noise Ratio (SINR) associated with a plurality of training diagonal elements.

In another implementation, as shown inFIG.3B, the BS101may determine the optimal equalizer using threshold based adaptive switching. Herein, as shown at operation308, the BS101may estimate the channel coefficients of each slot of a plurality of slots with respect to time based on received Demodulation Reference Signal (DM-RS) symbols. At operation309, a covariance of interference-and-noise (Rz) matrix is determined for at least one RB of the plurality of RBs of each slot based on the channel coefficients. Further, a noise variance (σ2) is determined based on noise measurements performed on one or more sub-carriers without the interference. Then, at operation310, the BS101may estimate an interference proportion for the at least one RB based on the covariance of interference-and-noise (Rz) matrix and the noise variance (σ2). Particularly, the BS101estimates the interference proportion by identifying diagonal elements from the covariance of interference-and-noise (Rz) matrix. Herein, the diagonal elements indicate interference-plus-noise power across each receiver antennas. An interference-plus-noise power is estimated based on an average of the diagonal elements. Then, an interference power is estimated based on a function of the interference-plus-noise power and the noise variance (σ2). Thereafter, the interference proportion is estimated by the BS101based on a ratio of the estimated interference power and the interference-plus-noise power.

At operation311, the BS101may determine the optimal equalizer from the plurality of equalizers based on a comparison of the interference proportion with a predetermined interference threshold for the at least one RB. In an embodiment, the predetermined interference threshold is determined and configurable based on Block Error Rate (BLER) performance measurements and predefined configurations of BS101.

The BS101may one of a distributed base station and a centralized base station. An embodiment of a distributed BS is explained inFIG.1C.

FIG.1Cillustrates an embodiment of a base station in distributed environment for determining an optimal equalizer for managing interference in a wireless communication system, according to an embodiment of the disclosure.

The BS101may use distributed architecture (for example, centralized radio access network (C-RAN), virtualized radio access network (vRAN), and O-RAN) for additional flexibility, wherein the operations of the BS101may be distributed between a Remote Radio Head (RRH)115and a Base Band Unit (BBU)117. In the distributed BS architecture, the RRH115and BBU117are connected via a fronthaul link as shown. While different RRHs are located in their respective cell sites, multiple BBUs are located in a single physical location. This reduces the maintenance cost since different cells can be serviced from same physical location by a field maintenance engineer. The RRH115performs lower-PHY operations (such as, sampling, analog-to-digital converter (ADC)) and a part of Rx signal processing (depending on functional split). While the BBU117performs computationally intensive signal processing.

In the distributed BS, the disclosure identifies whether the optimal equalizer selection is made at RRH115or the BBU117. In one embodiment, the decision on the optimal equalizer in a given RB may be taken at the RRH115in every slot. In such case, computationally intensive inverse calculations and channel estimation may be performed at BBU117.

In such case, initially, SRS from a user equipment may be received by the BBU117. Based on the received SRS, the BBU117may estimate channel estimations and transmits the same to the RRH115. Further, once a user associated with the UE transmits data on PUSCH channel, the RRH115may determine interference statistics using DMRS and channel shared by the BBU117. Then, the RRH115may perform determine the optimal equalizer from the plurality of equalizers based on the interference and noise statistics using the trained machine learning model implemented at the RRH115. For instance, for RBs where MMSE is selected as the optimal equalizer, the RRH115may computes HHy and transmits to the BBU117(Dimension=number of transmitting layers). The BBU117may then perform the computation (HHH+σ2I)−1and multiplies it to HHy to obtain MMSE estimate. For RBs where MMSE-IRC is selected, the RRH115may transmit “y” to the BBU117without combining (Dimension=#Rx antennas at RRH). In such case, the entire MMSE-IRC computation HH(HHH+{circumflex over (R)}z)−1y is performed at the BBU117.

In another implementation, the decision on the optimal equalizer in a given RB may be performed at the BBU117during the SRS slots, and the selected equalizer may be used till the next SRS arrives. All computations (such as, interference estimation, adaptive switching, channel estimation, equalization, and the like) are performed at the BBU117, and hence RRH implementation is highly simplified.

