arXiv subjects

H. Murat Gürsu

Publications and source records attributed to H. Murat Gürsu.

11 recordsLinked to original sources

arXiv2022

Tree-Algorithms with Multi-Packet Reception and Successive Interference Cancellation

In this paper, we perform a thorough analysis of tree-algorithms with multi-packet reception (MPR) and successive interference cancellation (SIC), showing a number of novel results. We first derive the basic performance parameters, which are the expected length of the collision resolution interval and the normalized throughput, conditioned on the number of contending users. We then study their asymptotic behaviour, identifying an oscillatory component that amplifies with the increase in MPR. In the next step, we derive the throughput for the gated and windowed access, assuming Poisson arrivals. We show that for windowed access, the bound on maximum stable normalized throughput increases with the increase in MPR. his implies that investing in advanced physical capabilities, i.e., MPR and SIC pays off from the perspective of the medium access control algorithm.

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arXiv2022

On d-ary tree algorithms with successive interference cancellation

In this paper, we outline the approach for the derivation of the length of the collision resolution interval for d-ary tree algorithms (TA) with gated access and successive interference cancellation (SIC), conditioned on the number of the contending users. This is the basic performance parameter for TA with gated access. We identify the deficiencies of the analysis performed in the seminal paper on TA with SIC by Yu and Giannakis, showing that their analysis is correct only for binary splitting, i.e. for d=2. We also provide some insightful results on the stable throughput that can be achieved for different values of d.

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arXiv2021

Analysis of Tree-Algorithms with Multi-Packet Reception

In this paper, we analyze binary-tree algorithms in a setup in which the receiver can perform multi-packet reception (MPR) of up to and including K packets simultaneously. The analysis addresses both traffic-independent performance as well as performance under Poisson arrivals. For the former case, we show that the throughput, when normalized with respect to the assumed linear increase in resources required to achieve K-MPR capability, tends to the same value that holds for the single-reception setup. However, when coupled with Poisson arrivals in the windowed access scheme, the normalized throughput increases with K, and we present evidence that it asymptotically tends to 1. We also provide performance results for the modified tree algorithm with K-MPR in the clipped access scheme. To the best of our knowledge, this is the first paper that provides an analytical treatment and a number of fundamental insights in the performance of tree-algorithms with MPR.

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arXiv2020

Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V Communication

We consider the distributed resource selection problem in Vehicle-to-vehicle (V2V) communication in the absence of a base station. Each vehicle autonomously selects transmission resources from a pool of shared resources to disseminate Cooperative Awareness Messages (CAMs). This is a consensus problem where each vehicle has to select a unique resource. The problem becomes more challenging when---due to mobility---the number of vehicles in vicinity of each other is changing dynamically. In a congested scenario, allocation of unique resources for each vehicle becomes infeasible and a congested resource allocation strategy has to be developed. The standardized approach in 5G, namely semi-persistent scheduling (SPS) suffers from effects caused by spatial distribution of the vehicles. In our approach, we turn this into an advantage. We propose a novel DIstributed Resource Allocation mechanism using multi-agent reinforcement Learning (DIRAL) which builds on a unique state representation. One challenging issue is to cope with the non-stationarity introduced by concurrently learning agents which causes convergence problems in multi-agent learning systems. We aimed to tackle non-stationarity with unique state representation. Specifically, we deploy view-based positional distribution as a state representation to tackle non-stationarity and perform complex joint behavior in a distributed fashion. Our results showed that DIRAL improves PRR by 20% compared to SPS in challenging congested scenarios.

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arXiv2020

AoI-based Finite Horizon Scheduling for Heterogeneous Networked Control Systems

Age of information (AoI) measures information freshness at the receiver. AoI may provide insights into quality of service in communication systems. For this reason, it has been used as a cross-layer metric for wireless communication protocols. In this work, we employ AoI to calculate penalty functions for a centralized resource scheduling problem. We consider a single wireless link shared by multiple, heterogeneous control systems where each sub-system has a time-varying packet loss probability. Sub-systems are competing for network resources to improve the accuracy of their remote estimation process. In order to cope with the dynamically changing conditions of the wireless link, we define a finite horizon age-penalty minimization problem and propose a scheduler that takes optimal decisions by looking $H$ slots into the future. The proposed algorithm has a worst-case complexity that grows exponentially with $H$. However, by narrowing down our search space within the constrained set of actions, we are able to decrease the complexity significantly without losing optimality. On the contrary, we show by simulations that the benefit of increasing $H$ w.r.t. remote state estimation performance diminishes after a certain $H$ value.

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arXiv2020

Probability Analysis of Age of Information in Multi-hop Networks

Age-of-information (AoI) is a metric quantifying information freshness at the receiver. It captures the delay together with packet loss and packet generation rate. However, the existing literature focuses on average or peak AoI and neglects the complete distribution. In this work, we consider a N-hop network with time-invariant packet loss probabilities on each link. We derive closed form equations for the probability mass function of AoI. We verify our findings with simulations. Our results show that the performance indicators considered in the literature such as average or peak AoI may give misleading insights into the real AoI performance.

