arXiv subjects

Aryan Kaushik

Publications and source records attributed to Aryan Kaushik.

47 records · Page 3Linked to original sources

arXiv2022

Energy Efficient Dual-Functional Radar-Communication: Rate-Splitting Multiple Access, Low-Resolution DACs, and RF Chain Selection

Dual-Functional Radar-Communication systems enhance the benefits of communications and radar sensing by jointly implementing these on the same hardware platform and using the common RF resources. An important and latest concern to be addressed in designing such Dual-Functional Radar-Communication systems is maximizing the energy-efficiency. In this paper, we consider a Dual-Functional Radar-Communication system performing simultaneous multi-user communications and radar sensing, and investigate the energy-efficiency behaviour with respect to active transmission elements. Specifically, we formulate a problem to find the optimal precoders and the number of active RF chains for maximum energy-efficiency by taking into consideration the power consumption of low-resolution Digital-to-Analog Converters on each RF chain under communications and radar performance constraints. We consider Rate-Splitting Multiple Access to perform multi-user communications with perfect and imperfect Channel State Information at Transmitter. The formulated non-convex optimization problem is solved by means of a novel algorithm. We demonstrate by numerical results that Rate Splitting Multiple Access achieves an improved energy-efficiency by employing a smaller number of RF chains compared to Space Division Multiple Access, owing to its generalized structure and improved interference management capabilities.

cs.IT↗

arXiv2021

Pre-scaling and Codebook Design for Joint Radar and Communication Based on Index Modulation

This paper develops an efficient index modulation (IM) approach for the joint radar-communication (JRC) system based on a multi-carrier multiple-input multiple-output (MIMO) radar. The communication information is embedded into the transmitted radar pulses by selecting the corresponding indices of the carrier frequencies and antenna allocations, providing two degrees of freedom. Our contribution involves the development of a novel codebook based minimum Euclidean distance (MED) maximization and a constellation randomization pre-scaling (CRPS) scheme for efficient IM-JRC transmission. It can be inferred that the IM approach integrating the CRPS scheme followed by the codebook design maximizes the signal-to-noise ratio gain. The numerical results support the effectiveness of the proposed approach and show enhanced bit error rate performance when compared to the existing baseline.

eess.SP↗

arXiv2021

Hardware Efficient Joint Radar-Communications with Hybrid Precoding and RF Chain Optimization

In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems. A dynamic active RF chain selection mechanism is implemented in the baseband processing and the energy efficiency (EE) maximization problem is solved using fractional programming to obtain the optimal number of RF chains at the current channel state. Subsequently hybrid precoders are computed employing a sub-arrayed MIMO structure for EE maximization with weighted formulation of the communication and radar metrics, and the solution is based on alternating minimization. The simulation results show that the proposed method with minimum hardware achieves the best EE while maintaining the rate performance, and an efficient trade-off between sensing and communication.

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arXiv2021

Rate-Splitting Multiple Access for Joint Radar-Communications with Low-Resolution DACs

In this paper, we introduce the design of a multi-antenna Joint Radar-Communication (JRC) system with Rate Splitting Multiple Access (RSMA) and low resolution Digital-to-Analog Converter (DAC) units. Using RSMA, the communication messages are split into private and common parts, then precoded and quantized before transmission. We use a problem formulation to design the JRC system with RSMA and low resolution DACs by maximizing communication sum-rate and the proximity of the resulting JRC waveform to an optimal radar beampattern under an average transmit power constraint. We solve the joint sum-rate maximization and beampattern error minimization problem using Alternating Direction Method of Multipliers (ADMM) method. The numerical results show that RSMA achieves a significantly higher sum-rate compared to Space Division Multiple Access (SDMA) while providing the same Normalized Mean Square Error (NMSE) for the designed radar beampattern.

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arXiv2021

Virtual Network Function Placement in Satellite Edge Computing with a Potential Game Approach

Satellite networks, as a supplement to terrestrial networks, can provide effective computing services for Internet of Things (IoT) users in remote areas. Due to the resource limitation of satellites, such as in computing, storage, and energy, a computation task from a IoT user can be divided into several parts and cooperatively accomplished by multiple satellites to improve the overall operational efficiency of satellite networks. Network function virtualization (NFV) is viewed as a new paradigm in allocating network resources on-demand. Satellite edge computing combined with the NFV technology is becoming an emerging topic. In this paper, we propose a potential game approach for virtual network function (VNF) placement in satellite edge computing. The VNF placement problem aims to maximize the number of allocated IoT users, while minimizing the overall deployment cost. We formulate the VNF placement problem with maximum network payoff as a potential game and analyze the problem by a game-theoretical approach. We implement a decentralized resource allocation algorithm based on a potential game (PGRA) to tackle the VNF placement problem by finding a Nash equilibrium. Finally, we conduct the experiments to evaluate the performance of the proposed PGRA algorithm. The simulation results show that the proposed PGRA algorithm can effectively address the VNF placement problem in satellite edge computing.

