arXiv subjects

Jinho Choi

Publications and source records attributed to Jinho Choi.

At least 91 records · Page 5Linked to original sources

arXiv2021

Sliding Network Coding for URLLC

In this paper, we propose a network coding (NC) based approach to ultra-reliable low-latency communication (URLLC) over erasure channels. In transmitting multiple data packets, we demonstrate that the use of random NC can improve the reliability in terms of decoding error probability, while it incurs a longer decoding delay than a well-known K-repetition. To avoid a long decoding delay, we consider a sliding NC (SNC) design that allows a highly reliable transmission of each data packet with a guaranteed decoding delay. A few design examples are derived and their decoding error rates are analyzed. Through the analysis, we can show that the decoding error rate of SNC is much lower than that of K-repetition at the same spectral efficiency, which means a more reliable transmission can be achieved using SNC than K-repetition.

cs.IT↗

arXiv2021

On Throughput Bounds of NOMA-ALOHA

In this paper, we focus on the throughput of random access with power-domain non-orthogonal multiple access (NOMA) and derive bounds on the throughput. In particular, we demonstrate that the expression for the throughput derived in [1] is an upper-bound and derive a new lower-bound as a closed-form expression. This expression allows to find the traffic intensity that maximizes the lower-bound, which is shown to be the square root of the number of power levels in NOMA. Furthermore, with this expression, for a large number of power levels, we obtain the asymptotic maximum throughput that is increased at a rate of the square root of the number of power levels.

cs.IT↗

arXiv2021

Next Generation Mega Satellite Networks: Opportunities, Challenges, and Performance

Digital connectivity has become the foundation of prosperity and an essential need for functioning societies. Despite this dependence, limitation on Internet access remains a prevalent issue, largely hinged on socioeconomic and geographic factors. A promising solution to attain global access equality is based on integrated terrestrial-satellite networks that rely on low Earth orbit (LEO) mega constellations. While the benefits of LEO constellations complement the shortcomings of terrestrial networks, their incorporation impacts the network design, adding complexity and challenges. This article presents a systematic analysis of next generation LEO mega satellite constellations and outlines opportunities by virtue of the many benefits these constellations can provide and highlights the major challenges. Furthermore, it provides a synopsis of analytic models and underscores modern simulation approaches for next generation mega satellite constellations. This provides network designers with the necessary insights into satellite network performance.

cs.IT↗

arXiv2021

Communication-Efficient Distributed SGD using Preamble-based Random Access

In this paper, we study communication-efficient distributed stochastic gradient descent (SGD) with data sets of users distributed over a certain area and communicating through wireless channels. Since the time for one iteration in the proposed approach is independent of the number of users, it is well-suited to scalable distributed SGD. Furthermore, since the proposed approach is based on preamble-based random access, which is widely adopted for machine-type communication (MTC), it can be easily employed for training models with a large number of devices in various Internet-of-Things (IoT) applications where MTC is used for their connectivity. For fading channel, we show that noncoherent combining can be used. As a result, no channel state information (CSI) estimation is required. From analysis and simulation results, we can confirm that the proposed approach is not only scalable, but also provides improved performance as the number of devices increases.

cs.IT↗

arXiv2021

Enhancing Cognitive Models of Emotions with Representation Learning

We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions that can be used to computationally describe psychological models of emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model that enables to interpret dynamically learned representations optimized for an emotion classification task. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions. Our layer analysis can derive an emotion graph to depict hierarchical relations among the emotions. Our emotion representations can be used to generate an emotion wheel directly comparable to the one from Plutchik's\LN model, and also augment the values of missing emotions in the PAD emotional state model.

cs.CL↗

arXiv2021

Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction

Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state sensor and its remote observer, respectively. This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension, but also learns the system dynamics by lifting it via a Koopman operator, thereby allowing the observer to locally predict future states after training convergence. Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.

cs.LG↗

arXiv2021

RIS-Assisted Coverage Enhancement in Millimeter-Wave Cellular Networks

The use of millimeter-wave (mmWave) bandwidth is one key enabler to achieve the high data rates in the fifth-generation (5G) cellular systems. However, mmWave signals suffer from significant path loss due to high directivity and sensitivity to blockages, limiting its adoption within small-scale deployments. To enhance the coverage of mmWave communication in 5G and beyond, it is promising to deploy a large number of reconfigurable intelligent surfaces (RISs) that passively reflect mmWave signals towards desired directions. With this motivation, in this work we study the coverage of an RIS-assisted large-scale mmWave cellular network using stochastic geometry, and derive the peak reflection power expression of an RIS and the downlink signal-to-interference ratio (SIR) coverage expression in closed forms. These analytic results clarify the effectiveness of deploying RISs in the mmWave SIR coverage enhancement, while unveiling the major role of the density ratio between active base stations (BSs) and passive RISs. Furthermore, the results show that deploying passive reflectors is as effective as equipping BSs with more active antennas in the mmWave coverage enhancement. Simulation results confirm the tightness of the closed form expressions, corroborating our major findings based on the derived expressions.

