Spectrum is one of the most valuable assets in wireless communications. Over the last 30 years, telecom operators in the US have spent more than $240B to acquire wireless spectrum. A goal of a radio access network (RAN) system is to extract the maximum spectral efficiency (bits/second/Hertz) possible, which translates into more capacity, stronger network resilience with fewer dropped packets, and better economics per site.
As operators look for ways to extract more value from existing spectrum, Massive MIMO (Multiple-input, multiple-output) has become an important method for increasing capacity and improving how efficiently the network serves users.
Massive MIMO promised a revolutionary leap in spectral efficiency. However, in field deployments, the industry is operating below what the technology can theoretically achieve, leaving immense capacity unused. The root causes are fundamentally system-level problems. The network struggles to accurately track user locations, signals overlap and cause interference, and the system fails to pair users efficiently for simultaneous data transmission.
The industry has examined these challenges through a “compute-constrained” lens, treating compute as a scarce resource, forcing compromises to fit complex algorithms within the strict limitations of CPU power and performance budgets.