In this implementation, initially, the SRS transmitted from the users may be received by the BBU117. Based on the received SRS, the BBU117may estimate channel and interference statistics. Further, the BBU117may perform AI-based switching and determine the optimal equalizer for each RB. In such case, the BBU117transmits the information on the optimal equalizer for each RB with the RRH115. In an example, in each RB for which MMSE is optimal, the RRH115may compute the HHy and transmits to the BBU118(Dimension=#Tx layers). Then, the BBU117performs the computation (HHH+2I)−1and associates it to HHy to obtain MMSE estimate. While, in RBs where MMSE-IRC is optimal, the RRH115may transmit the “y” to the BBU117without combining (Dimension=#Rx antennas at RRH). Thus, the entire MMSE-IRC computation HH(HHH+{circumflex over (R)}z)−1y is performed at the BBU117.

FIG.2illustrates a detailed diagram of a base station for determining an optimal equalizer for managing interference in a wireless communication system, according to an embodiment of the disclosure.

The BE101may include the Central Processing Units113(also referred as “CPUs” or “a processor113”), the Input/Output (I/O) interface109, and the memory111. In some embodiments, the memory111may be communicatively coupled to the processor113. The memory111stores instructions executable by the processor113. The processor113may comprise at least one data processor for executing program components for executing user or system-generated requests. The memory111may be communicatively coupled to the processor113. The memory111stores instructions, executable by the processor113, which, on execution, may cause the processor113to determine an optimal equalizer for managing interference in a wireless communication system. In an embodiment, the memory111may include one or more modules211and data200. The one or more modules211may be configured to perform the steps of the disclosure using the data200, to determine an optimal equalizer for managing interference in a wireless communication system. In an embodiment, each of the one or more modules211may be a hardware unit which may be outside the memory111and coupled with the BS101. As used herein, the term modules211refers to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide described functionality. The one or more modules211when configured with the described functionality defined in the disclosure will result in a novel hardware.

In one implementation, the modules211may include, for example, a channel coefficients estimation module213, a matrix determination module215, a noise variation determination module217, an interference proportion estimating module219, an optimal equalizer determination module221, and other modules223. It will be appreciated that such aforementioned modules211may be represented as a single module or a combination of different modules. In one implementation, the data200may include, for example, channel coefficient data201, interference matrix data203, machine learning(or deep learning) model204, noise variance205, equalizer data207, and other data209.

In an embodiment, the channel coefficients estimation module213may estimate the channel coefficients of each slot of the plurality of slots with respect to time based on received Demodulation Reference Signal (DM-RS) symbol. In an embodiment, the channel coefficients estimation module213may use the DMRS symbol information and estimate the channel coefficients using any known estimation techniques. The channel coefficients estimated based on the DMRS symbol may be stored as the channel coefficient data201.

In an embodiment, the matrix determination module215may be configured to receive the channel coefficients from the channel coefficients estimation module213and determine the covariance of interference-and-noise (Rz) matrix for at least one Resource Block (RB) of the plurality of RBs of each slot based on the channel coefficients. The covariance of interference-and-noise (Rz) matrix may be stored as the interference matrix data203. The covariance of interference-and-noise (Rz) matrix is represented by Equation 5 below. Typically, Rzis estimated on a per-B basis for a given slot as follows:

Let Ndbe the number of DMRS REs per RB in a slot,

Let (d,d) be the set of RE positions in a slot where DMRS is transmitted;

Let p(k,) be the pilot transmitted in RE(k,)∈(d,d);

Then, z(k,)=y(k,)−H(k,)p(k,) is the estimate of interference-plus-noise vector;

The covariance is estimated as:

In an embodiment, the noise variation determination module217may determine the noise variance (σ2) based on noise measurements performed for one or more sub-carriers without the interference. Generally, one slot is constituted by 14 OFDM symbol while one RB is constituted by 12 consecutive sub-carriers. That is, the slot is across time and the RB is across frequency. The determined noise variance may be stored as the noise variance205. Typically, co-channel interference arises from cell edge users. These users may transmit in only a subset of carriers compared with what may be available with the BS101to serve its users. The remaining sub-carriers may be interference free. To determine interference free sub-carriers, the noise variation determination module217may observe noise power on each sub-carrier and identify interference free subcarriers as those with least noise power. The noise variance (σ2) may be determined using Equation 6 as defined below.

