A method to enable visibility regions (VRS) detection and identification, and a VR-aware user scheduling technique in extremely large aperture arrays (ELAA) based wireless networks

Abstract

The invention particularly relates to a visibility regions (VRs) identification / detection method and a VR-aware user scheduling technique in extra-large antenna array (ELAA)-based communication networks to enhance user fairness in accessing the network, as well as the overall system capacity.

Description

[0001] A method to enable visibility regions (VRs) detection and identification, and a VR-aware user scheduling technique in Extremely Large Aperture Arrays (ELAA) based Wireless Networks

[0002] Technical Field

[0003] The invention relates to a method that enables visibility regions (VRs) identification and detection, and a VR-aware user scheduling technique in Extremely Large Aperture Arrays (ELAA) based Wireless Networks.

[0004] The invention particularly relates to a visibility regions (VRs) identification / detection method and a VR-aware user scheduling technique in extra-large antenna array (ELAA)-based communication networks to enhance user fairness in accessing the network, as well as the overall system capacity.

[0005] Present State of the Art

[0006] In the realm of wireless communication networks, the pursuit of higher data rates, reduced latency, improved reliability, and enhanced system capacity has led to the development of complex and massive network infrastructures. Extremely Large

[0007] Aperture Arrays (ELAA) represent an innovative approach to address these requirements by deploying large-scale antenna arrays. However, with the growth in network complexity, several technical challenges have emerged. One such challenge is the effective identification and allocation of visibility regions (VRs). A visibility region (VR) of a user represents a section within ELAA structure where the transmission and reception of signals are optimized, offering enhanced communication performance. The ability to precisely identify and control VRs is paramount for network optimization, interference mitigation, and resource allocation. In the quest to meet the demands of speed and capacity in wireless networks, issues like noise, interference, and others have become prevalent. Consequently, a need arises for a novel method to address technical problems, particularly in the context of Extremely Large Aperture Arrays (ELAA) as a solution. In the present state of the art, there are only a few approaches based on visibility regions (VRs) identification and detection in Extremely Large Aperture Arrays (ELAA) based Wireless Networks and the following articles have been found: “On the Uplink Transmission of Extra-Large Scale Massive MIMO Systems,” [1]; “Exploring the NonOverlapping Visibility Regions in XL-MIMO Random Access and Scheduling,” [2]; “Extremely Large Aperture Massive MIMO: Low Complexity Receiver Architectures,” [3]; “A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems,” [4]; “A Grant-Based Random Access Protocol in Extra-Large Massive MIMO System,” [5]; “Achieving Fair Random Access Performance in Massive MIMO Crowded Machine-Type Networks,” [6]; “Ultra-Massive multiple input multiple output technology in 6G,” [7]; “Toward Extra Large-Scale MIMO: New Channel Properties and Low-Cost Designs,” [8].

[0008] Next generations of wireless networks are expected to feature larger antenna arrays with a drastically increased dimensions in terms of number of antenna elements as compared to the current massive multiple-inputs multiple-outputs (mMIMO) systems. Such large-sized arrays are necessary in order to facilitate significant improvement in network’s capacity, spectral efficiency as well as the spatial resolutions. In the current literature, these large-sized arrays are referred to with different names, including extralarge antenna array (ELAA), extra-large MIMO (XL-MIMO), ultra-massive MIMO (UM- MIMO), extremely large aperture massive MIMO (xMaMIMO), as in [1]-[8].

[0009] Numerous studies have revealed that, due to their large aperture sizes, ELAAs experience several new channel characteristics that are not present in the ordinary massive MIMO array structures. With ELAA transceiver architecture, most of the UEs (User equipment) and scatterers fall within the near field of the base station (BS) in [1], [7]-[8]. The near-field of an antenna array is defined as the region where the communication between the ELAA and the user is carried out in a distance smaller than the Fraunhofer distance of the array, beyond the Fraunhofer distance, the communication is said to be carried out in far-field with the conventional planar wave propagation as presented in classic channel models in

