AI-Enhanced MIMO Implementation: Proven Large-Scale Technology OR Selective Deployment?

SoftBank and Ericsson have officially commercialized their Artificial Intelligence (AI)-controlled Massive Multiple Input, Multiple Output (mMIMO) coverage optimization solution in several large-scale venues in Japan after the successful trial that took place at Expo 2025 in Osaka, Japan. This new coverage optimization solution incorporates an external layer of AI control on the mMIMO system by ingesting user distribution and estimates of the beam direction at a minute interval to allow for near real-time reconfiguration of both the horizontal and vertical beam patterns.

Unlike conventional methods of optimizing mMIMO through static or scheduled configuration and prior history of traffic dependency, the system reacts dynamically to real-time data as it interacts with its environment. In addition, it can identify abnormal usage patterns caused by unpredictable external events, along with variations in crowd dynamics due to environmental conditions, and change the coverage area, if necessary, to accommodate those users. In the Osaka trial conducted by SoftBank, a reported 24% increase in downlink throughput was achieved primarily because of minimizing packet stalling during periods of high-traffic volume; consequently, it was possible to provide more reliable service to end users. While the current deployment of technology is limited to specific venues, this deployment does represent a significant move toward AI-mediated control of the air interface within commercial networks, as opposed to responding reactively to changes in usage patterns based on previous traffic analysis data.

AI-assisted mMIMO is changing the method of radio planning from being static to one of being adaptive and intent-based for radio links. Traditional Radio Access Network (RAN) architectures function with predictable traffic distributions to enable optimal radio coverage for a given area, which has become less effective in environments where user density and demand are continually shifting. AI provides real-time optimization of beamforming angles, power allocation, user scheduling, and interference mitigation. This results in enhanced throughput, reduced latency, and the system’s load being balanced throughout the network. MIMO technology, enhanced with AI, enables operators to dynamically manage beam patterns based on the continual collection of spatial user data to manage congestion, rather than just absorb it as a result of congestion.

Managing congestion in this manner has a direct impact on providing deterministic connectivity to end customers. Because of the inherent variability of the radio interface, the ability to provide end-to-end performance has historically been limited, even where transport, packet switching, and cloud infrastructure are highly optimized. AI-based beamforming reduces the risk of burst latency and packet loss by anticipating rapid shifts in demand and reallocating radio resources ahead of sustained peaks. Although the radio channel itself is chaotic and impacted by mobility, environmental, and weather elements, the fundamental change that takes place is that the network can now respond more quickly and adaptively to actively counter these disruptions. It is this swift and intelligent response that allows time-sensitive and mission-critical applications to provide a more stable performance in highly dynamic environments.

More importantly, AI-enhanced mMIMO will also change the way that capacity is planned and valued. Instead of being engineered primarily using peak hour assumptions, operators will be able to begin using a more dynamic model for capacity allocation based on demand as it occurs in real time. This change will have an impact on how effectively operators are using their spectrum, site planning, and investment strategies because operators will be able to get incremental capacity from software-driven optimizations versus traditional continuous physical densification. Over the long term, this will change how operators evaluate the return they achieve from their RAN investments, especially in areas with restrictions on adding new sites or deploying new spectrum.

AI-enhanced mMIMO also provides operators with structural efficiencies. Rather than constantly powering up broad spatial layers, AI-based systems will selectively power only those beams with active users, providing enhanced spectral efficiency and substantially reducing waste energy compared to traditional methods. As networks continue the trend toward supporting AI workloads and distributed compute, efficiency will be a useful offset to the costs of operation and energy as they relate to network efficiency.

AI-enabled mMIMO has strong potential, but adoption faces several challenges. Most deployments will occur in coordinated hybrid networks, where private enterprise networks, public operator networks, and shared spectrum systems operate together and carry mixed commercial and critical traffic. This convergence adds complexity in traffic prioritization, policy enforcement, security boundaries, and operational transparency, making governance and control as important as the radio technology itself. Also, the benefits of beamforming from AI vary greatly based on the predictability and the level of volatility of the environment in which it is operating. In some cases, AI-enabled beamforming may not provide enough benefit to justify the additional overhead, which includes extra computational resources, more complex network management, increased power consumption, and the operational effort required to monitor, train, and maintain the AI systems integrated into mMIMO infrastructure.

To work within these limitations, operators need to closely align AI-enabled mass MIMO deployments in mission-critical environments. The best near-term opportunities are public safety networks, transportation hubs, industrial campuses, utilities, and large venues because unpredictability in demand greatly impacts operational success. These use cases allow operators to demonstrate measurable improvement in reliability, continuity, and managing congestion, providing credible reference points for the broader extension of use cases.

Governance and orchestration of AI-enhanced mMIMO should also be treated as primary components of architecture, rather than supportive components. Control of the air interface using AI technologies requires transparency and being integrated into existing policy and compliance frameworks (especially in shared/mission-critical networks). Building these into the architecture will aid in reducing operational risk and building trust with enterprise/public sector purchasers while establishing AI-enabled mMIMO as a sustainable infrastructural evolution, rather than functioning as an experimental optimization layer.