arXiv subjects

Hossein S. Ghadikolaei

Publications and source records attributed to Hossein S. Ghadikolaei.

13 recordsLinked to original sources

arXiv2024

FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning

This paper investigates efficient distributed training of a Federated Learning~(FL) model over a wireless network of wireless devices. The communication iterations of the distributed training algorithm may be substantially deteriorated or even blocked by the effects of the devices' background traffic, packet losses, congestion, or latency. We abstract the communication-computation impacts as an `iteration cost' and propose a cost-aware causal FL algorithm~(FedCau) to tackle this problem. We propose an iteration-termination method that trade-offs the training performance and networking costs. We apply our approach when clients use the slotted-ALOHA, the carrier-sense multiple access with collision avoidance~(CSMA/CA), and the orthogonal frequency-division multiple access~(OFDMA) protocols. We show that, given a total cost budget, the training performance degrades as either the background communication traffic or the dimension of the training problem increases. Our results demonstrate the importance of proactively designing optimal cost-efficient stopping criteria to avoid unnecessary communication-computation costs to achieve only a marginal FL training improvement. We validate our method by training and testing FL over the MNIST dataset. Finally, we apply our approach to existing communication efficient FL methods from the literature, achieving further efficiency. We conclude that cost-efficient stopping criteria are essential for the success of practical FL over wireless networks.

cs.LG↗

arXiv2023

Beam Alignment Using Trajectory Information in Mobile Millimeter-wave Networks

Millimeter-wave and terahertz systems rely on beamforming/combining codebooks to determine the best beam directions during the initial access and data transmission. Existing approaches suffer from large codebook sizes and high beam searching overhead in the presence of mobile devices. To address this issue, we utilize the similarity of the channel in adjacent locations to divide the user trajectory into a set of separate regions and maintain a set of candidate beams for each region in a database. Due to the tradeoff between the number of regions and the signalling overhead, i.e., the greater number of regions results in a higher signal-to-noise ratio (SNR) but also a larger signalling overhead for the database, we propose an optimization framework to find the minimum number of regions based on the trajectory of a mobile device. Using a ray tracing tool, we demonstrate that the proposed method provides high SNR while being more robust to the location information accuracy in comparison to the lookup table baseline and fixed size region baseline.

cs.IT↗

arXiv2023

Reinforcement Learning-based Joint Handover and Beam Tracking in Millimeter-wave Networks

In this paper, we develop an algorithm for joint handover and beam tracking in millimeter-wave (mmWave) networks. The aim is to provide a reliable connection in terms of the achieved throughput along the trajectory of the mobile user while preventing frequent handovers. We model the association problem as an optimization problem and propose a reinforcement learning-based solution. Our approach learns whether and when beam tracking and handover should be performed and chooses the target base stations. In the case of beam tracking, we propose a tracking algorithm based on measuring a small spatial neighbourhood of the optimal beams in the previous time slot. Simulation results in an outdoor environment show the superior performance of our proposed solution in achievable throughput and the number of handovers needed in comparison to a multi-connectivity baseline and a learning-based handover baseline.

eess.SY↗

arXiv2022

A-LAQ: Adaptive Lazily Aggregated Quantized Gradient

Federated Learning (FL) plays a prominent role in solving machine learning problems with data distributed across clients. In FL, to reduce the communication overhead of data between clients and the server, each client communicates the local FL parameters instead of the local data. However, when a wireless network connects clients and the server, the communication resource limitations of the clients may prevent completing the training of the FL iterations. Therefore, communication-efficient variants of FL have been widely investigated. Lazily Aggregated Quantized Gradient (LAQ) is one of the promising communication-efficient approaches to lower resource usage in FL. However, LAQ assigns a fixed number of bits for all iterations, which may be communication-inefficient when the number of iterations is medium to high or convergence is approaching. This paper proposes Adaptive Lazily Aggregated Quantized Gradient (A-LAQ), which is a method that significantly extends LAQ by assigning an adaptive number of communication bits during the FL iterations. We train FL in an energy-constraint condition and investigate the convergence analysis for A-LAQ. The experimental results highlight that A-LAQ outperforms LAQ by up to a $50$% reduction in spent communication energy and an $11$% increase in test accuracy.