In an embodiment, the interference proportion estimating module219may estimate the interference proportion for the at least one RB based on the covariance of interference-and-noise (Rz) matrix and the noise variance. Particularly, the interference proportion estimating module219may estimate the interference proportion for the at least one RB by identifying diagonal elements from the covariance of interference-and-noise (Rz) matrix. Herein, the diagonal elements indicate interference-plus-noise power across each receiver antennas. The interference proportion estimating module219may then estimate an interference-plus-noise power based on an average of the diagonal elements. Then, an interference power is estimated based on a function of the interference-plus-noise power and the noise variance (σ2). Thereafter, the interference proportion estimating module219may estimate the interference proportion based on a ratio of the estimated interference power and the interference-plus-noise power.

In an embodiment, the optimal equalizer determination module221may be configured to determine an optimal equalizer from the plurality of equalizers for managing the interference, based on diagonal elements of the Rzmatrix and σ2of the at least one RB. The determined optimal equalizer may be stored as the equalizer data207. In one implementation, the optimal equalizer determination module221may determine the optimal equalizer using the machine learning(or deep learning) model204based on the diagonal elements of the Rzmatrix and σ2of the at least one RB.

FIGS.4A and4Bshow flow diagrams of training a machine learning model for determining an optimal equalizer according to various embodiments of the disclosure.

Throughout various embodiments, The machine learning model may be referred to an artificial intelligence (AI) model, or the like. The machine learning model may be trained based on training dataset comprising data points each corresponding to an OFDM slot and is composed of, transmission bits, received baseband signal vectors, DMRS symbols, and noise variance in respective slot. As shown in operations401and402, channel estimation is performed for each slot based on received DMRS symbols stored in a dataset. At operation403, by using the estimated channels from operation402, the covariance of interference-plus-noise Rzis estimated for each RB of all the slots in the dataset. Next, at operation404, vectors [diag({circumflex over (R)}z)σ2] are formed for each RB of all slots and stored as input features to be used for training. Then, at operations405and406, outputs/labels for the model training are the decisions on optimal equalizer between the MMSE and MMSE-IRC in each RB (or RB-group). To obtain these decision labels, both MMSE and MMSE-IRC are performed on each slot in the training dataset, followed by LDPC decoding to recover the data bits.

At operations407and408, the decoded bits from MMSE and MMSE-IRC equalizers are compared with transmitted bits, and the number of bit errors produced by MMSE (eMMSE) and MMSE-IRC (eIRC) are calculated for all slots. Lastly, at operation409, if the number of errors produced by MMSE is less than MMSE-IRC (eMMSE≤eIRC), then MMSE is selected as the optimal equalizer and the decision label is set as t=1. On the other hand, if eIRC<eMMSE, then MMSE-IRC is selected as the optimal equalizer, and the decision label is set as t=0. The decision labels for all slots are stored to be used as training labels for the machine learning model.

FIG.4Ashow the flow diagram of training the machine learning model with respect to two equalizers according to an embodiment of the disclosure.

However, the disclosure may also include training the machine learning model with respect to multiple equalizers as shown inFIG.4B. Training the machine learning model204with multiple equalizers include same operations as discussed inFIG.4A, except that of generation of output labels for the training. This is highlighted as bold in theFIG.4B. Explanations for other operations are omitted to avoid repetition. In this case particularly wherein optimal equalizer is to be determined between a plurality of equalizers (equalizer 1, . . . equalizer L), the machine learning model is required to include L output neurons and one-hot representation for output labels. That is, for a given RB of the dataset, if Equalizer-i produces the least number of errors, then the output label is a one-hot vector of length L that contains ‘1’ in ith position and ‘0’ in the remaining positions. The machine learning model is then trained with [diag({circumflex over (R)}z), σ2] as input and one-hot label as output.

