In the future, 5G radio access and support for the internet of things (IoT) is becoming more important, which is called machine type communications. Different from current mobile communication systems, machine type communications generates relatively small packets. In order to support such small packets with high reliability, channel coding techniques are inevitable. One of the most effective channel codes in such conditions is the tail-biting convolutional code, since it is used in LTE systems due to its good performance for small packet sizes. By employing a list Viterbi algorithm for the tail-biting convolutional code, the block error rate (BLER) performances is further improved. Therefore, this paper evaluates the BLER performances of several list Viterbi algorithms, i.e., circular parallel list Viterbi algorithm (CPLVA), per stage CPLVA (PSCPLVA), and successive state and sequence estimation (SSSE). In the evaluation, computational complexity is also taken into account. It is shown that the performance of the CPLVA is better in the wide range of computational complexity defined in this paper.

To support the rapid increase of mobile traffic, the LTE-based air interface is expected to be employed in the unlicensed spectrum known as “Licensed-Assisted Access (LAA).” The LAA terminal, which employs an LTE-based air interface, suffers from interference from WiFi access points as well as the LAA base station. The interference rejection combining (IRC) receiver, which employs a linear minimum mean square error (MMSE) filter, can suppress this interference from WiFi access points in addition to that of the LAA base station. The IRC receiver is effective, since it requires no knowledge of the interference, which is generally difficult to obtain for different systems. In this paper, we use a link-level simulation to evaluate the performance of the IRC receiver in suppressing the interference from WiFi access points, and show that the IRC receiver can effectively cancel the interference from WiFi systems as well as LTE systems, although we observed a slight performance degradation due to the covariance matrix estimation error caused by the WiFi interference fluctuation in the frequency-domain.