What Is Distributed MIMO and How Does AI Scale the Next-Gen Wireless Network?

Massive MIMO promised a step change in spectral efficiency. In practice, deployments are delivering around 25 percent of what the theory projected. The gap is not a hardware problem – it is a computational one. A session moderated by Abe Nejad of The Network Media Group (NMG) brought together experts from Eridan, NVIDIA, NTIA, Northeastern University, and Rakuten Symphony to examine how Distributed MIMO, orchestrated by AI within an Open RAN framework, closes that gap and what it will take to make it commercially real.

Speakers:

• Doug Kirkpatrick – CTO, Eridan
• Soma Velayutham – GM, AI/5G/Telecoms, NVIDIA
• Amanda Toman – Director, Wireless Innovation Fund, NTIA
• Tommaso Melodia – Director, Institute for Intelligent Networked Systems, Northeastern University
• Vihang Kamble – CTO, RAN BU, Rakuten Symphony

Watch the full interview.

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dMIMO vs. Massive MIMO: Dynamic Scalability Is the Difference

Traditional Massive MIMO is fixed by design: once a 64x64 array is installed, that is the system you have. Distributed MIMO breaks that constraint. Disaggregating and reaggregating antenna elements across multiple transmission and reception points (TRPs) enables the network to become dynamically configurable. For users at the cell edge, where interference from neighboring cells traditionally degrades signal quality, dMIMO allows signals from multiple TRPs to be constructively combined, delivering significantly higher throughput. 

The trade-off is complexity: fronthaul latency, synchronization requirements, and the combinatorial challenge of managing antenna clustering, spectrum, and power allocation at scale are problems that classical approaches cannot solve. That is where AI enters.

AI Is Not Optional for dMIMO — It Is the Architecture

The panel was emphatic on this point: dMIMO at scale is an AI problem. Radio is not a parameterization exercise; it is organic, constantly moving, and must be continuously recomputed. A hierarchy of AI controllers, operating from near-real-time applications close to the base station up to higher-level coordinators managing virtual base station clusters, is what allows the system to learn how to best serve users given the resources available at any given moment. GPU-accelerated, software-defined platforms enable the iterative recomputation that static hardware architectures simply cannot support. With AI-native, programmable compute substrates, commercial viability is within reach.

Operational Realities: What the Early Trials Are Teaching Us

The challenges are systemic. Even slight fronthaul jitter can cause dMIMO gains to evaporate. Phase, timing, and frequency synchronization must all be achieved simultaneously. Some of the methods defined by 3GPP for doing so depend on UE feedback, introducing another variable that can undermine distributed gains in practice. 

The gap between current distributed schemes and true coherent joint transmission remains real, and bridging it requires the entire ecosystem to move together: mature 3GPP standards, advanced UE modem implementations, operators willing to invest in ideal fronthaul infrastructure, and AI-native baseband software capable of dynamically optimizing the cooperating antenna set.

What Operators Need to Do Now to Prepare

The panel closed with a clear set of priorities for operators preparing for AI-orchestrated dMIMO at scale. 

1. First, put the architectural foundations in place: hierarchical AI decision-making in the RAN does not exist today and needs to be designed in, not retrofitted. 
2. Second, stop treating dMIMO and dynamic spectrum sharing as separate problems; they are intertwined. 
3. Third, invest in physically accurate digital twins: this is where ideas get validated before deployment. Without that simulation layer, the pace of iteration will be too slow. 
4. Finally, the regulatory environment must evolve. The dynamism that dMIMO and AI-native spectrum management promise cannot be realized under regulatory frameworks written for a static, hardware-defined world.

‍Key takeaways

• dMIMO delivers what Massive MIMO promised. By transmitting from multiple distributed TRPs, dMIMO constructively combines signals at the cell edge,  addressing coverage and capacity challenges that fixed arrays cannot.
• AI is not an add-on to dMIMO; it is the enabling layer. A hierarchy of AI controllers managing antenna clustering, spectrum, and power allocation is what makes distributed MIMO operable at commercial scale.
• Fronthaul and synchronization remain the hard engineering problems. Even marginal jitter or synchronization gaps can negate dMIMO gains; solving these requires coordinated action across the full ecosystem.
• Digital twins are the required proving ground. Physically accurate simulation environments allow operators and vendors to iterate and validate before committing to live network deployment.
• dMIMO is the 6G baseline. The panel agreed: distributed coherent transmission will not be optional in 6G. The work done now on standards, hardware, and AI-native architectures is what makes that transition possible.

“The challenges are not conceptual; they are at the systemic and operational levels. Even the slightest jitters could cause the dMIMO gains to evaporate. 3GPP has done incremental work, no doubt, but there is still a gap between the current distributed schemes and the coherent joint transmission. This gap needs to be bridged.”

— Vihang Kamble, CTO, RAN BU, Rakuten Symphony

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