In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices.

For the frequency division duplexing-based large-scale multiple-input multiple-output (MIMO) systems, we consider a multi-user MIMO (MU-MIMO) non-orthogonal multiple access (NOMA) scheme, referred to as beam division-NOMA (BD-NOMA), which is featured by a long-term feedback-based user grouping and clustering, along with a short-term feedback-based BD-NOMA scheduling. The performance of BD-NOMA is not warranted in practice because near users are severely interfered by strong signals for the far users of other beams, and furthermore, the long-term feedback-based user grouping and clustering might result in short-term inter-beam interference. We consider the short-term feedback-based joint optimization problem of user selection and power allocation for NOMA in each beam and system-wide power allocation for MU-MIMO, dealing with both intra-beam and inter-beam interference at the same time. We formulate a joint optimization problem for a large-scale MIMO system with the multiple channel quality indicators-based short-term feedback. In our analysis, we reformulate the original problem into an equivalent one that can be solved iteratively with the weighted minimum mean square error-based algorithm. Then, the performance analysis is presented to conclude that the proposed optimization is essential for enabling BD-NOMA to outperform beam division multiple access, especially when inter-beam interference becomes critical.

We propose superposition multiple-input multiple-output (MIMO) coding for the transmission of unequally important sources in a point-to-multipoint system. First, a tradeoff between Alamouti code and spatial multiplexing (V-BLAST) is analyzed in terms of the average bit error rate (BER), where the maximum data rates of both MIMO schemes are set to be equal. The results show that for a given target bit error rate, Alamouti code is preferable for a low data rate, and spatial multiplexing is preferable for a high data rate. For layered sources such as scalable video, the more important component typically has lower data rate than does the less important component. Based on these, we construct a superposition MIMO scheme where two different MIMO techniques are hierarchically combined such that important data is Alamouti coded, less important data is spatially multiplexed, and then two unequally important data symbols are superposed.

The 3GPP standardized the physical layer specification in 5G New Radio to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) coexistence in usage scenarios including aerial vehicles (AVs). Dynamic multiplexing of URLLC traffic was standardized to increase the outage capacity. DM allocates a fully overlapped bandwidth part (BWP) of eMBB and URLLC AVs to perform the immediate scheduling of URLLC traffic by puncturing ongoing eMBB traffic. However, DM often suffers from a significant frame error incurred by puncturing. Meanwhile, BWP can be sliced orthogonally for eMBB and URLLC AVs, possibly preventing overdimensioning the resources depending on the eMBB and URLLC traffic loads. In this paper, we propose a dynamic BWP allocation scheme that switches between two multiplexing methods, dynamic multiplexing (DM) and orthogonal slicing (OS), so as to minimize an impact of uRLLC traffic on eMBB traffic. To implement efficient BWP allocation, the capacity region is analyzed by considering the effect of physical layer parameters, such as modulation and coding scheme (MCS) levels and code block group size on DM and OS. OS is effective for improving the eMBB throughput under a URLLC latency constraint for deterministic and predictable URLLC traffic, whereas DM has limited error-correcting capability against the URLLC’s puncturing effect. The relative MCS level of eMBB and URLLC is critical in determining the eMBB traffic tolerance against puncturing. Identifying the performance tradeoff between DM and OS, the tolerance level is quantified by a URLLC load threshold. It is given in an approximate closed form, which is an essential reference for selecting DM over OS, enabling dynamic BWP allocation for the URLLC AV.

In 5G cellular communication systems, a new type of simulation methodology may be required due to the emergence of new services and applications, new environments and requirements, new performance indicators, and increased need for accurate end-toend performance measurements of systems including wired and wireless sections. In this paper, we address the characteristics of 5G simulations compared with 4G simulations especially focusing on system level simulation. Considering the characteristics of 5G simulations, the requirements for 5G simulators can be derived. In order to fulfill the requirements, a system level simulator is currently being developed and some selected features of the simulator are presented.

In this paper, we propose a method to determine the optimal cyclic delay value of cyclic delay diversity (CDD) in orthogonal frequency division multiplexing (OFDM) systems. As the cyclic delay value increases, we can get signal to interference and noise ratio (SINR) gain by the diversity effect, while SINR loss increases because of channel estimation errors. If the optimal cyclic delay value obtained by the proposed method is applied to a CDD scheme, we can minimize the required SINR for a given frame error rate (FER) under the above-mentioned trade-off.