While performing the training, the optimal equalizer determination module221may select a neural network architecture such that it comprises M+1 input layers and one output layer. In an embodiment, the number of hidden layers and the neurons in each hidden layer can be chosen flexibly based on experimentation to balance performance-complexity trade-off. In an embodiment, a deep neural network, and the machine learning model204may be trained by feeding the vectors [diag({circumflex over (R)}z) σ2] as input and the optimal equalizer label t as output (as discussed above). In an embodiment, sigmoid/softmax activation layer may be used for output layer. In an embodiment, the training of the machine learning model204may be performed offline before implementing the system in real-time.

In another implementation, the optimal equalizer determination module221may determine the optimal equalizer by receiving the interference proportion from the interference proportion estimating module219and comparing it with a predetermined interference threshold for the at least one RB. For instance, if the estimated interference proportion is less than the predetermined threshold, the optimal equalizer determination module221may determine to use the MMSE, so that complexity is reduced without loss in performance. While, if the estimated interference proportion is more than the pre-determined threshold, the optimal equalizer determination module221may use the MMSE-IRC so that the co-channel interference is effectively mitigated.

The other data209may store data, including temporary data and temporary files, generated by the one or more modules211for performing the various functions of the BS101. The one or more modules211may also include the other modules223to perform various miscellaneous functionalities of the BS101. The other data209may be stored in the memory111. It will be appreciated that the one or more modules223may be represented as a single module or a combination of different modules.

FIGS.6A and6Bshow flowcharts illustrating method operations for determining an optimal equalizer for managing interference in a wireless communication system, according to various embodiments of the disclosure.

Referring toFIG.6A, the method600may comprise one or more operations. The method600may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

The order in which the method600is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.FIG.6Aexplicitly shows a flowchart illustrating method operations for determining an optimal equalizer for managing interference using a machine learning model in a wireless communication system.

At operation601, channel coefficients of each slot of the plurality of slots is estimated based on the received Demodulation Reference Signal (DM-RS) symbols.

At operation602, a covariance of interference-and-noise (Rz) matrix is determined for at least one Resource Block (RB) of a plurality of RBs of each slot based on the channel coefficients.

At operation603, a noise variance (σ2) is determined based on noise measurements performed for one or more sub-carriers without the interference.

At operation604, an optimal equalizer from a plurality of equalizers for managing the interference, based on diagonal elements of the Rzmatrix and σ2of the at least one RB using a machine learning model. Herein, the plurality of equalizers comprises at least one of, a Minimum Mean Squared Error (MMSE) equalizer, MMSE with Interference Rejection Combiner (MMSE-IRC) equalizer, and a MMSE with Successive Interference Cancellation (MMSE-SIC) equalizer. The machine learning model may be pretrained by generating input features comprising a plurality of training diagonal elements of Rzand σ2of each RB. The training diagonal elements are obtained based on training channel coefficients of each slot based on training dataset of DMRS symbols. The equalization is performed on each slot using each of the plurality of equalizers, on the training dataset. Further, decoded bits for each of the equalizers are obtained by performing the predefined decoding technique. Then, a number of error bits are determined for each slot generated by each of the plurality of equalizers during equalization based on the respective decoded bits. Then, output labels are determined for the machine learning model for each slot based on a comparison of the number of error bits corresponding to each of the plurality of equalizers with respect to each other. In an embodiment, the machine learning model may be trained based on a correlation between interference proportion and operating Signal to Interference Noise Ratio (SINR) associated with a plurality of training diagonal elements.

FIGS.5A and5Bshow graphs for showing accuracy of adaptive switching of equalizers according to various embodiments of the disclosure.

For instance, numerical evaluations are performed for two system configurations, 4 Tx layers, 16 Rx antennas as shown inFIG.5Aand 12 Tx layers, 64 Rx antennas as shown inFIG.5B. Consider, the dataset for both the above configurations consist of 10000 points, such that each data point is formed by (Tx bits, received vectors, DMRS symbols, noise variance). The datasets are processed for generating input features and output labels for model training.

For the machine learning architecture for 4Tx, 16Rx system, consider the inputs as →16→ReLU→8 ReLU→4 ReLU→1→Sigmoid.

Consider, three hidden layers are present with 16, 8, and 4 neurons, respectively.

Output layer uses sigmoid activation, while rest of the layers use ReLU activation.