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arXiv2019

Veni Vidi Dixi: Reliable Wireless Communication with Depth Images

The upcoming industrial revolution requires deployment of critical wireless sensor networks for automation and monitoring purposes. However, the reliability of the wireless communication is rendered unpredictable by mobile elements in the communication environment such as humans or mobile robots which lead to dynamically changing radio environments. Changes in the wireless channel can be monitored with frequent pilot transmission. However, that would stress the battery life of sensors. In this work a new wireless channel estimation technique, Veni Vidi Dixi, VVD, is proposed. VVD leverages the redundant information in depth images obtained from the surveillance cameras in the communication environment and utilizes Convolutional Neural Networks CNNs to map the depth images of the communication environment to complex wireless channel estimations. VVD increases the wireless communication reliability without the need for frequent pilot transmission and with no additional complexity on the receiver. The proposed method is tested by conducting measurements in an indoor environment with a single mobile human. Up to authors best knowledge our work is the first to obtain complex wireless channel estimation from only depth images without any pilot transmission. The collected wireless trace, depth images and codes are publicly available.

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arXiv2019

Boost Your CotS IEEE 802.15.4 Network with Inter-Slot Interference Cancellation for Industrial IoT

The current cellular standardization for 5G is working towards wireless advances to enable further productivity for industrial automation. However, this development will take several years. Meanwhile, the capabilities of the currently available standards should be pushed to their limits. To this end, in this work, we present results from the first inter-slot successive interference cancellation testbed using commercial off the shelf IEEE 802.15.4 sensors. Through our implementation, we have measured a throughput of $0.72$ packets per slot which doubles the currently used contention-based access, Slotted ALOHA, with a limit of $0.36$ packets per slot. The hardware effects of the boards, which degrade the successive interference cancellation performance from the theoretical limit of $1$ packet per slot, are modeled and validated through measurements. We also propose a model that can be used to calculate the expected successive interference cancellation throughput with the specific hardware available in a factory. Furthermore, our proposed model should replace the perfect physical layer assumptions for researchers to design new MAC algorithms taking practical limitations into account.

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arXiv2019

Admission Control based Traffic-Agnostic Delay-Constrained Random Access (AC/DC-RA) for M2M Communication

The problem of wireless M2M communication is twofold: the reliability aspect and the scalability aspect. The solution of this problem demands a delay constrained random access protocol. To this end we propose Admission Control based Traffic Agnostic Delay Constrained Random Access (AC/DC-RA) protocol. Our main contribution is enabling the stochastic delay constraints agnostic to the traffic, such that the stochastic delay constraint is valid with respect to varying number of arrivals. We achieve this with an admission control decision that uses a novel collision estimation algorithm for active number of arrivals per contention resource. We use an adaptive contention resolution algorithm to react to the varying number of arrivals. Using these tools, the admission control solves the stability problem. We show with simulations that AC/DC-RA provide stochastic delay constrained random access in a traffic agnostic way to sustain a stable performance against Poisson and Beta arrivals without any modification to the protocol.

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arXiv2019

On Throughput Maximization of Grant-Free Access with Reliability-Latency Constraints

Enabling autonomous driving and industrial automation with wireless networks poses many challenges, which are typically abstracted through reliability and latency requirements. One of the main contributors to latency in cellular networks is the reservation-based access, which involves lengthy and resource-inefficient signaling exchanges. An alternative is to use grant-free access, in which there is no resource reservation. A handful of recent works investigated how to fulfill reliability and latency requirements with different flavors of grant-free solutions. However, the resource efficiency, i.e., the throughput, has been only the secondary focus. In this work, we formulate the throughput of grant-free access under reliability-latency constraints, when the actual number of arrived users or only the arrival distribution are known. We investigate how these different levels of knowledge about the arrival process influence throughput performance of framed slotted ALOHA with $K$-multipacket reception, for the Poisson and Beta arrivals. We show that the throughput under reliability-latency requirements can be significantly improved for the higher expected load of the access network, if the actual number of arrived users is known. This insight motivates the use of techniques for the estimation of the number of arrived users, as this knowledge is not readily available in grant-free access. We also asses the impact of estimation error, showing that for high reliability-latency requirements the gains in throughput are still considerable.

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arXiv2019

Hard Latency-Constraints for High-Throughput Random Access: SICQTA

Enabling closed control loops via wireless communication has attracted a lot of interest recently and is investigated under the name cyber-physical systems. Under cyber-physical systems one challenging scenario is multiple loops sharing a wireless medium, and the age of the control information has to be minimized without sacrificing reliability to guarantee the control stability. The number of transmitting devices depends on the control parameters thus, it is stochastic. Wireless uplink resource allocation given low latency constraints for unknown number of devices is a hard problem. For this problem, random access is the most prominent way to minimize latency, but reliability is sacrificed. However, as reliability is also critical for such applications, improved random access algorithms with hard latency guarantees are needed. Currently available random access algorithms with hard latency guarantees have low throughput and some of them are limited to low number of active devices. In this work, we provide a high-throughput random access algorithm with hard latency-constraints (SICQTA) that scales to any number of active devices. This algorithm, making use of feedback, has a varying throughput between $0.69$ and $1$ depending on the number of devices, which is unprecedented in the state of the art up to our best knowledge.

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