News Feature | March 10, 2017

KAUST Develops 5G Signal Optimization Algorithm

By Jof Enriquez
Follow me on Twitter @jofenriq

5G Mobile Network

Researchers at Saudi Arabia's King Abdullah University of Science and Technology (KAUST) have developed an efficient wireless signal optimization algorithm that will help realize the full implementation of massive MIMO (multiple input, multiple output) antenna technology for 5th generation (5G) roll-outs.

Massive MIMO is considered an enabling technology for next-generation 5G networks to accommodate more users while achieving high performance signal processing for large-scale multiple antennas. Current MIMO implementations, however, fail to approach their full potential for higher data transmission because existing signal decoding schemes in use for MIMO are computationally intensive, resulting in transmission delays, thereby failing to live up to the promise of the always-on, real-time connectivity required by 5G applications and use cases.

As a solution, KAUST researchers Mohamed-Slim Alouini, Professor of Electrical Engineering at the University, and Professor David Keyes, of the University's Extreme Computing Research Center, have developed a new algorithm called spherical decoding that could make signal transmission more efficient.

"The complexity of spherical decoding can be much lower than that of "brute force" decoding methods, but its latency was still too high in huge MIMO networks and there was no systematic way to optimize the search parameters," explained Keyes in a news release. "We demonstrated analytically and via exhaustive simulations that the search sphere can be tuned to achieve the best complexity-performance tradeoff."

The KAUST team found out that latency under the spherical decoding scheme improved further when combined with parallel computation and optimization for modern graphics processors.

As they explain in Science Direct, "We redesign the nonlinear sphere decoder method to increase the performance of the system, cast most memory-bound computations into compute-bound operations to reduce the overall complexity, and maintain the real-time processing thanks to the graphics processing unit (GPU) computational power."

After these adjustments, the team claims to have achieved a latency of less than 10 milliseconds, which is required for mobile communications, and a low-bit error rate at a signal power more than 10 times lower than that of other schemes. Their algorithm could make future massive MIMO wireless systems more viable than currently achieved, according to the researchers.

"This could help the future deployment of large antenna systems that can offer high-spectral efficiency and low-bit-error rates, while having low-decoding complexity," said Keyes. "This problem is of crucial interest in wireless communication and has considerable commercial potential among multimedia wireless service providers."