arXiv subjects

Matti Latva-aho

Publications and source records attributed to Matti Latva-aho.

At least 109 records · Page 6Linked to original sources

arXiv2020

Iterative Bayesian-based Localization Mechanism for Industry Verticals

We propose and evaluate an iterative localization mechanism employing Bayesian inference to estimate the position of a target using received signal strength measurements. The probability density functions of the target's coordinates are estimated through a Bayesian network. Herein, we consider an iterative procedure whereby our predictor (posterior distribution) is updated in a sequential order whenever new measurements are made available. The performance of the mechanism is assessed in terms of the respective root mean square error and kernel density estimation of the target coordinates. Our numerical results showed the proposed iterative mechanism achieves increasingly better estimation of the target node position each updating round of the Bayesian network with new input measurements.

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arXiv2020

Factory Automation: Resource Allocation of an Elevated LiDAR System with URLLC Requirements

Ultra-reliable and low-latency communications (URLLC) play a vital role in factory automation. To share the situational awareness data collected from the infrastructure as raw or processed data, the system should guarantee the URLLC capability since this is a safety-critical application. In this work, the resource allocation problem for an infrastructure-based communication architecture (Elevated LiDAR system/ ELiD) has been considered which can support the autonomous driving in a factory floor. The decoder error probability and the number of channel uses parameterize the reliability and the latency in the considered optimization problems. A maximum decoder error probability minimization problem and a total energy minimization problem have been considered in this work to analytically evaluate the performance of the ELiD system under different vehicle densities.

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arXiv2020

Ultra Reliable Communication via Optimum Power Allocation for HARQ Retransmission Schemes

In this work, we develop low complexity, optimal power allocation algorithms that would allow ultra reliable operation at any outage probability target with minimum power consumption in the finite blocklength regime by utilizing Karush-Kuhn-Tucker (KKT) conditions. In our setup, we assume that the transmitter does not know the channel state information (CSI). First, we show that achieving a very low packet outage probability by using an open loop setup requires extremely high power consumption. Thus, we resort to retransmission schemes as a solution, namely Automatic Repeat Request (ARQ), Chase Combining Hybrid ARQ (CC-HARQ) and Incremental Redundancy (IR) HARQ. Countrary to classical approaches, where it is optimal to allocate equal power with each transmission, we show that for operation in the ultra reliable regime (URR), the optimal strategy suggests transmission with incremental power in each round. Numerically, we evaluate the power gains of the proposed protocol. We show that the best power saving is given by IR-HARQ protocol. Further, we show that when compared to the one shot transmission, these protocols enable large average and maximum power gains. Finally, we show that the larger the number of transmissions is, the larger power gains will be attained.

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arXiv2020

Hierarchical User Clustering for mmWave-NOMA Systems

Non-orthogonal multiple access (NOMA) and mmWave are two complementary technologies that can support the capacity demand that arises in 5G and beyond networks. The increasing number of users are served simultaneously while providing a solution for the scarcity of the bandwidth. In this paper we present a method for clustering the users in a mmWave-NOMA system with the objective of maximizing the sum-rate. An unsupervised machine learning technique, namely, hierarchical clustering is utilized which does the automatic identification of the optimal number of clusters. The simulations prove that the proposed method can maximize the sum-rate of the system while satisfying the minimum QoS for all users without the need of the number of clusters as a prerequisite when compared to other clustering methods such as k-means clustering.

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arXiv2020

Admission Control in 5G Networks for the Coexistence of eMBB-URLLC Users

In this paper, we consider the problem of admission control in 5G networks where enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users are coexisting. URLLC users require low latency and high reliability while eMBB users require high data rates. Thus, it is essential to control the admission of eMBB users while giving priority to all URLLC users in a network where both types of users are coexisting. Our aim is to maximize the number of admitted eMBB users to the system with a guaranteed data rate while allocating resources to all URLLC users. We formulated this as an l_0 minimization problem. Since it is an NP-hard problem we have used approximation methods and sequential convex programming to obtain a suboptimal solution. Numerically we have shown that the proposed algorithm achieves near-optimal performance. Our algorithm is able to maximize the number of admitted eMBB users with an optimal allocation of resources while giving priority to all URLLC users.