[0001] ,[7]-[8]. The Fraunhofer distance is given by where ® is the array aperture size and is the wavelength of the signal. The near-field property and the large size of ELAA generate a non- stationary signal reception at the base station. The non-stationarity is due to the time difference of arrival of the signal at different array elements caused by the curvature of a spherical wavefront (SW) experienced in near-field ELAA communication. Meaning that, at a given time, different antenna elements of the ELAA receive varying power levels and polarization depending on wavefront curvature, or the Time and angle of arrival. The second major cause of the non-stationary reception is that scatterers, and blockers in the propagation environment, force users to interact with portions of the ELAA, called visibility region (VR), rather than the full array in [1]-[8]. The spherical wavefront (SW) and visibility region (VR) are indeed by product the key causes of the non-stationary (varying power levels, path gains, AoA) reception in ELAA-based communication. These two characteristics have challenged most of the spatially stationary channel models that are currently in used. Firstly, because the spherical wavefront results into the spreading of the symbol leading to inter-symbol interference when classical models are applied, and thus new models and receivers are required. Secondly, the spatial non-stationarity on a ELAA makes it difficult to generate the channel state information because of the power sparsity across the array. On contrary, the spatial non-stationarity leads to a new spatial separability possibility such that concurrent communications can be carried out in distinct regions of the ELAA and may be distinguished based on the occupied sub-arrays; this is known as spatial resolution or orthogonality. Several studies have thus drawn conclusions on the potentiality of VRs and spatial resolution in enhancing the system capacity, multiplexing gain, spectrum efficiency and energy efficiency in ELAA based communications.

[0010] Visibility regions (VRs) are considered as extra spatial resolution resources for user multiplexing in improving system capacity and spectrum efficiency in 6G and future generations of wireless communication systems. However, because VRs for different users are generally randomly distributed across the ELAA, their overlap is inevitable and may impede the spatial resolution. In other words, VRs associated with different users may totally or partially overlap, leading to significant interference between users. Fortunately, depending on the environment characteristics, users may have different number of VRs, leading to having users with different degrees of freedom (DoF) in accessing the network. Therefore, an efficient exploitation of VRs as communication resources requires proper scheduling mechanism that ensures fairness in allocating VRs while avoiding interference between users.

[0011] In state-of-the-art proposals, user scheduling is done such that in scenarios where VRs do not overlap; payload resources are simultaneously reused among users to improve on the multiplexing gain and spectrum efficiency in [1] and [2], It is also common that unused array elements are switched off to reduce the processing time and energy consumption. The idea behind switching strategies is that, given two or more partially overlapping VRs, overlapping array elements can be switched off to eliminate and mitigate the impact of the interference, and then information of specific users can be recovered from the power or energy collected on the remaining non-overlapping VRs elements. This method is efficient when the power or energy in the overlapping regions does not significantly contribute to the signal-to-noise ratio (SNR) required for information recovery, because switching off VR elements can result in significant power or energy loss in some instances.

[0012] On the other hand, fully overlapping visibility regions have been observed in two different forms or scenarios. The first of which is considered to occur when a VR of one user is large enough to completely contain a VR of another user. In this scenario, interference-cancellation procedures have been developed and adopted, in which the signal from the non-interfering section of the large VR is extracted first and then used to cancel or subtract the interference injected into the contained VR

[0001] ,[3]. The second scenario happens when two VRs of the same size totally overlap; in this case, interference-cancellation does not function, and hence concurrent user scheduling strategies, such as the strong-user-based scheduling techniques discussed below, were proposed.

[0013] The fundamental assumption in strong-user-based scheduling strategies is that the user with the highest power or energy emitted to the specified visibility region or highest contribution to spectrum efficiency is assigned the requested visibility region, as adopted in channel state information-based techniques and strongest-user collision resolution techniques in [1]-[5]. Channel state information: The research in “On the Uplink Transmission of Extra-Large Scale Massive MIMO Systems” created a greedy user and subarray scheduling approach based on channel state information, in which the user with the best channel state information is selected for communication. Because of the size of the array and the significant non-stationary reception across the array, extracting precise channel state information is a time- and resource-intensive task. Strongest-user collision resolution: “A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems” and “A Grant-Based Random Access Protocol in Extra-Large Massive MIMO System” articles provided the strongest-user collision resolution (SUCRe) and grant-based Random-access approaches, respectively. When a collision of pilots is identified during the random-access operation, payload resources in a specific area of the array are assigned to the strongest colliding user. In general, the strongest-user choice, received power-based decision, or CSI-based decision does not take into account user fairness and service quality, and as a result, the decision is improper for today's multi-service communication world. The work in “Achieving Fair Random Access Performance in Massive MIMO Crowded Machine-Type Networks” improves on these works by presenting an access class barring with power control (ACBPC) protocol, which ensures that a user's connectivity is independent of power or distance to the base station. This is more equitable because it does not prioritize users who are closer to the base station over edge users.