cs.LG↗

arXiv2022

Interplay between Distributed AI Workflow and URLLC

Distributed artificial intelligence (AI) has recently accomplished tremendous breakthroughs in various communication services, ranging from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wireless connected devices, random channel fluctuations, and the incumbent services simultaneously running on the same network affect the performance of distributed learning. In this paper, we investigate the interplay between distributed AI workflow and ultra-reliable low latency communication (URLLC) services running concurrently over a network. Using 3GPP compliant simulations in a factory automation use case, we show the impact of various distributed AI settings (e.g., model size and the number of participating devices) on the convergence time of distributed AI and the application layer performance of URLLC. Unless we leverage the existing 5G-NR quality of service handling mechanisms to separate the traffic from the two services, our simulation results show that the impact of distributed AI on the availability of the URLLC devices is significant. Moreover, with proper setting of distributed AI (e.g., proper user selection), we can substantially reduce network resource utilization, leading to lower latency for distributed AI and higher availability for the URLLC users. Our results provide important insights for future 6G and AI standardization.

cs.NI↗

arXiv2020

Learning-based Load Balancing Handover in Mobile Millimeter Wave Networks

Millimeter-wave (mmWave) communication is a promising solution to the high data rate demands in the upcoming 5G and beyond communication networks. When it comes to supporting seamless connectivity in mobile scenarios, resource and handover management are two of the main challenges in mmWave networks. In this paper, we address these two problems jointly and propose a learning-based load balancing handover in multi-user mobile mmWave networks. Our handover algorithm selects a backup base station and allocates the resource to maximize the sum rate of all the users while ensuring a target rate threshold and preventing excessive handovers. We model the user association as a non-convex optimization problem. Then, by applying a deep deterministic policy gradient (DDPG) method, we approximate the solution of the optimization problem. Through simulations, we show that our proposed algorithm minimizes the number of the events where a user's rate is less than its minimum rate requirement and minimizes the number of handovers while increasing the sum rate of all users.

eess.SP↗

arXiv2020

Learning-based Handover in Mobile Millimeter-wave Networks

Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data rates in the future cellular and wireless networks. The need for directional communication between base stations (BSs) and users in mmWave systems, that is achieved through beamforming, increases the complexity of the channel estimation. Moreover, in order to provide better coverage, dense deployment of BSs is required which causes frequent handovers and increased association overhead. In this paper, we present an approach that jointly addresses the beamforming and handover problems. Our solution entails an efficient beamforming method with a minimum number of pilots and a learning-based handover method supporting mobile scenarios. We use reinforcement learning algorithm to learn the optimal choices of the backup BSs in different locations of a mobile user. We show that our method provides high rate and reliability in all locations of the user's trajectory with a minimal number of handovers. Simulation results in an outdoor environment based on geometric mmWave channel modeling and real building map data show the superior performance of our proposed solution in achievable instantaneous rate and trajectory rate.

eess.SP↗

arXiv2020

A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks

Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference. Unfortunately, traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms in terms of latency and protocol overhead, while being sensitive to missing channel state information. In this paper, we propose hybrid model-based and data-driven multi-operator spectrum sharing mechanisms, which incorporate model-based beamforming and user association complemented by data-driven model refinements. Our solution has the same computational complexity as a model-based approach but has the major advantage of having substantially less signaling overhead. We discuss how limited channel state information and quantized codebook-based beamforming affect the learning and the spectrum sharing performance. We show that the proposed hybrid sharing scheme significantly improves spectrum utilization under realistic assumptions on inter-operator coordination and channel state information acquisition.

