arXiv subjects

Tho Le-Ngoc

Publications and source records attributed to Tho Le-Ngoc.

35 records · Page 2Linked to original sources

arXiv2022

Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems

We propose joint user association, channel assignment and power allocation for mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based on multi-connectivity and reinforcement learning. The mobile robots require control messages from the central guidance system at regular intervals. We use a two-phase communication scheme where robots can form multiple clusters. The robots in a cluster are close to each other and can have reliable Device-to-Device (D2D) communications. In Phase I, the APs transmit the combined payload of a cluster to the cluster leader within a latency constraint. The cluster leader broadcasts this message to its members in Phase II. We develop a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for joint user association and resource allocation (RA) for Phase I. The cluster leaders use their local Channel State Information (CSI) to decide the APs for connection along with the sub-band and power level. The cluster leaders utilize multi-connectivity to connect to multiple APs to increase their reliability. The objective is to maximize the successful payload delivery probability for all robots. Illustrative simulation results indicate that the proposed scheme can approach the performance of the centralized algorithm and offer a substantial gain in reliability as compared to single-connectivity (when cluster leaders are able to connect to 1 AP).

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arXiv2022

RIS-Aided Angular-Based Hybrid Beamforming Design in mmWave Massive MIMO Systems

This paper proposes a reconfigurable intelligent surface (RIS)-aided and angular-based hybrid beamforming (AB-HBF) technique for the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The proposed RIS-AB-HBF architecture consists of three stages: (i) RF beamformer, (ii) baseband (BB) precoder/combiner, and (iii) RIS phase shift design. First, in order to reduce the number of RF chains and the channel estimation overhead, RF beamformers are designed based on the 3D geometry-based mmWave channel model using slow time-varying angular parameters of the channel. Second, a BB precoder/combiner is designed by exploiting the reduced-size effective channel seen from the BB stages. Then, the phase shifts of the RIS are adjusted to maximize the achievable rate of the system via the nature-inspired particle swarm optimization (PSO) algorithm. Illustrative simulation results demonstrate that the use of RISs in the AB-HBF systems has the potential to provide more promising advantages in terms of reliability and flexibility in system design.

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arXiv2022

Nature-Inspired Intelligent α-Fair Hybrid Precoding in Multiuser Massive Multiple-Input Multiple-Output Systems

This paper proposes a novel nature-inspired $α$-fair hybrid precoding (NI-$α$HP) technique for millimeter-wave multi-user massive multiple-input multiple-output systems. Unlike the existing HP literature, we propose to apply $α$-fairness for maintaining various fairness expectations (e.g., sum-rate maximization, proportional fairness, max-min fairness, etc.). After developing the analog RF beamformer via slow time-varying angular information, the digital baseband (BB) precoder is designed via the reduced-dimensional effective channel matrix seen from the BB-stage. For the $α$-fairness, we derive the optimal digital BB precoder expression with a set of parameters, where optimizing them is an NP-hard problem. Hence, we efficiently optimize the parameters in the digital BB precoder via five nature-inspired intelligent algorithms. Numerical results present that when the sum-rate maximization is the target, the proposed NI-$α$HP technique greatly improves the sum-rate capacity and energy-efficiency performance compared to other benchmarks. Moreover, NI-$α$HP supports different fairness expectations and reduces the rate gap among UEs by varying the fairness level ($α$).

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arXiv2022

Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of high user mobility in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a multi-agent reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, acting as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the sum-rate of V2N links and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm provides a close performance compared to that of the well-known exhaustive search algorithm.

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arXiv2022

Energy-Efficient Throughput Maximization in mmWave MU-Massive-MIMO-OFDM: Genetic Algorithm based Resource Allocation

This paper develops a new genetic algorithm based resource allocation (GA-RA) technique for energy-efficient throughout maximization in multi-user massive multiple-input multiple-output (MU-mMIMO) systems using orthogonal frequency division multiplexing (OFDM) based transmission. We employ a hybrid precoding (HP) architecture with three stages: (i) radio frequency (RF) beamformer, (ii) baseband (BB) precoder, (iii) resource allocation (RA) block. First, a single RF beamformer block is built for all subcarriers via the slow time-varying angle-of-departure (AoD) information. For enhancing the energy efficiency, the RF beamformer aims to reduce the hardware cost/complexity and total power consumption via a low number of RF chains. Afterwards, the reduced-size effective channel state information (CSI) is utilized in the design of a distinct BB precoder and RA block for each subcarrier. The BB precoder is developed via regularized zero-forcing technique. Finally, the RA block is built via the proposed GA-RA technique for throughput maximization by allocating the power and subcarrier resources. The illustrative results show that the throughput performance in the MU-mMIMO-OFDM systems is greatly enhanced via the proposed GA-RA technique compared to both equal RA (EQ-RA) and particle swarm optimization based RA (PSO-RA). Moreover, the performance gain ratio increases with the increasing number of subcarriers, particularly for low transmission powers.