[0014] As seen, the technical problems in the present, state visibility regions (VRs) identification / detection and user scheduling fairness difficulties in extra-large antenna array (ELAA)-based communication networks.

[0015] Due mainly to inadequacy of the solutions to meet above mentioned fairness needs, it was necessary to make a development in the relevant technical field.

[0016] The Purpose of the invention

[0017] The invention is inspired by present situations and aims to solve above mentioned disadvantages.

[0018] The main purpose of the invention is to identify VRs and efficiently allocate them to users while limiting co-user interference and improving system capacity, spectrum and energy efficiency.

[0019] Another purpose of the invention is to regulate VR creation and allocation in cases where VRs from several users may interfere or overlap.

[0020] In order to achieve above mentioned purposes, the invention comprises the process steps of i. Calculating beam sweeping angles, II. Performing beam sweeping and VRs identification, ill. Creating lookup tables, iv. Validation of visibility regions and lookup tables.

[0021] The structural and characteristic features of the invention and all its advantages will be understood more clearly thanks to the figures given below and the detailed description written with reference to these figures and therefore the evaluation should be made considering these figures and detailed description.

[0022] References List

[0023] 100. Calculating beam sweeping angles

[0024] 110. Performing beam sweeping and VRs identification

[0025] 120. Creating lookup tables

[0026] 130. Validation of visibility regions and lookup tables

[0027] 140. Delivering lookup tables to the VR-aware scheduling process for VRs allocation

[0028] 150. Calculating the number of VRs per user

[0029] 151. Calculating the number of VRs for each user based on the VRs lookup table information

[0030] 152. Requesting users’ services priorities

[0031] 153. Calculating each user's gain by combining the number of VRs and the service priority

[0032] 154. Assigning VRs starting with users with low gains

[0033] 160. Assigning VRs starting with users who have fewer VRs

[0034] 170. Calculating the number of interference-free VRs per user

[0035] 180. Assigning VRs starting with users who have less interference-free VRs

[0036] 190. Requesting or calculating of users’ services priorities 200. Assigning VRs starting with users who have a high service priority

[0037] VR. Visibility region

[0038] VRs. Visibility regions

[0039] UE. User equipment

[0040] BS. Bas station

[0041] Figures to Help Understanding of the Invention

[0042] Figure 1 is a representative illustration of the beam sweeping-based VRs detection procedure in the proposed invention.

[0043] Figure 2 is a representative illustration of a sample scenario of the visibility regions of two users in the proposed invention.

[0044] Figure 3 is a representative illustration of a first implementation option in the proposed invention.

[0045] Figure 4 is a representative illustration of a second implementation option in the proposed invention.

[0046] Figure 5 is a representative illustration of a third implementation option in the proposed invention.

[0047] Figure 6 is a representative illustration of a fourth implementation option in the proposed invention.

[0048] Figure 7 is a representative illustration of a conceptualized summary of the implementations in the proposed invention.

[0049] Detailed Description of the Invention

[0050] The invention particularly relates to a visibility regions (VRs) identification / detection method and a VR-aware user scheduling technique in extra-large antenna array (ELAA)-based communication networks to enhance user fairness in accessing the network, as well as the overall system capacity.

[0051] Although VRs are projected to improve the multiplexing gain in future wireless communication, there is little thinking in the literature on how to address the problem associated with their identification. Furthermore, present scheduling methods lack fairness in allocating VRs. This viewpoint is related to the fact that existing algorithms primarily use the energy emitted to the targeted visible region as the primary allocation consideration; in this instance, the VR is granted to the strongest user. The allocation based on received energy or power does not ensure a sufficient degree of fairness, resulting in minimal improvement in terms of the number of supported users. Also, interference cancellation techniques are complicated and have error propagation issues.

[0052] Efficient resource allocation and scheduling processes in multiple access communication systems are constantly required to reap the greatest benefits from additional multiplexing dimensions. This invention thus proposes a fair and reformed VRs allocation technique for utilizing the spatial resolution inherited from using ELAA. The aim is to identify VRs and efficiently allocate them to users while limiting co-user interference and improving system capacity, spectrum and energy efficiency. Most notably, this invention takes advantage of the beamforming feature on the user side to regulate VR creation and allocation in cases where VRs from several users may interfere or overlap.