eess.SP↗

arXiv2021

Modeling RIS Empowered Outdoor-to-Indoor Communication in mmWave Cellular Networks

With the increasing adoption of millimeter-waves (mmWave) over cellular networks, outdoor-to-indoor (O2I) communication has been one of the challenging research problems due to high penetration loss of buildings. To address this, we investigate the practicability of utilizing reconfigurable intelligent surfaces (RISs) for assisting such O2I communication. We propose a new notion of prefabricated RIS-empowered wall consisting of a large number of chipless radio frequency identification (RFID) sensors. Each sensor maintains its own bank of delay lines. These sensors which are built within the building walls can potentially be controlled by a main integrated circuit (IC) to regulate the phase of impinging signals. To evaluate our idea, we develop a thorough performance analysis of the RIS-based O2I communication in the mmWave network using stochastic-geometry tools for blockage models. Our analysis facilitates two closed-form approximations of the downlink signal-to-noise ratio (SNR) coverage probability for RIS-based O2I communication. We perform extensive simulations to evaluate the accuracy of the derived expressions, thus providing new observations and findings.

eess.SP↗

arXiv2020

Performance Analysis of 2-Step Random Access with CDMA in Machine-Type Communication

There is a growing interest in the transition from 4-step random access to 2-step random access in machine-type communication (MTC), since 2-step random access is well-suited to short message delivery in various Internet of Things (IoT) applications. In this paper, we study a 2-step random access approach that uses code division multiple access (CDMA) to form multiple channels for data packet transmissions with a spreading factor less than the number of channels. As a result, the length of data transmission phase in 2-step random access can be shorter at the cost of multiuser interference. To see how the decrease of the length of data transmission phase can improve the spectral efficiency, we derive the throughput as well as the spectral efficiency. From the results of analysis, we can show that the 2-step CDMA-based random access approach can have a higher spectral efficiency than conventional 2-step approach with orthogonal channel allocations, which means that the performance of MTC can be improved by successfully transmitting more data packets per unit time using CDMA. This is also confirmed by simulation results.

cs.IT↗

arXiv2020

Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

eess.SP↗

arXiv2020

Grant-Free Random Access in Machine-Type Communication: Approaches and Challenges

Massive machine-type communication (MTC) is expected to play a key role in supporting Internet of Things (IoT) applications such as smart cities, smart factory, and connected vehicles through cellular networks. MTC is characterized by a large number of MTC devices and their sparse activities, which are difficult to be supported by conventional approaches and motivate the design of new access technologies. In particular, in the 5th generation (5G), grant-free or 2-step random access schemes are introduced for MTC to be more efficient by reducing signaling overhead. In this article, we first introduce grant-free random access and discuss how it can be modified with massive multiple-input multiple-output (MIMO) to exploit a high spatial multiplexing gain. We then explain preamble designs that can improve the performance and variations based on the notion of non-orthogonal multiple access (NOMA). Finally, design challenges of grant-free random access towards next generation cellular systems are presented.

cs.IT↗

arXiv2020

Data-aided Sensing for Gaussian Process Regression in IoT Systems

In this paper, for efficient data collection with limited bandwidth, data-aided sensing is applied to Gaussian process regression that is used to learn data sets collected from sensors in Internet-of-Things systems. We focus on the interpolation of sensors' measurements from a small number of measurements uploaded by a fraction of sensors using Gaussian process regression with data-aided sensing. Thanks to active sensor selection, it is shown that Gaussian process regression with data-aided sensing can provide a good estimate of a complete data set compared to that with random selection. With multichannel ALOHA, data-aided sensing is generalized for distributed selective uploading when sensors can have feedback of predictions of their measurements so that each sensor can decide whether or not it uploads by comparing its measurement with the predicted one. Numerical results show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing compared to conventional multichannel ALOHA with equal uploading probability.

cs.IT↗

arXiv2020

Data-aided Sensing for Distributed Detection

In this paper, we study data-aided sensing (DAS) for distributed detection in wireless sensor networks (WSNs) when sensors' measurements are correlated. In particular, we derive a node selection criterion based on the J-divergence in DAS for reliable decision subject to a decision delay constraint. Based on the proposed J-divergence based DAS, the nodes can be selected to rapidly increase the log-likelihood ratio (LLR), which leads to a reliable decision with a smaller number of the sensors that upload measurements for a shorter decision delay. From simulation results, it is confirmed that the J-divergence based DAS can provide a reliable decision with a smaller number of sensors compared to other approaches.

