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In this paper, we propose deep learning-based channel estimation and pilot reduction for mmWave point-to-point multi-input multi-output systems. The proposed scheme consists of a two-step approach where the first step is applying a denoising autoencoder for channel estimation. With the denoising characteristic of autoencoder, sparse channel estimation can be conducted although the orthogonality of pilot sequences is not guaranteed due to shorter pilots. The second step is exploiting the temporal correlation of the channel, using the previous estimate to extract information for the current estimate. Through simulation, the proposed scheme shows superior performance with reduced pilots.

Cloud Radio Access Network (C-RAN) refers to the virtualization of base station functionalities by means of cloud computing. With centralized processing at the cloud, the amount of hardware and infrastructure in a network can be reduced and it allows for operators to save expenses needed to deploy and maintain the network. Along with high spectral and energy efficiency, C-RAN becomes a key technology evolving to 5G network and it is also expected to play a critical role in the next generation of wireless network. In this paper, theoretical expressions are derived for throughput performance at Medium Access Control (MAC) layer with and without Cloud Radio Random-Access (CRRA). We first develop a CRRA protocol by accounting for the Multi-Packet Reception (MPR) capabilities afforded thanks to the deployment of C-RAN. We then analyze the throughput performance based on error exponent analysis. Numerical results show the performance advantages of C-RAN and verify theoretical expressions.

The unmanned aerial vehicle (UAV)-aided free-space optical/radio frequency (FSO/RF) technique has recently attracted significant attention, which offers new possibilities thanks to its dynamic deployment capability. However, many researchers have not considered the fading characteristic of both RF and FSO links and the limited UAVs' size, weight, and power (SWAP) simultaneously. This paper focuses on a mixed FSO/RF system that facilitates the communication between a base station and mobile stations via a hovering UAV acting as a decode-and-forward relay. Considering the fading effects and the power amplifier (PA) non-linearity caused by the SWAP constraint, we mathematically analyze the closed-form and asymptotic outage probabilities. Especially in high transmit power regions, we present the destructive impact of nonlinear PAs on the outage probability and investigate modulation schemes to mitigate this degradation. The derived outage expressions match well with numerical results. Moreover, we propose a joint placement and transmit power optimization algorithm that minimizes the end-to-end outage probability with round-robin and absolute signal-to-noise ratio-based scheduling schemes. Finally, simulation results show that the performance of the proposed algorithm is comparable to that of the brute-force method.

This paper proposes a robust symbol timing synchronization scheme for return link initial access based on the Digital Video Broadcasting-Return Channel via Satellite 2nd generation (DVB-RCS2) system for the Low Earth Orbit (LEO) satellite channel. In most cases, the feedforward estimator structure is considered for implementing Time Division Multiple Access (TDMA) packet demodulators such as the DVB-RCS2 system. More specifically, the Non-Data-Aided (NDA) approach, without using any kind of preamble, pilot, and postamble symbols, is applicable for fine symbol timing synchronization. However, it hinders the improvement in estimation accuracy, especially when dealing with short packet lengths during the initial access from the User Terminal (UT) to the Gateway (GW). Moreover, when a UT sends a short random access packet for initial access or resource request to the LEO satellite channel, the conventional schemes suffer from a large Doppler error depending on UT’s location in a beam and satellite velocity. To ameliorate these problems, we propose a novel symbol timing synchronization algorithm for GW, and its advantage is confirmed through computer simulation.

NAND flash memory is becoming smaller and denser to have a larger storage capacity as technologies related to fine processes are developed. As a side effect of high-density integration, the memory can be vulnerable to circuit-level noise such as random telegraph noise, decreasing the reliability of the memory. Therefore, low-density parity-check code that provides multiple decoding modes is adopted in the NAND flash memory systems to have a strong error correcting capability. Conventional static error recovery flow (ERF) applies decoding modes sequentially, and read latency can increase when preceding decoding modes fail. In this paper, we consider a dynamic ERF using machine learning (ML) that predicts an optimal decoding mode guaranteeing successful decoding and minimum read latency and applies it directly to reduce read latency. Due to process variation incurred in the manufacturing of memory, memory characteristics are different by chips and it becomes difficult to apply a trained prediction model to different chips. Training the customized prediction model at each memory chip is impractical because the computational burden of training is heavy, and a large number of training data is required. Therefore, we consider ERF prediction based on reusable ML to deal with varying input and output relationships by chips due to process variation. Reusable ML methods reuse pre-trained model architecture or knowledge learned from source tasks to adapt the model to perform its task without any loss of performance in different chips. We adopt two reusable ML approaches for ERF prediction based on transfer learning and meta learning. Transfer learning method reuses the pre-trained model by reducing domain shift between a source chip and a target chip using a domain adaptation algorithm. On the other hand, meta learning method learns shared features from multiple source chips during the meta training procedure. Next, the meta-trained model reuses previously learned knowledge to fastly adapt to the different chips. Numerical results validate the advantages of the proposed methods with high prediction accuracy in multiple chips. In addition, the proposed ERF prediction based on transfer and meta learning can yield a noticeable reduction in average read latency as compared to conventional schemes.

In millimeter wave communication, the beamforming technique with accurate angle information plays a key role to overcome the high path-loss and mitigate the interference. Particularly with multiple mobile stations (MSs), accurate multi-beam tracking without any knowledge of dynamic model is challenging. In this regard, we propose the model-free multi-beam tracking algorithm combining the Q-learning with auxiliary beam pair-based angle estimation in multi-MS environment. The proposed scheme benefits from low pilot overhead and high-resolution angle estimation. Simulation results show that the proposed scheme outperforms the conventional schemes in terms of the effective sum-rate.