[0053] This invention explores the problem of VRs identification and allocation in ELAA-based networks and comes up with a beam sweeping-based VRs identification approach as well as a VR-aware scheduling strategy for dealing with spatial separability in multiuser multi-visibility-region uplink transmissions. When users' VRs overlap or interfere, the VR-aware scheduling mechanism is triggered to assign VRs. With regards to this VR- aware scheduling, four implementation options are proposed; these include the number of VRs based allocation, number of interference-free VRs based allocation, service priority-based VR allocation, and gain-based VR allocation. The proposed visibility identification method and VR-aware scheduling strategy are unique in the literature on how to address the VRs identification and allocation problem. This invention is beneficial to the industry since it promotes the reuse of wireless resources while minimizing interference between users, thus improving system capacity and spectral efficiency. The beam sweeping-based VRs detection method and the VR-aware scheduling technique are two proposals that work as presented in the following section.

[0054] Beam sweeping-based VRs detection method. Calculating beam sweeping angles (100):

[0055] A user with M antenna elements performs beam sweeping in different angles whereby the number of beam sweeping angles is equal to s . For example, if the sweeping is done in terms of azimuth angles, from 7 to s with a linear array, the angular span of Also, the sweeping angles are calculated as , resents the beam angle.

[0056] Performing beam sweeping and VRs identification (110):

[0057] The beam sweeping process is done such that for each sweeping angle, the channel probing signal is transmitted towards the ELAA. Meanwhile, the base station examines the evolution of the probed signal across all of its array elements and selects those that receive substantial power. The array elements with RSSI (Received Signal Strength Indicator) greater than a threshold are deemed visible to the user with regard to the sweeping angle; the set of these array elements is referred to as the visibility region. On one hand, the threshold ensures that the signal at the array element is greater than the system's average noise power, and on the other hand, it ensures that the signal-to- interference-plus-noise ratio (SINR) in a VR is adequate for meeting QoS (Quality of Service) requirements. The sweeping process is repeated for all probing angles and all users. The VRs identification procedure can be completed for one user at a time, however this scheduling does not complete in a timely manner, causing key applications to be delayed. VRs for several users can be identified simultaneously to reduce scheduling time, as seen below.

[0058] To identify VRs for many users simultaneously, the users' channel probing signals must be orthogonal. In this situation, users are given typical orthogonal pilot or coded signals. Users use certain sweeping angles to send channel probing signals to ELAA at the same time. The base station uses the pilots to discriminate received signals and calculates the received signal strength or SNR of each user at each array element. The array elements that are visible to each user are decided by a predetermined threshold (with respect to the RSSI or SNR).

[0059] Creating lookup tables (120) to record VRs associated with all probing angles for all users: The lookup table of each user is made up of its VRs, and each column in it represents a VR. Elements of each column are obtained according to Performing beam sweeping and VRs identification (110), and are given as,

[0060] Meaning that, the ^ssarray element belongs to the visibility region associated with the user if the RSSI is greater or equal to a threshold (®), and thus the entry in the column 1 , and 0 otherwise.

[0061] Table 1 : Sample VRs identification lookup table

[0062] Each user has a specific number of sweeping angles, that is for the for each sweeping angle , the RSSI is evaluated across the array, and the value of is found for each antenna element, where represents the ^antenna element, can take values from 1 to With the entry can also take values from 1 to -

[0063] Validation of visibility regions and lookup tables (130):

[0064] Firstly, VRs validation consists of identifying sweeping angles that do not lead to consistent visibility regions. This process is critical because under some geometrical configurations of the environment, the RSSI across the entire array can be below the threshold, this means that the ELAA is not visible to user under the specific sweeping angle. As a result, all the entries in the VRs lookup table are zeros for that sweeping angle and thus the corresponding column should be removed from the lookup table. To this end, for VR to be valid under a sweeping angle, its norm, or the norm of the corresponding column in the lookup table should be bounded, meaning that the size of the VR should be greater than a minimum, also, it should not exceed a predefined maximum VR size so that to maintain the constraint that a VR cannot occupy the entire array. represents the column generated in the lookup table by the beam sweeping angle, "i ” represents the norm’s sign. ^ is the threshold that is associated with minimum VR size, and represents the threshold that is associated with the maximum VR size.