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arXiv2022

Deep Learning based Multi-User Power Allocation and Hybrid Precoding in Massive MIMO Systems

This paper proposes a deep learning based power allocation (DL-PA) and hybrid precoding technique for multiuser massive multiple-input multiple-output (MU-mMIMO) systems. We first utilize an angular-based hybrid precoding technique for reducing the number of RF chains and channel estimation overhead. Then, we develop the DL-PA algorithm via a fully-connected deep neural network (DNN). DL-PA has two phases: (i) offline supervised learning with the optimal allocated powers obtained by particle swarm optimization based PA (PSO-PA) algorithm, (ii) online power prediction by the trained DNN. In comparison to the computationally expensive PSO-PA, it is shown that DL-PA greatly reduces the runtime by 98.6%-99.9%, while closely achieving the optimal sum-rate capacity. It makes DL-PA a promising algorithm for the real-time online applications in MU-mMIMO systems.

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arXiv2022

Full-Duplex Non-Coherent Communications for Massive MIMO Systems with Analog Beamforming

In this paper, a novel full-duplex non-coherent (FD-NC) transmission scheme is developed for massive multiple-input multiple-output (mMIMO) systems using analog beamforming (ABF). We propose to use a structured Grassmannian constellation for the non-coherent communications that does not require channel estimation. Then, we design the transmit and receive ABF via the slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information, respectively. The ABF design targets maximizing the intended signal power while suppressing the strong self-interference (SI) occurred in the FD transmission. Also, the proposed ABF technique only needs a single transmit and receive RF chain to support large antenna arrays, thus, it reduces hardware cost/complexity in the mMIMO systems. It is shown that the proposed FD-NC offers a great improvement in bit error rate (BER) in comparison to both half-duplex non-coherent (HD-NC) and HD coherent schemes. We also observe that the proposed FD-NC both reduces the error floor resulted from the residual SI in FD transmission, and provides lower BER compared to the FD coherent transmission.

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arXiv2022

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16x16=256 antennas and 22 RF chains. Illustrative results indicate that 91.4% online CSI can be reduced by using the proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8% as compared to the computationally expensive ray tracing.

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arXiv2021

Full-Duplex mmWave Massive MIMO Systems: A Joint Hybrid Precoding/Combining and Self-Interference Cancellation Design

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered as one of the primary candidates for the fifth generation (5G) and beyond 5G wireless communication networks to satisfy the ever-increasing capacity demands. Full-duplex technology can further enhance the advantages of mmWave massive MIMO systems. However, strong self-interference (SI) is the major limiting factor in full-duplex technology. Hence, this paper proposes a novel angular-based joint hybrid precoding/combining (AB-JHPC) technique for the full-duplex mmWave massive-MIMO systems. Our primary goals are listed as: (i) improving the self-interference cancellation (SIC), (ii) increasing the intended signal power, (iii) decreasing the channel estimation overhead, (iv) designing the massive MIMO systems with a low number of RF chains. First, the RF-stage of AB-JHPC is developed via slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information. A joint transmit/receive RF beamformer design is proposed for covering (excluding) the AoD/AoA support of intended (SI) channel. Second, the BB-stage of AB-JHPC is constructed via the reduced-size effective intended channel. After using the well-known singular value decomposition(SVD) approach at the BB-stage, we also propose a new semi-blind minimum mean square error (S-MMSE) technique to further suppress the residual SI power by using AoD/AoA parameters. The numerical results demonstrate that the SI signal is remarkably canceled via the proposed AB-JHPC technique. It is shown that AB-JHPC achieves 85.7 dB SIC and the total amount of SIC almost linearly increases via antenna isolation techniques. We observe that the proposed full-duplex mmWave massive MIMO systems double the achievable rate capacity compared to its half-duplex counterpart as the antenna array size increases and the transmit/receive antenna isolation improves.