cs.IT↗

arXiv2020

Communication-efficient Variance-reduced Stochastic Gradient Descent

We consider the problem of communication efficient distributed optimization where multiple nodes exchange important algorithm information in every iteration to solve large problems. In particular, we focus on the stochastic variance-reduced gradient and propose a novel approach to make it communication-efficient. That is, we compress the communicated information to a few bits while preserving the linear convergence rate of the original uncompressed algorithm. Comprehensive theoretical and numerical analyses on real datasets reveal that our algorithm can significantly reduce the communication complexity, by as much as 95\%, with almost no noticeable penalty. Moreover, it is much more robust to quantization (in terms of maintaining the true minimizer and the convergence rate) than the state-of-the-art algorithms for solving distributed optimization problems. Our results have important implications for using machine learning over internet-of-things and mobile networks.

cs.LG↗

arXiv2019

Efficient Beamforming for Mobile mmWave Networks

We design a lightweight beam-searching algorithm for mobile millimeter-wave systems. We construct and maintain a set of path skeletons, i.e., potential paths between a user and the serving base station to substantially expedite the beam-searching process. To exploit the spatial correlations of the channels, we propose an efficient algorithm that measures the similarity of the skeletons and re-executes the beam-searching procedure only when the old one becomes obsolete. We identify and optimize several tradeoffs between: i) the beam-searching overhead and the instantaneous rate of the users, and ii) the number of users and the update overhead of the path skeletons. Simulation results in an outdoor environment with real building map data show that the proposed method can significantly improve the performance of beam-searching in terms of latency, energy consumption and achievable throughout.

eess.SP↗

arXiv2018

Fast and Reliable Initial Access with Random Beamforming for mmWave Networks

Millimeter-wave (mmWave) communications rely on directional transmissions to overcome severe path loss. Nevertheless, the use of narrow beams complicates the initial access procedure and increase the latency as the transmitter and receiver beams should be aligned for a proper link establishment. In this paper, we investigate the feasibility of random beamforming for the cell-search phase of initial access. We develop a stochastic geometry framework to analyze the performance in terms of detection failure probability and expected latency of initial access as well as total data transmission. Meanwhile, we compare our scheme with the widely used exhaustive search and iterative search schemes, in both control plane and data plane. Our numerical results show that, compared to the other two schemes, random beamforming can substantially reduce the latency of initial access with comparable failure probability in dense networks. We show that the gain of the random beamforming is more prominent in light traffics and low-latency services. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave network design.

cs.IT↗

arXiv2018

Low-latency Networking: Where Latency Lurks and How to Tame It

While the current generation of mobile and fixed communication networks has been standardized for mobile broadband services, the next generation is driven by the vision of the Internet of Things and mission critical communication services requiring latency in the order of milliseconds or sub-milliseconds. However, these new stringent requirements have a large technical impact on the design of all layers of the communication protocol stack. The cross layer interactions are complex due to the multiple design principles and technologies that contribute to the layers' design and fundamental performance limitations. We will be able to develop low-latency networks only if we address the problem of these complex interactions from the new point of view of sub-milliseconds latency. In this article, we propose a holistic analysis and classification of the main design principles and enabling technologies that will make it possible to deploy low-latency wireless communication networks. We argue that these design principles and enabling technologies must be carefully orchestrated to meet the stringent requirements and to manage the inherent trade-offs between low latency and traditional performance metrics. We also review currently ongoing standardization activities in prominent standards associations, and discuss open problems for future research.

eess.SP↗

arXiv2018

Reducing Initial Cell-search Latency in mmWave Networks

Millimeter-wave (mmWave) networks rely on directional transmissions, in both control plane and data plane, to overcome severe path-loss. Nevertheless, the use of narrow beams complicates the initial cell-search procedure where we lack sufficient information for beamforming. In this paper, we investigate the feasibility of random beamforming for cell-search. We develop a stochastic geometry framework to analyze the performance in terms of failure probability and expected latency of cell-search. Meanwhile, we compare our results with the naive, but heavily used, exhaustive search scheme. Numerical results show that, for a given discovery failure probability, random beamforming can substantially reduce the latency of exhaustive search, especially in dense networks. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave system design.

cs.IT↗