cs.IT↗

arXiv2020

Explore-Before-Talk: Multichannel Selection Diversity for Uplink Transmissions in Machine-Type Communication

Improving the data rate of machine-type communication (MTC) is essential in supporting emerging Internet of things (IoT) applications ranging from real-time surveillance to edge machine learning. To this end, in this paper we propose a resource allocation approach for uplink transmissions within a random access procedure in MTC by exploiting multichannel selection diversity, coined explore-before-talk (EBT). Each user in EBT first sends pilot signals through multiple channels that are initially allocated by a base station (BS) for exploration, and then the BS informs a subset of initially allocated channels that are associated with high signal-to-noise ratios (SNRs) for data packet transmission by the user while releasing the rest of the channels for other users. Consequently, EBT exploits a multichannel selection diversity gain during data packet transmission, at the cost of exploration during pilot transmission. We optimize this exploration-exploitation trade-off, by deriving closed-form mean data rate and resource outage probability expressions. Numerical results corroborate that EBT achieves a higher mean data rate while satisfying the same outage constraint, compared to a conventional MTC protocol without exploration.

cs.IT↗

arXiv2020

An Approach to Preamble Collision Reduction in Grant-Free Random Access with Massive MIMO

In this paper, we study grant-free random access with massive multiple input multiple output (MIMO) systems. We first show that the performance of massive MIMO based grant-free random access is mainly decided by the probability of preamble collision. The implication of this is that although the number of antennas can be arbitrarily large, the (average) number of successful packet transmissions can be limited by the number of preambles. Then, we propose an approach to preamble collision reduction without increasing the number of preambles using the notion of superpositioned preambles (S-preambles). Since the channel estimation for conjugate beamforming can be carried out when unused S-preambles are known, a simple approach is derived to detect unused S-preambles and its performance is analyzed for a special case (superpositions with 2 preambles). Based on analysis and simulation results, it is confirmed that the proposed approach with S-preambles can increase the success probability with the same spectral efficiency as the conventional approach.

cs.IT↗

arXiv2020

Re-Transmission Diversity Multiple Access based on SIC and HARQ-IR

We consider multiple access with re-transmission diversity (RTxD) based on a hybrid automatic request (HARQ) protocol with incremental redundancy (IR) in this paper. In order to mitigate multiple access interference (MAI), we employ successive interference cancellation (SIC) at a receiver and derive conditions that all the signals from active users can be successfully decoded within a certain number of re-transmissions of IR blocks. We consider two special cases, and based on them we derive two multichannel random access schemes that have subchannels in the power and rate domains. Through the analysis and simulations, we can show that the proposed RTxD random access schemes can outperform other existing similar RTxD random access schemes.

cs.IT↗

arXiv2020

Random Access with Layered Preambles based on NOMA for Two Different Types of Devices in MTC

In machine-type communication (MTC), random access has been employed for a number of devices and sensors to access uplink channels using a pool of preambles. To support different priorities due to various quality-of-service (QoS) requirements, random access can be generalized with multiple pools, which may result in low spectral efficiency. In this paper, for high spectral efficiency, random access with layered preambles (RALP) is proposed to support devices with two different priorities based on the notion of power-domain non-orthogonal multiple access (NOMA). In RALP, two groups of devices, namely type-1 and type-2 devices, are supported with different priorities, where type-1 devices have higher priority than type-2 devices. Closed-form expressions are derived for the detection performance of preambles transmitted by type-1 devices, which can be used for a certain performance guarantee of type-1 devices of high priority. Low-complexity preamble detection methods are also discussed.

cs.IT↗

arXiv2020

On Throughput Improvement using Immediate Re-transmission in Grant-Free Random Access with Massive MIMO

To support machine-type communication (MTC), massive multiple-input multiple-output (MIMO) has been considered for grant-free random access. In general, the performance of grant-free random access with massive MIMO is limited by the number of preambles and the number of active devices. In particular, when there are a number of active devices transmitting data packets simultaneously, the signal-to-interference-plus-noise ratio (SINR) cannot be high enough for successful decoding. In this paper, in order to improve performance, we consider immediate re-transmissions for an active device that has a low SINR although it does not experience preamble collision to exploit re-transmission diversity (RTD) gain. To see the performance of the proposed approach, we perform throughput analysis with certain approximations and assumption. Since the proposed approach can be unstable due to immediate re-transmissions, conditions for stable systems are also studied. Simulations are carried out and it is shown that analysis results reasonably match simulation results.

cs.IT↗