In this paper, we study a waveform design based on time-reversal (TR) for multi-user wireless power transfer (WPT) systems in multipath channels. The existing waveforms for WPT using the non-linear energy harvesting (EH) model have been designed in the frequency domain, whereas we design the waveform directly in the time domain by modifying TR to be suitable for WPT. In the non-linear EH model, the peak signal with the largest magnitude among the received signals is responsible for most of the harvested energy at the energy receiver (ER). Since the peak signal of TR for multi-user consists of the sum of the inter-user interference (IUI) signal and the desired signal, the phase difference between these signals can reduce the magnitude of the peak signal. Thus, we propose a novel waveform based on TR, called a phase aligned TR (PATR). The objective of the proposed PATR is to increase the harvested energy by aligning the phase between the desired signal and IUI signal as in-phase. We derive the optimal phase set to be pre-rotated for maximizing the peak signal, and design the PATR waveform by combining the modified TR and the derived optimal phase set. We prove the superior performance of the proposed PATR compared to the existing schemes in the multi-user WPT systems with the non-linear EH model by simulation. Moreover, we show that the gain for the harvested energy of the proposed PATR increases as the number of transmit antennas and the number of ERs increase.

For orthogonal frequency division multiplexing (OFDM) systems, the carrier frequency offset (CFO) estimation is required for the frequency synchronization before the data reception. When 1-bit ADCs are employed in OFDM systems, the available information is limited to the sign of the received signal, and the acquisition of the CFO is not easy problem. To overcome this limitation, we utilize the extended preamble and construct the bank of the CFO estimators based on auto-correlation (AC) taking the samples from the preamble with flexible intervals, which produces the candidates for CFO estimate. We propose the CFO estimation algorithms using the normalized squared errors (NSEs) as the concept to check the quality of the candidates of CFO estimate. The simulation results show that the proposed algorithm works well in the multipath fading channel, and is superior to the conventional CFO estimation method based on AC with fixed interval.

To achieve a low error rate of NAND flash memory, reliable reference voltages should be updated based on the accurate knowledge of program/erase (P/E) cycles and retention time, because those severely distort the threshold voltage distribution of memory cell. Due to the sensitivity to the temperature, however, a flash memory controller is unable to acquire the exact knowledge of retention time, meaning that it is challenging to estimate accurate read reference voltages in practice. In this article, we propose a novel machine-learning-based read reference voltage estimation framework for the NAND flash memory systems without the knowledge of retention time. To establish an unknown input-output relation of the estimation model, we derive input features by sensing and decoding memory cells in the minimum read unit. In order to define the relation between unlabeled input features and a pre-assigned class label, namely label read reference voltages, we propose three mapping functions: 1) k -nearest neighbors-based, 2) nearest-centroid-based, and 3) polynomial regression-based read reference voltage estimators. For the proposed estimation schemes, we analyze that the storage overhead and computational complexity are increasing function of the exploited feature dimension. Accordingly, we propose a feature selection (or dimension reduction) algorithm to select the minimum dimension and corresponding features to reduce the overhead and complexity while maintaining high estimation accuracy. Based on extensive numerical analysis, we validate that the derived features successfully replace unknown knowledge of retention time, and the proposed feature selection algorithm precisely adjusts the trade-off between overhead/complexity and estimation accuracy. Furthermore, the simulation and analysis results show that the proposed framework not only outperforms the conventional estimation schemes but also achieves the near-optimal frame error rate while sustaining low latency performance.

In conventional maximum distance separable (MDS) coded caching systems, storage-efficient caching strategy which minimizes unnecessarily redundant data is believed to be optimal. However, the conventional storage efficient caching strategy may not be optimal because redundantly stored data can be utilized as diversity sources to combat wireless channel impairments due to channel fading. This paper explores how MDS coded caching facilitates coordinated maximum ratio transmission (MRT) in cache enabled wireless networks. Optimizing the length of MDS coded sequence and the amount of redundant cached data, we demonstrate the gain of MDS coded caching leveraged by MRT outperforms conventional uncoded caching schemes. Keywords: Wireless caching, Coded caching, Maximum distance separable code, Maximum ratio transmission

Abstract In multi-user beamforming systems, the inter-user interference are controlled by spatial filtering. Since the implementation of digital beamforming is difficult due to high hardware cost of large array system, the analog beamforming system with one phase shifter at each antenna element is considered with antenna selection scheme. However, the beamwidth is not sufficiently narrow to perfectly remove the interference due to limited size of antenna arrays. To mitigate the interference, we propose the random antenna selection system in millimeter wave channel. The random antenna selection expands the effective array size so that the beamwidth becomes narrow. We compare the beamwidth of conventional fixed antenna selection with random antenna selection and analyze the amount of interference for each selection scheme. Theoretical analysis and bit error rate simulation results indicate that the beamforming with random antenna selection can take advantage of large array.

Internet of Things (IoT) devices are likely to be developed by using different technologies and standards. Such IoT devices are being deployed in large numbers in various domains; thus, collaboration between standards bodies to provide interoperability is a key to the success of IoT. This paper describes a recent effort in oneM2M to broaden the IoT ecosystem. Semanticsenabled IoT platforms allow IoT devices to understand the meaning of IoT data in a standard way. oneM2M interoperability specifications guarantee that IoT devices from different vendors can communicate with each other. Additionally, an interworking framework bridges different IoT technologies.