Performance of 5G codes over a noisy channel

Abstract

At present, the need for mobile internet keeps increasing every day, especially with the rise of IoT devices, as it's estimated that by the year 2025, there will be more than 5 billion IoT devices connected to the network. For wireless mobile communication, a huge bandwidth is needed to adapt the different rates for different applications. The 5G network will provide lower latency and also achieve higher speeds than previous networks. In 5G wireless communication, both turbo codes and tail-biting convolutional codes failed to meet 5G standards even though they proved their efficiency for the LTE standard. In 5G, a more advanced error correction method is needed for both LDPC codes and polar codes, specifically LDPC codes dealing with data channels and polar codes dealing with control channels. As error correction and detection are the main requirements for 5G wireless communication, the BER performance against the (Eb/NO) performance is really important as you don't want to lose almost any transmitted block. One of the methods used to check BER against EB/NO was to check an un-coded signal under various types of modulation, from BPSK up to 256 QAM; the higher the modulation, the worse the BER against EB/NO performance was getting. With 5G packing more data now, even higher than 256 QAM is possible. A performance test of the codes that are being used in 5G has been simulated here. As is customary, the higher the modulation, the worse the BER against EB/NO. A 5G-NR scenario has been performed using BPSK modulation with an AWGN channel to demonstrate how the codes perform under the best modulation scenario. The 5G standard has been applied to both codes as base graph 1 and base graph 2 have been used for LDPC at different code rates. The same goes for polar as channels are in sequential order from worst to best as specified in the standard. The hardware performance for 5G is very challenging, so a single decoder has been used in both codes, with quantization implemented in both of them. As a result of simulations of BER at both codes, different plots have been shown. For LDPC codes, performance iterations had a noticeable improvement in BER levels starting at 10 iterations to 20 iterations and from 20 to 30 iterations. Not a huge BER improvement was seen, so 20 iterations have been implemented as the main iteration number for most of the graphs. For LDPC codes, both base graphs were used. For rate half, with midsize block BG1, had a better performance; for rates 2/3 and 5/6, rate 2/3 had an overall better performance compared to rate 5/6, with 4096 block size providing the best results in both rates. As for polar codes, successive cancelation was implemented for 256 and 512 block sizes with different rates. The lower the block size, the better the results were obtained for polar codes.