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arXiv2020

Underlaid FD D2D Communications in Massive MIMO Systems via Joint Beamforming and Power Allocation

This paper studies the benefits of incorporating underlaid full-duplex (FD) device-to-device (D2D) communications into massive multiple-input-multiple-output (MIMO) downlink systems. Due to the nature of cellular downlink and FD D2D transmission, the performances of cellular and D2D services are severely impaired due to high interference caused by base-stations (BSs) and D2D transceivers. As a consequence, integrating a large number of D2D links into exiting cellular networks might degrade the system performances. To overcome this challenge, utilizing the large uniform linear array (ULA) equipped at BSs, we propose a joint beamforming and power allocation design for average sum-rate maximization while considering the effects of interference to both cellular and D2D transmission. The problem formulation leads to a nonconvex vector-variable optimization problem, where we develop an efficient solution using a fractional programming (FP) based approach. Numerical results show that, at sufficiently high self-interference cancellation (SIC) levels and numbers of active D2D links, the FD D2D transmission provides a significant sum-rate improvement as compared to the half-duplex (HD) counterpart and pure cellular systems in absence of D2D.

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arXiv2016

Optimal Dynamic Point Selection for Power Minimization in Multiuser Downlink CoMP

This paper examines a CoMP system where multiple base-stations (BS) employ coordinated beamforming to serve multiple mobile-stations (MS). Under the dynamic point selection mode, each MS can be assigned to only one BS at any time. This work then presents a solution framework to optimize the BS associations and coordinated beamformers for all MSs. With target signal-to-interference-plus-noise ratios at the MSs, the design objective is to minimize either the weighted sum transmit power or the per-BS transmit power margin. Since the original optimization problems contain binary variables indicating the BS associations, finding their optimal solutions is a challenging task. To circumvent this difficulty, we first relax the original problems into new optimization problems by expanding their constraint sets. Based on the nonconvex quadratic constrained quadratic programming framework, we show that these relaxed problems can be solved optimally. Interestingly, with the first design objective, the obtained solution from the relaxed problem is also optimal to the original problem. With the second design objective, a suboptimal solution to the original problem is then proposed, based on the obtained solution from the relaxed problem. Simulation results show that the resulting jointly optimal BS association and beamforming design significantly outperforms fixed BS association schemes.

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arXiv2016

Leveraging Synergy of 5G SDWN and Multi-Layer Resource Management for Network Optimization

Fifth-generation (5G) cellular wireless networks are envisioned to predispose service-oriented, flexible, and spectrum/energy-efficient edge-to-core infrastructure, aiming to offer diverse applications. Convergence of software-defined networking (SDN), software-defined radio (SDR) compatible with multiple radio access technologies (RATs), and virtualization on the concept of 5G software-defined wireless networking (5G-SDWN) is a promising approach to provide such a dynamic network. The principal technique behind the 5G-SDWN framework is the separation of the control and data planes, from the deep core entities to edge wireless access points (APs). This separation allows the abstraction of resources as transmission parameters of each user over the 5G-SDWN. In this user-centric and service-oriented environment, resource management plays a critical role to achieve efficiency and reliability. However, it is natural to wonder if 5G-SDWN can be leveraged to enable converged multi-layer resource management over the portfolio of resources, and reciprocally, if CML resource management can effectively provide performance enhancement and reliability for 5G-SDWN. We believe that replying to these questions and investigating this mutual synergy are not trivial, but multidimensional and complex for 5G-SDWN, which consists of different technologies and also inherits legacy generations of wireless networks. In this paper, we propose a flexible protocol structure based on three mentioned pillars for 5G-SDWN, which can handle all the required functionalities in a more crosslayer manner. Based on this, we demonstrate how the general framework of CML resource management can control the end user quality of experience. For two scenarios of 5G-SDWN, we investigate the effects of joint user-association and resource allocation via CML resource management to improve performance in a virtualized network.

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arXiv2015

Joint User-Association and Resource-Allocation in Virtualized Wireless Networks

In this paper, we consider a down-link transmission of multicell virtualized wireless networks (VWNs) where users of different service providers (slices) within a specific region are served by a set of base stations (BSs) through orthogonal frequency division multiple access (OFDMA). In particular, we develop a joint BS assignment, sub-carrier and power allocation algorithm to maximize the network throughput, while satisfying the minimum required rate of each slice. Under the assumption that each user at each transmission instance can connect to no more than one BS, we introduce the user-association factor (UAF) to represent the joint sub-carrier and BS assignment as the optimization variable vector in the mathematical problem formulation. Sub-carrier reuse is allowed in different cells, but not within one cell. As the proposed optimization problem is inherently non-convex and NP-hard, by applying the successive convex approximation (SCA) and complementary geometric programming (CGP), we develop an efficient two-step iterative approach with low computational complexity to solve the proposed problem. For a given power-allocation, Step 1 derives the optimum userassociation and subsequently, for an obtained user-association, Step 2 find the optimum power-allocation. Simulation results demonstrate that the proposed iterative algorithm outperforms the traditional approach in which each user is assigned to the BS with the largest average value of signal strength, and then, joint sub-carrier and power allocation is obtained for the assigned users of each cell. Especially, for the cell-edge users, simulation results reveal a coverage improvement up to 57% and 71% for uniform and non-uniform users distribution, respectively leading to more reliable transmission and higher spectrum efficiency for VWN.

