User Stories

DOCOMO Beijing Labs Accelerates the Development of Mobile Communications Technology


Research, develop, and verify next-generation mobile communications technologies


Use MATLAB and Parallel Computing Toolbox to accelerate the development and simulation of innovative algorithms at the link level and the system level


  • Development time halved
  • Simulation time reduced from weeks to hours
  • Five times more scenarios verified

“With MATLAB we spend less time coding and more time developing innovative mobile communications algorithms. More importantly, with only minor modifications we can accelerate the simulation of algorithms on our computing cluster to thoroughly evaluate and verify them under a wide range of operating conditions and scenarios.”

Lead research engineer, DOCOMO Beijing Labs
User interface for DOCOMO Beijing Labs’ system-level simulator.

DOCOMO Beijing Communications Laboratories Co., Ltd., researches and develops advanced wireless communications technologies for 4G and beyond 4G (B4G) mobile networks. These technologies include physical layer signal processing methods and MIMO systems designed to increase system capacity in base stations and mobile devices for future cellular networks.

Researchers and engineers at DOCOMO Beijing Labs use MATLAB® to explore new ideas and theories, develop algorithms and systems, and functionally verify their designs. The researchers test design performance and robustness via link-level and system-level simulations. By accelerating these simulations with Parallel Computing Toolbox and MATLAB Parallel Server, DOCOMO Beijing Labs has cut development time in half while enabling its researchers to verify about five times more test cases, parameter settings, and operating scenarios than was previously possible.


Many of the algorithms developed at DOCOMO Beijing Labs involve complex processes and computationally intensive operations, including statistical signal processing, channel encoding and decoding, and complex operations on large matrices. The researchers sought to avoid programming these algorithms in a low-level language such as C or C++. With these languages, the team would spend an inordinate amount of time coding, searching for libraries, debugging, and plotting results.

Because the wireless systems DOCOMO Beijing Labs develops are highly complex, their system performance and robustness cannot be validated analytically. Instead, they rely on Monte Carlo simulations that test a range of scenarios and parameter values, including different network layouts, channel models, modulation orders, channel coding rates, and interference levels.

System-level simulations typically involve dozens of base stations and hundreds of devices. Running these simulations on a single computer took weeks. The researchers’ initial attempts at running them on a cluster involved manually distributing the jobs to different systems and then collecting and aggregating the results. The process was time-consuming and error-prone.


DOCOMO Beijing Labs used MATLAB and companion toolboxes to accelerate the exploration and development of sophisticated communications algorithms, which they simulated on a computing cluster using Parallel Computing Toolbox and MATLAB Parallel Server.

Researchers used MATLAB to interactively explore algorithm ideas and visualize the results of complex computations.

They developed a complete transmit-and-receive chain using MATLAB, filtering functions in Signal Processing Toolbox, and modulation, demodulation, encoding, and decoding functions in Communications Toolbox.

The researchers used this chain as a simulation framework for verifying advanced algorithms developed in MATLAB. For example, when developing a new modulation scheme for a next-generation device, they replaced calls to the modulator and demodulator functions in Signal Processing Toolbox with calls to their new custom functions.

The team then ran link-level simulations in MATLAB to verify the algorithm’s functionality and its robustness in the presence of channel noise.

After characterizing device-to-device performance, researchers developed system-level models that comprise multiple base stations and hundreds of mobile devices.

Using Parallel Computing Toolbox, the researchers accelerated simulations by executing multiple tasks concurrently on a multicore processor.

Requiring just a few minor modifications to the algorithm, this step verified the parallel version of the algorithm on a single computer in preparation for deployment on the lab’s 32-core computing cluster.

With MATLAB Parallel Server, the researchers performed numerous Monte Carlo simulations on the cluster to obtain bit error rate, block error rate, system throughput, outage percentages, and other statistics. These simulations enabled the team to compare the performance of different algorithms, assess robustness in poor channel conditions, and evaluate throughput across the network and at the cell edge.

DOCOMO Beijing Labs recently demonstrated a hardware implementation of two designs developed and verified using MATLAB: an 8x8 MIMO OFDM system and a multiuser MIMO for TD-LTE.


  • Development time halved. DOCOMO Beijing Labs researchers estimate that using MATLAB reduced development time by 50% compared with traditional C or C++ development. Instead of spending time on low-level coding details, the researchers can use high-level communications systems functions to rapidly and interactively develop new algorithms.

  • Simulation time reduced from weeks to hours. When executed on a single processor, some of the lab’s extensive simulations took weeks to complete. With Parallel Computing Toolbox and MATLAB Parallel Server, the same simulations were run on the lab’s 32-core cluster and completed within hours.

  • Five times more scenarios verified. In the past, time constraints forced researchers to omit some tests and simulation scenarios. With the time savings from rapid algorithm development in MATLAB and distributed simulations on the cluster, the lab has increased the number of simulations it runs by a factor of five, giving the researchers increased confidence in the robustness of their designs.

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