arXiv subjects

Michal Yemini

Publications and source records attributed to Michal Yemini.

31 records · Page 2Linked to original sources

arXiv2022

Semi-Decentralized Federated Learning with Collaborative Relaying

We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every communication round to the PS, each client computes a local consensus of the updates from its neighboring clients and eventually transmits a weighted average of its own update and those of its neighbors to the PS. We appropriately optimize these averaging weights to ensure that the global update at the PS is unbiased and to reduce the variance of the global update at the PS, consequently improving the rate of convergence. Numerical simulations substantiate our theoretical claims and demonstrate settings with intermittent connectivity between the clients and the PS, where our proposed algorithm shows an improved convergence rate and accuracy in comparison with the federated averaging algorithm.

cs.LG↗

arXiv2022

Learning in Restless Bandits under Exogenous Global Markov Process

We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribution of each arm. Under each global state, the rewards process of each arm evolves according to an unknown Markovian rule, which is non-identical among different arms. At each time, a player chooses an arm out of $N$ arms to play, and receives a random reward from a finite set of reward states. The arms are restless, that is, their local state evolves regardless of the player's actions. Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time $t$ the arm that maximizes the expected immediate value. The objective is to develop an arm-selection policy that minimizes the regret. To that end, we develop the Learning under Exogenous Markov Process (LEMP) algorithm. We analyze LEMP theoretically and establish a finite-sample bound on the regret. We show that LEMP achieves a logarithmic regret order with time. We further analyze LEMP numerically and present simulation results that support the theoretical findings and demonstrate that LEMP significantly outperforms alternative algorithms.

cs.LG↗

arXiv2021

Interference Reduction in Virtual Cell Optimization

Virtual cell optimization clusters cells into neighborhoods and performs optimized resource allocation over each neighborhood. In prior works we proposed resource allocation schemes to mitigate the interference caused by transmissions in the same virtual cell. This work aims at mitigating both the interference caused by the transmissions of users in the same virtual cell and the interference between transmissions in different virtual cells. We propose a resource allocation technique that reduces the number of users that cannot achieve their constant guaranteed bit rate, i.e., the "unsatisfied users", in an uplink virtual cell system with cooperative decoding. The proposed scheme requires only the knowledge of the number of users each base station serves and relies on creating the interference graph between base stations at the edges of virtual cells. Allocation of frequency bands to users is based on the number of users each base station would serve in a non cooperative setup. We evaluate the performance of our scheme for a mmWave system. Our numerical results show that our scheme decreases the number of users in the system whose rate falls below the guaranteed rate, set to $128$kbps, $256$kbps or $512$kbps, when compared with our previously proposed optimization methods.

eess.SP↗

arXiv2021

The Restless Hidden Markov Bandit with Linear Rewards and Side Information

In this paper we present a model for the hidden Markovian bandit problem with linear rewards. As opposed to current work on Markovian bandits, we do not assume that the state is known to the decision maker before making the decision. Furthermore, we assume structural side information where the decision maker knows in advance that there are two types of hidden states; one is common to all arms and evolves according to a Markovian distribution, and the other is unique to each arm and is distributed according to an i.i.d. process that is unique to each arm. We present an algorithm and regret analysis to this problem. Surprisingly, we can recover the hidden states and maintain logarithmic regret in the case of a convex polytope action set. Furthermore, we show that the structural side information leads to expected regret that does not depend on the number of extreme points in the action space. Therefore, we obtain practical solutions even in high dimensional problems.

cs.LG↗

arXiv2020

Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks

We consider a detection problem where sensors experience noisy measurements and intermittent communication opportunities to a centralized fusion center (or cloud). The objective of the problem is to arrive at the correct estimate of event detection in the environment. The sensors may communicate locally with other sensors (local clusters) where they fuse their noisy sensor data to estimate the detection of an event locally. In addition, each sensor cluster can intermittently communicate to the cloud, where a centralized fusion center fuses estimates from all sensor clusters to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. Minimizing the expected loss function of networks where noisy sensors are intermittently connected to the cloud, as in our hybrid communication scheme, has not been investigated to our knowledge. We leverage recently improved concentration inequalities to arrive at an optimized decision rule for each cluster and we analyze the expected detection performance resulting from our hybrid scheme. Our analysis shows that clustering the sensors provides resilience to noise in the case of low communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.

eess.SY↗

arXiv2019

Virtual Cell Clustering with Optimal Resource Allocation to Maximize Capacity

This work proposes a new resource allocation optimization and network management framework for wireless networks using neighborhood-based optimization rather than fully centralized or fully decentralized methods. We propose hierarchical clustering with a minimax linkage criterion for the formation of the virtual cells. Once the virtual cells are formed, we consider two cooperation models: the interference coordination model and the coordinated multi-point decoding model. In the first model base stations in a virtual cell decode their signals independently, but allocate the communication resources cooperatively. In the second model base stations in the same virtual cell allocate the communication resources and decode their signals cooperatively. We address the resource allocation problem for each of these cooperation models. For the interference coordination model this problem is an NP-hard mixed-integer optimization problem whereas for the coordinated multi-point decoding model it is convex. Our numerical results indicate that proper design of the neighborhood-based optimization leads to significant gains in sum rate over fully decentralized optimization, yet may also have a significant sum rate penalty compared to fully centralized optimization. In particular, neighborhood-based optimization has a significant sum rate penalty compared to fully centralized optimization in the coordinated multi-point model, but not the interference coordination model.

cs.IT↗

arXiv2019

Optimal Resource Allocation for Cellular Networks with Virtual Cell Joint Decoding