cs.NI↗

arXiv2021

A Distributed Virtual Network Function Placement Approach in Satellite Edge and Cloud Computing

Satellite edge computing has become a promising way to provide computing services for Internet of Things (IoT) devices in remote areas, which are out of the coverage of terrestrial networks, nevertheless, it is not suitable for large-scale IoT devices due to the resource limitation of satellites. Cloud computing can provide sufficient available resources for IoT devices but it does not meet the service requirements of delay sensitive users as high network latency. Collaborative edge and cloud computing is to facilitate flexible service provisioning for numerous IoT devices by incorporating the advantages of edge and cloud computing. In this paper, we investigate the virtual network function (VNF) placement problem in collaborative satellite edge and cloud computing to minimize the satellite network bandwidth usage and the service end-to-end delay. We formulate the VNF placement problem as an integer non-linear programming problem and propose a distributed VNF placement (D-VNFP) algorithm to address it, as the VNF placement problem is NP-hard. The experiments are conducted to evaluate the effectiveness of the proposed D-VNFP algorithm. The results show that the proposed D-VNFP algorithm outperforms the existing baseline algorithms of Greedy and Viterbi for solving the VNF placement problem in satellite edge and cloud computing.

cs.NI↗

arXiv2021

Service Chaining Placement Based on Satellite Mission Planning in Ground Station Networks

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this paper, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer nonlinear programming (INLP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks.

cs.NI↗

arXiv2020

Energy Efficiency Maximization in Millimeter Wave Hybrid MIMO Systems for 5G and Beyond

At millimeter wave (mmWave) frequencies, the higher cost and power consumption of hardware components in multiple-input multiple output (MIMO) systems do not allow beamforming entirely at the baseband with a separate radio frequency (RF) chain for each antenna. In such scenarios, to enable spatial multiplexing, hybrid beamforming, which uses phase shifters to connect a fewer number of RF chains to a large number of antennas is a cost effective and energy-saving alternative. This paper describes our research on fully adaptive transceivers that adapt their behaviour on a frame-by-frame basis, so that a mmWave hybrid MIMO system always operates in the most energy efficient manner. Exhaustive search based brute force approach is computationally intensive, so we study fractional programming as a low-cost alternative to solve the problem which maximizes energy efficiency. The performance results indicate that the resulting mmWave hybrid MIMO transceiver achieves significantly improved energy efficiency results compared to the baseline cases involving analogue-only or digital-only signal processing solutions, and shows performance trade-offs with the brute force approach.

eess.SP↗

arXiv2020

Joint Bit Allocation and Hybrid Beamforming Optimization for Energy Efficient Millimeter Wave MIMO Systems

In this paper, we aim to design highly energy efficient end-to-end communication for millimeter wave multiple-input multiple-output systems. This is done by jointly optimizing the digital-to-analog converter (DAC)/analog-to-digital converter (ADC) bit resolutions and hybrid beamforming matrices. The novel decomposition of the hybrid precoder and the hybrid combiner to three parts is introduced at the transmitter (TX) and the receiver (RX), respectively, representing the analog precoder/combiner matrix, the DAC/ADC bit resolution matrix and the baseband precoder/combiner matrix. The unknown matrices are computed as a solution to the matrix factorization problem where the optimal fully digital precoder or combiner is approximated by the product of these matrices. A novel and efficient solution based on the alternating direction method of multipliers is proposed to solve these problems at both the TX and the RX. The simulation results show that the proposed solution, where the DAC/ADC bit allocation is dynamic during operation, achieves higher energy efficiency when compared with existing benchmark techniques that use fixed DAC/ADC bit resolutions.

eess.SP↗

arXiv2020

Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. When compared with genetic algorithm (GA) and fast elitist non-dominated sorting genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO algorithm is more effective than the existing solutions to solve the problem of resource allocation with a large number of the requested tasks. Furthermore, we provide the performance comparison of the HMAO algorithm with the first-fit greedy approach in on-line resource allocation.

cs.DC↗

arXiv2019

Energy Efficient ADC Bit Allocation and Hybrid Combining for Millimeter Wave MIMO Systems

Low resolution analog-to-digital converters (ADCs) can be employed to improve the energy efficiency (EE) of a wireless receiver since the power consumption of each ADC is exponentially related to its sampling resolution and the hardware complexity. In this paper, we aim to jointly optimize the sampling resolution, i.e., the number of ADC bits, and analog/digital hybrid combiner matrices which provides highly energy efficient solutions for millimeter wave multiple-input multiple output systems. A novel decomposition of the hybrid combiner to three parts is introduced: the analog combiner matrix, the bit resolution matrix and the baseband combiner matrix. The unknown matrices are computed as the solution to a matrix factorization problem where the optimal, fully digital combiner is approximated by the product of these matrices. An efficient solution based on the alternating direction method of multipliers is proposed to solve this problem. The simulation results show that the proposed solution achieves high EE performance when compared with existing benchmark techniques that use fixed ADC resolutions.

eess.SP↗