The training is carried out and the trained model is used for determining the optimal equalizer. In an example, the network may perform around 465 floating point operations (FLOPs) for selecting the optimal equalizer. Since machine learning switching is performed once per RB, this amounts to 465 FLOPs per RB to decide the optimal equalizer. Therefore, the complexity overhead induced by the machine learning model per RE is

which is insignificant compared with the complexity of performing MMSE (747 FLOPs) and MMSE-IRC (5675 FLOPs) in each RE.

For the machine learning architecture of 12Tx, 16Rx system configuration, consider inputs as, i/p→32→ReLU→16 ReLU→4 ReLU→1→Sigmoid. In this case, the machine learning switching with the above network requires around 2713 FLOPs per RB and hence around twenty FLOPs per RE. This is insignificant compared with the complexity of MMSE (21696 FLOPs) and MMSE-IRC (286891 FLOPs) in each RE.

In an embodiment, the machine learning based optimal equalizer determination may be validated via 5G link level simulations. The numerical results in terms of the complexity reduction and switching accuracy (i.e., the percentage of times AI-based switching selects the optimizer equalizer) are shown in below Table 4 under TDL-C and D channels. In an example, 15 kHz sub-carrier spacing, 16-QAM modulation and rate-2/3 LDPC code are used. The interference proportion is randomly distributed between 0 and 1 in each slot, and the simulations are carried out for 10,000 slots.

The Table 4 shows that, the machine learning based switching achieves significant reduction in complexity compared with MMSE-IRC. For example, for 12Tx and 64Rx system, complexity is reduced by up to 66% while also achieving reliable equalization.

FIG.6Bshows a flowchart illustrating method operations for determining an optimal equalizer for managing interference using a threshold based technique in a wireless communication system according to an embodiment of the disclosure.

As shown, at operation606is equivalent to operations601,607is similar to operations602and608is same as operation603. Further, at operation609, an interference proportion for the at least one RB is estimated based on the covariance of interference-and-noise (Rz) matrix and the noise variance (σ2). At operation610, an optimal equalizer from a plurality of equalizers is determined based on a comparison of the interference proportion with a predetermined interference threshold for the at least one RB. For instance, if the estimated interference proportion is less than the predetermined threshold, the MMSE may be determined as the optimal equalizer, so that complexity is reduced without loss in performance. While, if the estimated interference proportion is more than the pre-determined threshold, the MMSE-IRC may be determined as the optimal equalizer so that the co-channel interference is effectively mitigated.

Computer System

FIG.7illustrates a block diagram of a computer system700according to an embodiment of the disclosure.

In an embodiment, the computer system700may be the BS101. Thus, the computer system700may be used to determine an optimal equalizer for managing interference in a wireless communication system. The computer system700may transmit the one or more requests to the network (for instance, a base station in the network), over a communication network709. The computer system700may comprise a Central Processing Unit702(also referred as “CPU” or “processor”). The processor702may comprise at least one data processor. The processor702may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

The processor702may be disposed in communication with the communication network709via a network interface703. The network interface703may communicate with the communication network709. The network interface703may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network709may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. The network interface703may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.

The communication network709includes, but is not limited to, a direct interconnection, an e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, and such. The first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

The memory705may store a collection of program or database components, including, without limitation, user interface706, an operating system707, web browser708etc. In some embodiments, computer system700may store user/application data, such as, the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle® or Sybase®.

In some embodiments, the computer system700may implement the web browser708stored program component. The web browser708may be a hypertext viewing application, for example MICROSOFT® INTERNET EXPLORER™ GOOGLE® CHROME™, MOZILLA® FIREFOX™, APPLE® SAFARI™, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers708may utilize facilities such as AJAX™, DHTML™, ADOBE® FLASH™, JAVASCRIPT™, JAVA™, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system700may implement a mail server (not shown in Figure) stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP™ ACTIVEX™, ANSI™ C++/C#, MICROSOFT®, NET™, CGI SCRIPTS™, JAVA™ JAVASCRIPT™, PERL™, PHP™ PYTHON™, WEBOBJECTS™, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system700may implement a mail client stored program component. The mail client (not shown in Figure) may be a mail viewing application, such as APPLE® MAIL™, MICROSOFT® ENTOURAGE™ MICROSOFT® OUTLOOK™, MOZILLA® THUNDERBIRD™, etc.