This work presents a new resource allocation optimization framework for cellular networks using neighborhood-based optimization. Under this optimization framework resources are allocated within virtual cells encompassing several base-stations and the users within their coverage area. Incorporating the virtual cell concept enables the utilization of more sophisticated cooperative communication schemes such as coordinated multi-point decoding. We form the virtual cells using hierarchical clustering given a particular number of such cells. Once the virtual cells are formed, we consider a cooperative decoding scheme in which the base-stations in each virtual cell jointly decode the signals that they receive. We propose an iterative solution for the resource allocation problem resulting from the cooperative decoding within each virtual cell. Numerical results for the average system sum rate of our network design under hierarchical clustering are presented. These results indicate that virtual cells with neighborhood-based optimization leads to significant gains in sum rate over optimization within each cell, yet may also have a significant sum-rate penalty compared to fully-centralized optimization.

cs.IT↗

arXiv2019

Virtual Cell Clustering with Optimal Resource Allocation to Maximize Cellular System Capacity

This work presents a new network optimization framework for cellular networks using neighborhood-based optimization. Under this optimization framework resources are allocated within virtual cells encompassing several base-stations and the users within their coverage areas. We form the virtual cells using hierarchical clustering with a minimax linkage criterion given a particular number of such cells. Once the virtual cells are formed, we consider an interference coordination model in which base-stations in a virtual cell jointly allocate the channels and power to users within the virtual cell. We propose two new schemes for solving this mixed integer NP-hard resource allocation problem. The first scheme transforms the problem into a continuous variables problem; the second scheme proposes a new channel allocation method and then alternately solves the channel allocation problem using this new method, and the power allocation problem. We evaluate the average system sum rate of these schemes for a variable number of virtual cells. These results quantify the sum-rate along a continuum of fully-centralized versus fully-distributed optimization for different clustering and resource allocation strategies. These results indicate that the penalty of fully-distributed optimization versus fully-centralized (cloud RAN) can be as high as 50%. However, if designed properly, a few base stations within a virtual cell using neighborhood-based optimization have almost the same performance as fully-centralized optimization.

cs.IT↗

arXiv2019

"Fog" Optimization via Virtual Cells in Cellular Network Resource Allocation

This work proposes a new resource allocation optimization framework for cellular networks using "fog" or neighborhood-based optimization rather than fully centralized or fully decentralized methods. In neighborhood-based optimization resources are allocated within virtual cells encompassing several base-stations and the users within their coverage area. As the number of base-stations within a virtual cell increases, the framework reverts to centralized optimization, and as this number decreases it reverts to decentralized optimization. We address two tasks that must be carried out in the fog optimization framework: forming the virtual cells and allocating the communication resources in each virtual cell effectively. We propose hierarchical clustering for the formation of the virtual cells given a particular number of such cells. Once the virtual cells are formed, we consider several optimization methods to solve the NP-hard joint channel access and power allocation problem within each virtual cell in order to maximize the sum rate of the entire system. We present numerical results for the system sum rate of each scheme under hierarchical clustering. Our results indicate that proper design of the fog optimization results in little degradation relative to centralized optimization even for a relatively large number of virtual cells. However, improper design leads to a significant decrease in sum rate relative to centralized optimization.

cs.IT↗

arXiv2017

Phase Noise Compensation for OFDM Systems

We describe a low complexity method for time domain compensation of phase noise in OFDM systems. We extend existing methods in several respects. First we suggest using the Karhunen-Loéve representation of the phase noise process to estimate the phase noise. We then derive an improved datadirected choice of basis elements for LS phase noise estimation and present its total least square counterpart problem. The proposed method helps overcome one of the major weaknesses of OFDM systems. We also generalize the time domain phase noise compensation to the multiuser MIMO context. Finally we present simulation results using both simulated and measured phased noise. We quantify the tracking performance in the presence of residual carrier offset.

cs.IT↗

arXiv2016

On the Multiple Access Channel with Asynchronous Cognition

In this paper we introduce the two-user asynchronous cognitive multiple access channel (ACMAC). This channel model includes two transmitters, an uninformed one, and an informed one which knows prior to the beginning of a transmission the message which the uninformed transmitter is about to send. We assume that the channel from the uninformed transmitter to the receiver suffers a fixed but unknown delay. We further introduce a modified model, referred to as the asynchronous codeword cognitive multiple access channel (ACC-MAC), which differs from the ACMAC in that the informed user knows the signal that is to be transmitted by the other user, rather than the message that it is about to transmit. We state inner and outer bounds on the ACMAC and the ACC-MAC capacity regions, and we specialize the results to the Gaussian case. Further, we characterize the capacity regions of these channels in terms of multi-letter expressions. Finally, we provide an example which instantiates the difference between message side-information and codeword side-information.

cs.IT↗

arXiv2016

On Simultaneous Percolation with Two Disk Types

In this paper we consider the simultaneous percolation of two Gilbert disk models. The two models are connected through excluding disks, which prevent elements of the second model to be in the vicinity of the first model. Under these assumptions we characterize the region of densities in which the two models both have a unique infinite connected component. The motivation for this work is the co-existence of two cognitive radio networks.

cs.NI↗

arXiv2015

Asynchronous Transmission over Single-User State-Dependent Channels

Several channels with asynchronous side information are introduced. We first consider single-user state-dependent channels with asynchronous side information at the transmitter. It is assumed that the state information sequence is a possibly delayed version of the state sequence, and that the encoder and the decoder are aware of the fact that the state information might be delayed. It is additionally assumed that an upper bound on the delay is known to both encoder and decoder, but other than that, they are ignorant of the actual delay. We consider both the causal and the noncausal cases and present achievable rates for these channels, and the corresponding coding schemes. We find the capacity of the asynchronous Gel'fand-Pinsker channel with feedback. Finally, we consider a memoryless state dependent channel with asynchronous side information at both the transmitter and receiver, and establish a single-letter expression for its capacity.

cs.IT↗