[0065] Secondly, in case a user experiences the same VR (i.e., fully, or highly overlapping VRs) for different of its sweeping angles, only one of the VRs can be retained in the lookup table. Decision on which VR to keep can be based on the size and / or RSSI and / or SNR / SINR that ensures better link performance of the user. The Similarity Ratio (SR) or the overlapping ratio between two visibility regions is thus calculated as follows. represents the number of similar entries, represents the number of different entries. Also, are the and " visibility region vectors of the user, and represents the transpose of the vector. When the similarity ratio exceeds a certain threshold, two visibility regions are regarded to be the same, and one of them is taken into account and remains in the user's VRs table. Lastly, delivering mentioned lookup tables to the VR-aware scheduling process for VRs allocation (140).

[0066] VR-aware scheduling technique.

[0067] Option 1 : Number of VRs based allocation:

[0068] Given that the base station has constructed lookup tables for users' visibility regions, the allocation of these VRs requires a decision support mechanism. To that aim, a VR- aware scheduling technique is proposed in which the base station classifies users based on the total number of VRs, the number of interference-free VRs, the users' service priorities, or both, in order to produce fair user scheduling queues.

[0069] Calculating of the number of VRs per user (150): The BS counts or calculates the number of VRs for each user based on the entries in VRs lookup tables.

[0070] Assigning VRs to users who have fewer VRs (160):

[0071] With the number of VRs based allocation, the BS begins assigning VRs to those who have fewer VRs (160). This assumption is based on the fact that users with a large number of VRs have high degree of freedom in accessing the network than those with a few VRs. For illustration purposes, let us suppose that there are two users in the base station coverage area; it is important to note that more than two users might be assumed. The first user (UE 1 / User equipment (User equipment is preferably a mobile device)) identifies one visibility region, while the second user (UE 2) identifies two visibility regions. One of the second user's visibility regions is distinct and does not overlap, whereas the other totally overlaps with the visibility region of the first user, as it is shown in Figure 2. When the overlapping VR is assigned to the second user, the first user is automatically denied an interference-free communication, this is because he relies on a single visibility region. On the other hand, if the first user is prioritized, meaning that the overlapping VR is assigned to the first user, it is therefore evident that both users will establish uplink communications with the ELAA since the second user can utilize this non-overlapping VR to communicate.

[0072] Option 2: Number of interference-free VRs based allocation:

[0073] Having a big number of VRs does not always suggest that a user is preferred for VR assignment. This is because some users may have many VRs but all of them overlap with VRs of other users. In this instance, a user may end up missing a VR regardless of how many VRs were visible to him; it is thus critical that the base station assigns VRs starting with users who have less number of interference-free VRs (i.e., the VRs that are not overlapping with other users’ VRs) to ensure user fairness. In this second implementation option of the invention as in Figure 4, step 150 and 160 of the first option are modified, leading to step (170) and (180).

[0074] Calculating the number of interference-free VRs per user (170) and assigning VRs starting with users who have less interference-free VRs to ensure user fairness (180).

[0075] Option 3: Service Priority-based VR allocation:

[0076] In this third implementation option of the invention as in Figure 5, step 150 and 160 of the first option are again modified leading to step (190) and (200). Requesting or calculating of user’s services priorities (190):

[0077] BS requests or computes users’ services priorities. The coexistence of heterogeneous services is an important aspect of 5G and future communication systems. In today's communication systems, many services such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC) are provisioned concurrently. It is thus possible that two or more services coexist and overlap in terms of VRs. When this occurs, the base station must allocate VRs based on QoS requirements, beginning with users who have a high service priority.

[0078] Assigning VRs starting with users who have a high service priority (200): The BS assigns VRs starting with users who have a high service priority.

[0079] Option 4: Gain-based VR allocation:

[0080] This implementation method takes into account both the number of VRs and the service priority of the users. This is especially relevant in congested networks, when user priority may involve factors other than the number of alternate visible regions. Like the second and third implementation options, steps 150 and 160 are modified, also, an additional intermediate step is added as follows;

[0081] Calculating the number of VRs for each user based on the VRs lookup table information (151): The BS calculates the number of VRs for each user based on the VRs lookup table information.

[0082] Requesting of user’s services priorities (152): The coexistence of heterogeneous services or services with different QoS requirements imposes the use of users’ service priorities in VRs allocation process, thus the base station requests the users’ services priorities at this stage.

