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Mavenir and Xilinx team up to expand Open RAN massive MIMO portfolio

Network software provider and adaptive processing platforms collaborate to accelerate Open RAN ecosystem, with mMIMO portfolio scheduled for launch later this year

With Huawei pivoting away from the traditional comms sector and governments around the world leading a clamour for diversity in comms technology supply, the time has never been riper for software-based systems to prosper – and end-to-end cloud-native network software provider Mavenir and adaptive computing supplier Xilinx have entered a partnership to take advantage.

The companies have announced that they are collaborating to bring to market a unified 4G/5G open radio access network (Open RAN or O-RAN) massive multiple-input and multiple-output (mMIMO) portfolio to enable Open RAN deployments.

The reasons for leadership in this area are clear. According to ABI Research, 5G Open RAN has significant momentum in the comms market, with supplier spending set to reach $10bn by 2026-27 and surpass traditional RAN at $30bn by 2030.

Assessing what the partnership could bring, ABI senior research director of 5G Dimitris Mavrakis said: “As Mavenir and Xilinx continue to work together to accelerate O-RAN-based massive MIMO adoption, their solutions will be well-timed to serve this high-growth market with the higher spectral efficiency, performance, power efficiency and cost needed as 5G demand intensifies.”

Specifically, the two companies are jointly developing the next generation of mMIMO products, which they say will bring the world’s first O-RAN-compliant 64TRX mMIMO products that support up to 400MHz instantaneous bandwidth in a compact form factor. Mavenir’s virtualised RAN (vRAN) software supports multi-user MIMO (MU-MIMO) with up to 16 layers, receiver algorithms and digital beamforming, all running on Mavenir’s cloud-native platform, as well as on other cloud platforms. The first mMIMO 64TRX joint solution is expected to be available in the fourth quarter of 2021.

Working together, the two companies say they have successfully completed end-to-end integration of a first-generation mMIMO solution using Open RAN principles. Held at the Mavenir lab in Bangalore, India, the integration covered multiple deployment scenarios and was evaluated by six communications service providers, all leading global operators. Mavenir delivered the vRAN support for mMIMO, including core network, centralised unit (CU) and distributed unit (DU), with Xilinx providing the category B O-RAN radio unit.

Summing up what he thought the demonstration had shown, Mavenir president and CEO Pardeep Kohli claimed that the firms had revealed an important milestone in the delivery of open and interoperable interfaces enabling the deployment of mMIMO in high-density, high-mobile-traffic metro areas.

“This integration demonstrates an efficient Open RAN massive MIMO solution to achieve diversification of the telecommunications supply chain,” he observed.

On Xilinx’s behalf, Liam Madden, executive vice-president and general manager at its wired and wireless group, said bringing together the firms’ collective technology and radio system expertise would accelerate the deployment of market-leading 5G O-RAN massive MIMO radio solutions.”

As it announced the partnership, Mavenir also revealed that it was working in close collaboration with Nvidia to launch an artificial intelligence (AI)-on-5G hyper-converged edge solution. Based on Nvidia’s hardware and software technologies, the “unique” solution is designed to enable enterprises and telcos to implement AI-on-5G applications for vertical industries – such as manufacturing, mining and healthcare – that cover Industry 4.0 applications, including industrial internet of things (IIoT), intelligent video analytics (IVA), extended reality (XR) and gaming use cases.

The use cases are said to be able to support remote and immersive collaboration, precision robotics control, people safety, intrusion detection, smart connected factories, real-time manufacturing component defect detection and proactive maintenance using near real-time analytics of sensor data.

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