In massive MIMO systems, there are issues such as increased hardware cost and power consumption. In this paper, we consider simplifying the entire system by quantizing the elements of MMSE weights and estimated channel matrices to four values. We compared the characteristics of the simplified MMSE with those of the direct-binary search method, which has been conventionally used as a weight generation method, and we showed that the simplified MMSE is more effective.
This paper presents the design, fabrication, and testing of a planar 2x2 sub-array antenna for satellite communication receiver applications in the Ka-band. The unit element of the sub-array is a aperture-coupled square patch designed to operate in the 19.2GHz - 21.2GHz frequency band. Each element support dual-circular polarization, enabling simultaneous reception of Left-Hand Circularly Polarized (LHCP) and RightHand Circularly Polarized (RHCP) signals. The multi-layered structure realized using conventional 10-layered printed circuit board (PCB) fabrication technology, allows for the integration of the planar hybrid coupler within the unit element as well as the integration of a 4-way reactive power combiner within the 2x2 sub-array. The return loss and the pattern measurements validate the full-wave electromangetic simulation-assisted design and shows the sub-array's suitability use in larger antenna array for in satellite communication receivers in the Ka-band.
This paper presents a range of simulation approaches relevant to the development of Non-Terrestrial Networks (NTN). Challenges specific to NTN—such as mobility, wide-area coverage, and atmospheric effects—require simulation techniques tailored to each scenario. Key use cases include satellite electromagnetic modeling, propagation analysis for LEO and UAVs, and rain attenuation evaluation.
High-frequency communications and space/air networks are needed to provide fast transmission rates with ubiquitous connections. The complicating factor, however, is that high frequencies are susceptible to high attenuation caused by adverse weather, such as clouds and rain. Given that weather is highly dynamic but follows predictable patterns, the use of Machine Learning to predict network state and thus proactively control connections is a promising network management method in these scenarios. To train these Machine Learning models, extensive datasets are needed. Unfortunately, existing data have either low time resolution (which does not sufficiently capture the fast changes in weather) or low space resolution (which is biased by focusing on limited locations). To address this, we propose two dataset generation methods that generate high-resolution data, which is still based on real-life measurements. Performance evaluation shows that generative artificial intelligence cannot properly capture the real-life variations in weather, but our proposed heuristics are capable of creating a dataset that follows realistic patterns and that can be used to control space/air wireless networks to improve downtime and throughput when compared to using existing datasets.
This paper proposes a method for resolving the delay ambiguity in orthogonal time frequency space (OTFS)-based sensing systems. In conventional OTFS-based sensing systems, the delay ambiguity occurs when the propagation delay from a target exceeds the delay period defined by the Zak transform. To address this problem, we exploit phase signatures by placing two pilot symbols at different Doppler bins in the transmitted signal and computing their phase difference from the received signal. Since the phase difference reflects how many times the propagation delay exceeds the delay period, we resolve the delay ambiguity by analyzing it. We evaluate the proposed method through the simulation of the OTFS-based sensing system under various signal-to-noise ratio (SNR) values in a single-target scenario. The result shows that the method resolves the delay ambiguity with 100% accuracy when the SNR exceeds 25 dB. These findings demonstrate that the proposed method provides a reliable and computationally efficient solution to the delay ambiguity in OTFS-based sensing systems.