FIG.8illustrates a structure of a base station according to an embodiment of the disclosure.

Referring toFIG.8, the base station according to an embodiment may include a transceiver810, memory820, and a processor830. The transceiver810, the memory820, and the processor830of the base station may operate according to a communication method of the base station described above. However, the components of the base station are not limited thereto. For example, the base station may include more or fewer components than those described above. In addition, the processor830, the transceiver810, and the memory820may be implemented as a single chip. The base station ofFIG.8may correspond to the base station ofFIG.1B.

The transceiver810collectively refers to a base station receiver and a base station transmitter, and may transmit/receive a signal to/from a terminal (UE) or a network entity. The signal transmitted or received to or from the terminal or a network entity may include control information and data. The transceiver810may include a RF transmitter for up-converting and amplifying a frequency of a transmitted signal, and a RF receiver for amplifying low-noise and down-converting a frequency of a received signal. However, this is only an example of the transceiver810and components of the transceiver810are not limited to the RF transmitter and the RF receiver.

Also, the transceiver810may receive and output, to the processor830, a signal through a wireless channel, and transmit a signal output from the processor830through the wireless channel.

The memory820may store a program and data required for operations of the base station. Also, the memory820may store control information or data included in a signal obtained by the base station. The memory820may be a storage medium, such as read-only memory (ROM), random access memory (RAM), a hard disk, a CD-ROM, and a DVD, or a combination of storage media.

The processor830may control a series of processes such that the base station operates as described above. For example, the transceiver810may receive a data signal including a control signal transmitted by the terminal, and the processor830may determine a result of receiving the control signal and the data signal transmitted by the terminal.

FIG.9illustrates a structure of a user equipment according to an embodiment of the disclosure.

Referring toFIG.9, the UE according to an embodiment may include a transceiver910, memory920, and a processor930. The transceiver910, the memory920, and the processor930of the UE may operate according to a communication method of the UE described above. However, the components of the UE are not limited thereto. For example, the UE may include more or fewer components than those described above. In addition, the processor930, the transceiver910, and the memory920may be implemented as a single chip. Also, the processor930may include at least one processor.

The transceiver910collectively refers to a UE receiver and a UE transmitter, and may transmit/receive a signal to/from a base station or a network entity. The signal transmitted or received to or from the base station or a network entity may include control information and data. The transceiver910may include a RF transmitter for up-converting and amplifying a frequency of a transmitted signal, and a RF receiver for amplifying low-noise and down-converting a frequency of a received signal. However, this is only an example of the transceiver910and components of the transceiver910are not limited to the RF transmitter and the RF receiver.

Also, the transceiver910may receive and output, to the processor930, a signal through a wireless channel, and transmit a signal output from the processor930through the wireless channel.

The memory920may store a program and data required for operations of the UE. Also, the memory920may store control information or data included in a signal obtained by the UE. The memory920may be a storage medium, such as read-only memory (ROM), random access memory (RAM), a hard disk, a CD-ROM, and a DVD, or a combination of storage media.

The processor930may control a series of processes such that the UE operates as described above. For example, the transceiver910may receive a data signal including a control signal transmitted by the base station or the network entity, and the processor930may determine a result of receiving the control signal and the data signal transmitted by the base station or the network entity.

The disclosure provides a solution to an important bottleneck in the 5G and beyond systems. Specifically, the disclosure provides an AI-based methods for significantly reducing the equalization complexity in 5G and beyond BSs, while also improving the equalization performance in the presence of co-channel interference. An embodiment of the disclosure saves significant computational resources and power. The disclosure is also extended to the O-RAN architecture. An embodiment of the disclosure enables to provide reliable communication to its users even under dense networks with co-channel interference.

Existing techniques reduces the complexity of MMSE-IRC and try to approximate the inverse calculation via series expansions and iterations. These approaches either require non-trivial changes in the existing receivers or suffer from performance degradation due to poor approximations. The disclosure takes a completely different approach wherein the BS switches adaptively between multiple equalizers, and hence no changes in existing receiver are necessary. Also, since no approximations are involved, the disclosure do not suffer from performance loss. In fact, the disclosure provides better performance than MMSE-IRC under certain regimes.

The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise.