Showing papers by "Wan Choi published in 2019"

UAV-Empowered Disaster-Resilient Edge Architecture for Delay-Sensitive Communication

Abstract: The 5G communication systems will enable enhanced mobile broadband, ultra-reliable low-latency, and massive connectivity services. Broadband and low-latency services are indispensable to public safety (PS) communication during natural or man-made disasters. Recently, 3GPP-LTE has emerged as a promising candidate to enable broadband PS communications. In this article, first we present six major PS-LTE enabling services and the current status of PS-LTE in 3GPP releases. Then, we discuss the spectrum bands allocated for PS-LTE in major countries by ITU. Finally, we propose a disaster resilient three-layered architecture for PS-LTE (DR-PSLTE). This architecture consists of an SDN layer to provide centralized control, UAV cloudlet layer to facilitate edge computing or to enable the emergency communication link, and a radio access layer. The proposed architecture is flexible and combines the benefits of SDNs and edge computing to efficiently meet the delay requirements of various PS-LTE services. Numerical results verified that under the proposed DR-PSLTE architecture, delay is reduced by 20 percent as compared with the

Low Latency Random Access for Sporadic MTC Devices in Internet of Things

Abstract: This paper proposes a compressed sensing-based random access protocol (CS-RACH), which is suitable for servicing a large number of machine-type communication devices in Internet of Things (IoT) network. In CS-RACH, we utilize a larger number of unique preambles compared to conventional LTE-RACH, however, the compressed sensing technique makes it possible to simultaneously detect the users with high accuracy. Compared to the user detection in conventional LTE-RACH, the proposed user detection can get rid of preamble collisions and decrease the collision probability, thereby the overall access latency is significantly reduced. To prove the benefits of the proposed CS-RACH, we mathematically analyze and compare access latency performance of LTE-RACH and CS-RACH. In particular, based on the least absolute shrinkage and selection operator approach, we derive a normalized throughput, access success probability, and average access latency. Our simulation results also exhibit that the proposed CS-RACH considerably reduces the access latency under reasonable conditions in IoT environments.

Achievable Rate-Energy Region in Two-Way Decode-and-Forward Energy Harvesting Relay Systems

Abstract: At an energy harvesting relay, securing residual harvested energy, net remaining energy after each receiving and forwarding cycle, is of importance for sustainable operation. However, there exists a tradeoff between the achievable rate and residual harvested energy, whereby understanding this tradeoff concretely is crucial for energy harvesting relay system design. This paper analyzes the rate-energy (R-E) region for achievable rate and residual harvested energy in two-way decode-and-forward (DF) relay systems with a power splitting based energy harvesting relay. In particular, we characterize R-E regions for multiple access broadcast (MABC) and time division broadcast protocols. Moreover, we propose a new energy harvesting relaying protocol, namely, information and energy signals multiple access broadcasts (IEBC), to improve the achievable R-E region. The boundary of the R-E regions is obtained by optimizing a power splitting factor in each protocol. Moreover, to have better analytic comparisons and useful insights on performance, we derive approximated R-E regions of all protocols for high and low signal-to-noise ratio cases. Based on the approximated R-E region, it is shown that if the required residual energy is large, the IEBC outperforms the others, but if the required residual energy is small, either the IEBC or MABC is preferred.

Probabilistic Caching Based on Maximum Distance Separable Code in a User-Centric Clustered Cache-Aided Wireless Network

Abstract: We investigate how the maximum-distance separable (MDS) coding can be incorporated into probabilistic caching to utilize the limited storage space efficiently. In a user-centric clustered wireless network, each caching helper node probabilistically caches a segment of MDS coded sequence of each file. The segment size is optimized to maximize the cache hit probability or successful file retrieval probability. We reveal that the best way of storing files is determined by the condition whether the average amount of MDS coded information stored for the requested file within a user’s cluster exceeds the amount required for file retrieval or not. In terms of the cache hit probability maximization, if the condition is not fulfilled, it is proved that storing the complete file with a low probability is optimal. Otherwise, storing either a segment as small as possible with a high probability or a complete file with a low probability, according to a given environment, is shown to be desirable. We also analyze the successful retrieval probability, which accounts for both a cache hit event and successful transmissions from multiple caching helper nodes. Since the successful retrieval probability is in an intractable form, to find the desirable segment size, the theoretically driven algorithms with low search complexity are developed.

Machine Learning-Based Beamforming in Two-User MISO Interference Channels

Abstract: As the demand for data rate increases, interference management becomes more important, especially in small cell environment of emerging wireless communication systems. In this paper, we investigate the machine learning-based beamforming design in two-user MISO interference channels. To see the possibilities of machine learning in beamforming design, we consider simple beamforming, where each user chooses one between two popular beamforming schemes, which are the maximum ratio transmission (MRT) beamforming and the zero-forcing (ZF) beamforming. We first propose a machine learning structure that takes transmit power and channel vectors as input and then recommends two users' choices between MRT and ZF as output. The numerical results show that our proposed machine learning-based beamforming design well finds the best beamforming combination and achieves the sum-rate more than 99.9% of the best beamforming combination.

Regulation of electron-hole recombination kinetics on uniform metal-semiconductor nanostructures for photocatalytic hydrogen evolution

Abstract: Photocatalytic hydrogen evolution has garnered considerable attention as a potential technology for the conversion of solar energy to chemical energy to replace fossil fuels with the development of hydrogen energy infrastructure. Semiconductors have been intensively studied as photocatalysts due to their tunable bandgap, eco-friendly reaction mechanism, photochemical stability, and ease of reusability. To achieve highly efficient photocatalysts, regulation of exctions, which are photoinduced electrons and holes in photocatalysts, is necessary. Semiconductor nanoparticles have been applied in this purpose because of their confined exciton pathways and differentiated catalytic characteristics depending on their size, shape, and morphology. In addition, metal cocatalysts have been decorated with semiconductor nanoparticles because the metal cocatalyst not only provides efficient shuttling of photoinduced electrons and proper reaction sites for the hydrogen evolution but also controls exciton pathways via fast electron transfer kinetics from semiconductor to metal. This research update reviews recent advances in representative metal-semiconductor hybrid nanostructures of core-shell and tipped nanorods for photocatalysts with a focus on the exciton pathways. The metal at semiconductor core-shell nanostructures has shown extraordinary photocatalytic stability via passivation of the metal by a semiconductor. In photocatalytic hydrogen evolution, the semiconductor shell hinders electron transfer to water. Hence, various core-shell related metal-semiconductor nanostructures such as yolk-shell, core-island shell, and double shell hollow structures have been proposed in efforts to overcome the electron transfer barrier to water. Metal tipped nanorods are another versatile nanostructure to control and monitor exciton pathways. The correlation between exciton pathways and photocatalytic efficiencies was demonstrated by monitoring metal tipped semiconductor nanorods with different composition, morphology, and surface structure. The insights reported here suggest a rational and versatile design strategy of metal-semiconductor hybrid nanostructures for developing highly efficient photocatalysts for hydrogen evolution.

Mobility-Aware Content Placement for Device-to-Device Caching Systems

Abstract: User mobility has a large effect on optimal content placement in device-to-device (D2D) caching networks. Since a typical user can communicate neighboring users who stay in the D2D communication area of the typical user, the optimal content placement should be changed according to the user mobility. Under consideration of randomness of incoming and outgoing users, we formulate an optimization problem to minimize the average data load of a BS. It is proved that minimization of the average data load of a BS can be transformed to maximization of a monotonic submodular function with a matroid constraint, for which a greedy algorithm can find near-optimal solutions. Moreover, when motions of neighboring users are rapid, the optimal content placement is derived in closed-form, aided by reasonable approximation and relaxation. In the high mobility regime, the optimal content placement is shown to cache partial amounts of the most popular contents.

Communication and Consensus Co-Design for Distributed, Low-Latency and Reliable Wireless Systems

Abstract: Designing distributed, fast and reliable wireless consensus protocols is instrumental in enabling mission-critical decentralized systems, such as robotic networks in the industrial Internet of Things (IIoT), drone swarms in rescue missions, and so forth. However, chasing both low-latency and reliability of consensus protocols is a challenging task. The problem is aggravated under wireless connectivity that may be slower and less reliable, compared to wired connections. To tackle this issue, we investigate fundamental relationships between consensus latency and reliability through the lens of wireless connectivity, and co-design communication and consensus protocols for low-latency and reliable decentralized systems. Specifically, we propose a novel communication-efficient distributed consensus protocol, termed Random Representative Consensus (R2C), and show its effectiveness under gossip and broadcast communication protocols. To this end, we derive a closed-form end-to-end (E2E) latency expression of the R2C that guarantees a target reliability, and compare it with a baseline consensus protocol, referred to as Referendum Consensus (RC). The result shows that the R2C is faster compared to the RC and more reliable compared when co-designed with the broadcast protocol compared to that with the gossip protocol.

Machine Learning-Based Dimension Optimization for Two-Stage Precoder in Massive MIMO Systems with Limited Feedback

Abstract: A two-stage precoder is widely considered in frequency division duplex massive multiple-input and multiple-output (MIMO) systems to resolve the channel feedback overhead problem. In massive MIMO systems, users on a network can be divided into several user groups of similar spatial antenna correlations. Using the two-stage precoder, the outer precoder reduces the channel dimensions mitigating inter-group interferences at the first stage, while the inner precoder eliminates the smaller dimensions of intra-group interferences at the second stage. In this case, the dimension of effective channel reduced by outer precoder is important as it leverages the inter-group interference, the intra-group interference, and the performance loss from the quantized channel feedback. In this paper, we propose the machine learning framework to find the optimal dimensions reduced by the outer precoder that maximizes the average sum rate, where the original problem is an NP-hard problem. Our machine learning framework considers the deep neural network, where the inputs are channel statistics, and the outputs are the effective channel dimensions after outer precoding. The numerical result shows that our proposed machine learning-based dimension optimization achieves the average sum rate comparable to the optimal performance using brute-forcing searching, which is not feasible in practice.

User-Cache Aided Transmission With Index Coding in $K$ -User Downlink Channels

Abstract: This paper considers a single-input single-output broadcast channel with receiver side information. We find the optimal clique cover index code and the optimal transmission time allocation that minimize outage probability when total transmission time is limited. As our problem is NP-hard, we first find the optimal time allocation for a given index code. Then, we describe a brute-force algorithm that finds the set of all decodable index codes and chooses the optimal one adopting the optimal time allocation. To reduce the computational complexity of the brute-force algorithm, we propose a pruning algorithm which solves the same problem using the Hasse diagram but does not harm the optimality. Our analysis reveals that the optimal index code is dependent on the channel conditions, not simply on the number of required transmissions, which implies that the index coding-channel coupling improves the outage performance. It is also shown that this claim is still valid for general scalar linear index coding. Our simulation results verify that our proposed schemes effectively reduce the outage probability compared to other reference schemes, and our pruning algorithm considerably reduces the computational complexity required for the brute-force algorithm.

Exploiting Mobility to Content Placement in D2D Caching Systems

Abstract: Mobility-aware content placement is studied for D2D caching system. In this work, user mobility is modeled in terms of relative motion of other users with respect to a typical user of interest. Due to mobility and wireless fading, the typical user may not receive all parts of the content requested. Hence, a base station (BS) is required to serve the rest parts of the content. Correspondingly, analyzing incoming and outgoing user in the D2D communication area of the typical user, we formulate a minimization problem for the average data load of a BS and then transform it to a maximization problem for a submodular function over a matroid constraint. Using submodularity, we propose a greedy algorithm which reduces the average data load of a BS near optimally.

Communication and Consensus Co-Design for Low-Latency and Reliable Industrial IoT Systems.

Abstract: Designing fast and reliable distributed consensus protocols is a key to enabling mission-critical and real-time controls of industrial Internet of Things (IIoT) nodes communicating over wireless links. However, chasing both low-latency and reliability of a consensus protocol at once is a challenging task. The problem is even aggravated under wireless connectivity that is slower and less reliable, compared to wired connections presumed in traditional consensus protocols. To tackle this issue, we investigate fundamental relationships between consensus latency and reliability under wireless connectivity, and thereby co-design communication and consensus protocols for low-latency and reliable IIoT systems. Specifically, we propose a novel communication-efficient distributed consensus protocol, termed Random Representative Consensus (R2C), and show its effectiveness under gossip and broadcast communication protocols. To this end, we derive closed-form end-to-end (E2E) latency expression of R2C that guarantees a target reliability, and compare this with a baseline consensus protocol, referred to as Referendum Consensus (RC).

Achievable Ergodic Secrecy Rate in Bursty Interference Channels With Opportunistic User Scheduling

Abstract: This paper studies secure transmissions in a bursty interference channel constructed by opportunistic user scheduling. To improve the physical layer security, we propose a signal-to-noise-plus-interference ratio (SINR) based opportunistic transmission scheme and mathematically analyze the ergodic secrecy rate per transmitter-receiver pair. In this scheme, a subset among $K$ transmitter-receiver pairs is selected opportunistically, but each transmitter determines its activation state based on limited feedback of the SINR from its designated receiver only, without any information from other transmitters. For comparison, we also analyze the ergodic secrecy rates per transmitter-receiver pair for two benchmark schemes: non-opportunistic transmission and random transmission. Using derived analytical expressions, the achievable ergodic secrecy rates per transmitter-receiver pair are obtained by optimizing the activation probability for each scheme. To solve the non-convex optimization problem, we first show that the objective function is a sum of quasi-concave functions. Then, leveraging this fact, we propose a two-level iterative algorithm based on the damped Newton method. Furthermore, asymptotic behaviors of the achievable ergodic secrecy rate per transmitter-receiver pair are analyzed to offer insights behind the mathematical expressions. Our analytical and numerical results exhibit the gain of the proposed SINR based opportunistic transmission scheme compared to the benchmark schemes.

Which One Is Better to Cache: Requested Contents or Interfering Contents?

Abstract: In wireless edge caching, it is usual to cache the contents likely to be requested. Contrary to this traditional approach, we show that under heterogeneous content preference, caching contents that have less probability to be requested but are likely to interfere can increase the achievable rate of users by exploiting interference neutralization. Optimal content placement, which maximizes the average achievable rate, is compared with two extreme content placements: most-requested and most-interfering. Optimal content placement is shown to balance caching interfering contents against caching requested contents.

Markov Chain Analysis for Compressed Sensing based Random Access in Cellular Systems

Abstract: In large scale cellular systems, it is needed to simultaneously support a massive number of users with low latency. However, in conventional cellular systems, random access performance is severely degraded as the number of users increases due to frequent collisions. Recently, compressed sensing based random access is touted as a good solution to improve random access performance of cellular systems. To investigate latency performance of compressed based random access in cellular systems, we model the random access process by a Markov chain, and derive successful access probability of users and expected access latency based on steady state analysis.