[0083] Calculating each user's gain by combining the number of VRs and the service priority (153): The BS then computes each user's gain by combining the number of VRs and the service priority. The following is a simplified formulation of the gain. The gain is a function of the number of VRs (^») of user and the priority associated with its service is the priority function that relates the gain to the service priority. The variable is a positive value such that a high value means high priority.

[0084] Assigning VRs starting with users with low gains (154): The BS assigns VRs starting with users with low gains to increase the number of accommodated users. This assumption lies on the fact that the gain hat is computed in step 153 reduces exponentially with user’s service priority and thus, high priority users will have small gain and thus prioritized, it is also important to note that the number of VRs influences the gain and thus the model achieves fairness in allocating VRs to users.

[0085] References

[0086] [1] X. Yang, F. Cao, M. Matthaiou and S. Jin, "On the Uplink Transmission of ExtraLarge Scale Massive MIMO Systems," in IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 15229-15243, Dec. 2020, doi: 10.1109 / TVT.2020.3037317.

[0087] [2] J. C. M. Filho, G. Brante, R. D. Souza and T. Abrao, "Exploring the NonOverlapping Visibility Regions in XL-MIMO Random Access and Scheduling," in IEEE Transactions on Wireless Communications, vol. 21 , no. 8, pp. 6597-6610, Aug. 2022, doi: 10.1109 / TWC.2022.3151329.

[0088] [3] A. Amiri, M. Angjelichinoski, E. de Carvalho and R. W. Heath, "Extremely Large Aperture Massive MIMO: Low Complexity Receiver Architectures," 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates, 2018, pp. 1-6, doi: 10.1109 / GLOCOMW.2018.8644126.

[0089] [4] E. Bjbrnson, E. de Carvalho, J. H. Sorensen, E. G. Larsson and P. Popovski, "A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 16, no. 4, pp. 2220-2234, April 2017, doi: 10.1109 / TWC.2017.2660489. [5] O. S. Nishimura, J. C. Marinello and T. Abrao, "A Grant-Based Random Access Protocol in Extra-Large Massive MIMO System," in IEEE Communications Letters, vol. 24, no. 11 , pp. 2478-2482, Nov. 2020, doi: 10.1109 / LCOMM.2020.3012586. [6] J. C. Marinello, T. Abrao, R. D. Souza, E. de Carvalho and P. Popovski, "Achieving

[0090] Fair Random Access Performance in Massive MIMO Crowded Machine-Type Networks," in IEEE Wireless Communications Letters, vol. 9, no. 4, pp. 503-507, April 2020, doi: 10.1109 / LWC.2019.2960247. [7] G. Xu, X. Xu, W. Yang and B. Xu, "Ultra-Massive multiple input multiple output technology in 6G," 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), Shenyang, China, 2020, pp. 56-59, doi: 10.1109 / ISCTT51595.2020.00018 [8] Y. Han, S. Jin, M. Matthaiou, T. Q. S. Quek and C. -K. Wen, "Toward Extra Large-

[0091] Scale MIMO: New Channel Properties and Low-Cost Designs" in IEEE Internet of Things Journal, vol. 10, no. 16, pp. 14569-14594, 15 Aug.15, 2023, doi: 10.1109 / J IOT.2023.3273328

Claims

CLAIMS1. A method that enables visibility regions (VRs) identification / detection, and a VR- aware user scheduling technique in extra-large antenna array (ELAA)-based communication networks to enhance user fairness in accessing the network, as well as the overall system capacity and characterized by comprising the process steps; i. Calculating beam sweeping angles (100),II. Performing of beam sweeping and VR’s identification (110), ill. Creating of lookup tables (120), iv. Validation of visibility regions and lookup tables (130).

2. The method according to claim 1 and characterized by comprising the process steps;• Calculating the number of VRs per user (150),• Assigning VRs starting with users who have fewer VRs (160).

3. The method according to claim 1 and characterized by comprising the process steps;• Calculating the number of interference-free VRs per user (170),• Assigning VRs starting with users who have less interference-free VRs (180).

4. The method according to claim 1 and characterized by comprising the process steps;• Requesting or calculating of user’s services priorities (190),• Assigning VRs starting with users who have a high service priority (200).

5. The method according to claim 1 and characterized by comprising the process steps;• Calculating the number of VRs for each user based on the VRs lookup table information (151 ),• Requesting users’ services priorities (152),• Calculating each user's gain by combining the number of VRs and the service priority (153),• Assigning VRs starting with users with low gains (154).

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