In the current one-size-fits-all approach implementation for wireless networks, most resources are underutilized and not optimized for high-bandwidth and low-latency scenarios. Improve the coverage in a multi-cell scenario considering the inter-site interference between multiple 5G massive MIMO cell sites.Perform adaptive optimization of weights for specific use cases with unique user-distribution.Dynamically optimize the weights of antenna elements using the historical data.Identify dynamic change and forecast the user distribution by analyzing historical data.The weights for antenna elements for a massive MIMO 5G cell site are critical for maximizing the beamforming effect. Serving specific users with beam forming steering a narrow beam with high gain to send the radio signals and information directly to the device instead of broadcasting across the entire cell, reducing radio interference across the cell.Serving multiple spatially separated users with an antenna array in the same time and frequency resource.Massive simply refers to the large number of antennas (32 or more logical antenna ports) in the base station antenna array. Massive MIMO enhances user experience by significantly increasing throughput, network capacity and coverage while reducing interference by: After the UE connects to the beam, data session begins on the UE-specific (dedicated) beam. Once the UE identifies the best beam, it can start the random-access procedure to connect to the beam using timing and angular information. Historic values based on past events and measurements including previous serving beam information, time spent on each serving beam, and distance trends.Beam Reference Signal Received Power (BRSRP).ML and AI can assist in finding the best beam by considering the instantaneous values updated at each UE measurement of the parameters mentioned below: Finding the best beam by using BRSRP can lead to multi-target regression (MRT) problem while finding the best beam by using BI can lead to multi-class classification (MCC) problem. The UE reports the Beam State Information (BSI) based on measurements of Beam Reference Signal (BRS) comprising of parameters such as Beam Index (BI) and Beam Reference Signal Received Power (BRSRP). The user equipment (UE) measures and reports all the candidate beams to the serving cell site, which will then decide if the UE needs to be handed over to a neighboring cell site and to which candidate beam. The more activated beams present, the higher the probability of finding the best beam although the higher number of activated beams increases the system resource consumption. The best beam is the beam with highest signal strength a.k.a. An ideal set is the set that contains fewer beams and has a high probability of containing the best beam. A machine learned algorithm can assist the 5G cell site to compute a set of candidate beams, originating either from the serving or its neighboring cell site. ML/AI-as-a-service offering for end usersĥG, deployed using mm-wave, has beam-based cell coverage unlike 4G which has sector-based coverage.Dynamic network slicing to address varied use cases with different QoS requirements.Application-based traffic steering and aggregation across heterogeneous access networks.High level of automation from the distributed ML and AI architecture at the network edge.
The integration of ML and AI with 5G multi-access edge computing (MEC) enables wireless operators to offer: The figure below shows how 5G enables simultaneous connections to multiple IoT devices generating massive amounts of data.
Optimize and fine tune network parameters for capacity expansion.Forecast the peak traffic, resource utilization and application types.Machine Learning (ML) and Artificial Intelligence (AI) can assist wireless operators to overcome these challenges by analyzing the geographic information, engineering parameters and historic data to: The heterogenous nature of future wireless networks comprising of multiple access networks, frequency bands and cells - all with overlapping coverage areas - presents wireless operators with network planning and deployment challenges.