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arXiv2015

Virtualization of Multi-Cell 802.11 Networks: Association and Airtime Control

This paper investigates the virtualization and optimization of a multi-cell WLAN. We consider the station (STA)-access point (AP) association and airtime control for virtualized 802.11 networks to provide service customization and fairness across multiple internet service providers (ISPs) sharing the common physical infrastructure and network capacity. More specifically, an optimization problem is formulated on the STAs transmission probabilities to maximize the overall network throughput, while providing airtime usage guarantees for the ISPs. Subsequently, an algorithm to reach the optimal solution is developed by applying monomial approximation and geometric programming iteratively. Based on the proposed three-dimensional Markov-chain model of the enhanced distributed channel access (EDCA) protocol, the detailed implementation of the optimal transmission probability is also discussed. The accuracy of the proposed Markov-chain model and the performance of the developed association and airtime control scheme are evaluated through numerical results.

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arXiv2015

Energy-Efficient Adaptive Power Allocation for Incremental MIMO Systems

We consider energy-efficient adaptive power allocation for three incremental multiple-input multiple-output (IMIMO) systems employing ARQ, hybrid ARQ (HARQ) with Chase combining (CC), and HARQ with incremental redundancy (IR), to minimize their rate-outage probability (or equivalently packet drop rate) under a constraint on average energy consumption per data packet. We first provide the rate-outage probability expressions for the three IMIMO systems, and then propose methods to convert them into a tractable form and formulate the corresponding non-convex optimization problems that can be solved by an interior-point algorithm for finding a local optimum. To further reduce the solution complexity, using an asymptotically equivalent approximation of the rate-outage probability expressions, we approximate the non-convex optimization problems as a unified geometric programming problem (GPP), for which we derive the closed-form solution. Illustrative results indicate that the proposed power allocation (PPA) offers significant gains in energy savings as compared to the equal-power allocation (EPA), and the simple closed-form GPP solution can provide closer performance to the exact method at lower values of rate-outage probability, for the three IMIMO systems.

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arXiv2015

Variable-Length Feedback Codes under a Strict Delay Constraint

We study variable-length feedback (VLF) codes under a strict delay constraint to maximize their average transmission rate (ATR) in a discrete memoryless channel (DMC) while considering periodic decoding attempts. We first derive a lower bound on the maximum achievable ATR, and confirm that the VLF code can outperform non-feedback codes with a larger delay constraint. We show that for a given decoding period, as the strict delay constraint, L, increases, the gap between the ATR of the VLF code and the DMC capacity scales at most on the order of O(L^{-1}) instead of O(L^{-1/2}) for non-feedback codes as shown in Polyanskiy et al. ["Channel coding rate in the finite blocklengh regime," IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2307-2359, May 2010.]. We also develop an approximation indicating that, for a given L, the achievable ATR increases as the decoding period decreases.

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arXiv2014

Full-Duplex Relay with Jamming Protocol for Improving Physical-Layer Security

This paper proposes a jointly cooperative relay and jamming protocol based on full-duplex (FD) capable relay to increase the source-destination secrecy rate in the presence of different types of eavesdroppers. In this so called \textit{FD-Relay with jamming (FDJ)} protocol, the FD-Relay, first, simultaneously receives data and sends jamming to the eavesdropper, and, then, forwards the data, while the source jams the eavesdropper. Achievable secrecy rates of the proposed FDJ in presence of different eavesdropper types and self-interference (SI) are derived and compared with those of the traditional half-duplex (HD) relay. The adaptive power allocation for secrecy rate maximization in a multi-carrier scenario for both proposed FDJ and HD-Relay is formulated as a non-convex optimization problem and corresponding iterative solution algorithm is developed using the difference-of-two-concave-functions (DC) programming technique. The simulation results confirm that FDJ offers significant improvements in the secrecy rate over the HD-Relay.

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