Diversity-Multiplexing Tradeoff of the Two-User X-Channel with Two Antennas

Abstract: Besides maximizing multiplexing gain, improving diversity is important for reliable communications in the presence of interference In this context, on-off switched interference alignment (IA) is investigated, where IA is intermittently utilized by switching IA on/off in order to improve diversity gain in an interference channel and hence to maximize diversity multiplexing tradeoff (DMT) This paper adopts Alamouti coding based IA for the on-off switched IA, which requires only local channel state information at the transmitter Optimizing the portion of IA utilization of the proposed scheme in closed form, we derive the achievable DMT and show that the intermittent utilization of IA with simultaneous non-unique decoding can improve DMT in the 2-user X-channel with two antennas The proposed scheme, to the best of our knowledge, surpasses any other existing schemes for the 2-user X-channel with two antennas and is closed to the ideal DMT

Analysis on User Activity in Compressed Sensing based Random Access

Abstract: In Compressed Sensing based Random Access CHannel (CS-RACH) protocol, a base station leverages compressed sensing technique to detect the active users in the cell coverage and estimate the channel gain between the users and the base station. In a real communication scenario, activity of a specific user usually varies time to time and thus can be seen as a random variable following ON/OFF distribution. Meanwhile, the performance of compressed sensing technique is dependent on the sparsity of the estimating vector, which is closely related to the user activity in CS-RACH scenario. In this perspective, we analyze the condition of the user activity for the stable operation of the protocol. Particularly, we use the least absolute shrinkage and selection operator (LASSO) approach, which gives a closed form expressions of the sparsity condition for the successful active user detection in an asymptotic manner. As a result, we obtain the condition of the user activity for stable operation of CS-RACH and verify the result with numerical simulations.

Maximizing Received Energy in Magnetic Resonance Wireless Power Transfer Using Feedback

Abstract: This paper considers the effect of variation of load resistance, as a function of the amount of charged energy in battery, in magnetic wireless power transfer. We calculate load resistance as a function of time, and show that load resistance increases as the receiver acquires energy over time. Unlike the previous studies that considered the resonance frequency of transmitter and receiver as a fixed value, so that it remains unchanged once it has been set, it is shown that the resonance frequency changes as load resistance varies. To avoid performance degradation due to the unmatched resonance frequencies between transmitter and receiver, we employ feedback from the receiver. Considering the cost of feedback, there is a trade-off between the number of feedbacks that the receiver sends and the collected energy at the receiver. For a point-to-point system model, the optimal feedback interval that maximizes the received energy is found. The numerical results show that the proposed protocol collect more energy compared to the conventional magnetic wireless power transfer systems.

On-off Switched Interference Alignment for Diversity Multiplexing Tradeoff Improvement in the 2-User X-Network With Two Antennas

Abstract: To improve diversity gain in an interference channel and hence to maximize diversity multiplexing tradeoff (DMT), we propose an on-off switched interference alignment (IA) where IA is intermittently utilized by switching IA on/off. For on-off switching, either IA with symbol extension or IA with Alamouti coding is adopted in this paper. Deriving and analyzing DMT of the proposed schemes, we reveal that the intermittent utilization of IA with simultaneous non-unique decoding can improve DMT in the 2-user X-channel with two antennas. Both proposed schemes are shown to achieve a diversity gain of 4 and DoF per user of $\frac {4}{3}$ . In particular, the on-off switched IA with Alamouti coding, to the best of our knowledge, surpasses any other existing schemes for the 2-user X-channel with two antennas and nearly approaches the ideal DMT.

Usage of Millimeter-wave and Sub-6GHz Band with Full-duplex Relays in Heterogeneous Networks: An Information-theoretic Analysis

Abstract: In this paper, we investigate the usage of millimeter- wave (mmWave) band and sub-6GHz band with respect to total transmission time as a performance metric. For mmWave communication, multi-hop relaying is considered, and it is able to operate as half-duplex of full-duplex mode. By consider simple system model, but not disregard its inherent characteristics of each band, we analytically investigate the trade-off between each band, and derive the conditions for each best-performing regime. In the end, we observe the trade-off with respect to various system parameters and our analysis is well matched with the simulation results.