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arXiv2020

An Initial Access Optimization Algorithm for millimeter Wave 5G NR Networks

The millimeter wave (mmWave) communication uses directional antennas. Hence, achieving fine alignment of transmit and receive beams at the initial access phase is quite challenging and time-consuming. In this paper, we provide a dynamic-weight based beam sweeping direction and synchronization signal block (SSB) allocation algorithm to optimize the cell search of the initial access in mmWave 5G NR networks. The number of SSBs transmitted in each beam sweeping direction depends on previously learned experience which is based on the number of detected UEs (user equipment) per SSB for each sweeping direction. Overall, numerical simulation results indicate that the proposed algorithm is shown to be capable of detecting more users with a lower misdetection probability. Furthermore, it is possible to achieve the same performance with a smaller number of dynamic resource (i.e., SSB) allocation, compared to constant resource allocation.

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arXiv2020

Block Error Performance of NOMA with HARQ-CC in Finite Blocklength

This paper investigates the performance of a two-user downlink non-orthogonal multiple access (NOMA) system using hybrid automatic repeat request with chase combining (HARQ-CC) in finite blocklength. First, an analytical framework is developed by deriving closed-form approximations for the individual average block error rate (BLER) of the near and the far user. Based upon that, the performance of NOMA is discussed in comparison to orthogonal multiple access (OMA), which draws the conclusion that NOMA outperforms OMA in terms of user fairness. Further, an algorithm is devised to determine the required blocklength and power allocation coefficients for NOMA that satisfies reliability targets for the users. The required blocklength for NOMA is compared to OMA, which shows NOMA has a lower blocklength requirement in high transmit signal-to-noise ratio (SNR) conditions, leading to lower latency than OMA when reliability requirements in terms of BLER for the two users are in the order of 10^(-5).

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arXiv2020

High Reliability Downlink MU-MIMO: New OSTBC Approach and Superposition Modulated Side Information

In this paper, a proposal to improve the reliability of a downlink multiuser (MU) MIMO transmission scheme is investigated with the use of a new approach in orthogonal space-time block codes (OSTBC) and network coding with a superposition modulated system and side information. With the new encoded OSTBC approach, diversity is offered where each user receives all other users' symbols, which allows the recovery of symbols in several ways. In addition, multiple users can be accommodated with the same resource, which is quite useful in a wireless system where resources are always restricted. By employing superposition modulation, the side information needed for error recovery can be transmitted over the same resource used for the normal information frame. In addition, the proposed system exploits diversity through a novel technique of sub-constellation alignment-based signal combining for efficient side information dissemination. A detailed analysis of the new OSTBC approach is carried out. It is shown that the performance of the MU-MIMO system can be improved significantly in terms of block and frame error rates (BLER, FER) considered as reliability measures. By accommodating a reasonable number of multiple users, high reliability is achieved at the expense of the rate. To compensate for the low rate, conventional OSTBC can be considered and simulation results are shown, where, as a penalty to pay, multiple orthogonal resources are required.

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arXiv2020

Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems

End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the bit error rate (BER) performance of autoencoder based systems and conventional channel coded systems with convolutional coding (CC), in order to understand the potential of deep learning-based systems as alternatives to conventional systems. From the simulations, autoencoder implementation was observed to have a better BER in 0-5 dB $E_{b}/N_{0}$ range than its equivalent half-rate convolutional coded BPSK with hard decision decoding, and to have only less than 1 dB gap at a BER of $10^{-5}$. Furthermore, we have also proposed a novel low complexity autoencoder architecture to implement end-to-end learning of coded systems in which we have shown better BER performance than the baseline implementation. The newly proposed low complexity autoencoder was capable of achieving a better BER performance than half-rate 16-QAM with hard decision decoding over the full 0-10 dB $E_{b}/N_{0}$ range and a better BER performance than the soft decision decoding in 0-4 dB $E_{b}/N_{0}$ range.

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arXiv2019

Low Latency Decoder for Short Blocklength Polar Codes

Polar codes have been gaining a lot of interest due to it being the first coding scheme to provably achieve the symmetric capacity of a binary memoryless channel with an explicit construction. However, the main drawback of polar codes is the low throughput of its successive cancellation (SC) decoding. Simplified SC decoding algorithms of polar codes can be used to reduce the latency of the polar decode by faster processing of specific sub-codes in the polar code. By combining simplified SC with a list decoding technique, such as SC list (SCL) decoding, polar codes can cater to the two conflicting requirements of high reliability and low latency in ultra-reliable low-latency (URLLC) communication systems. Simplified SC algorithm recognizes some special nodes in the SC decoding tree, corresponding to the specific subcodes in the polar code construction, and efficiently prunes the SC decoding tree, without traversing the sub-trees and computing log-likelihood ratios (LLRs) for each child node. However, this decoding process still suffers from the latency associated with the serial nature of SC decoding. We propose some new algorithms to process new types of node patterns that appear within multiple levels of pruned sub-trees and it enables to process of certain nodes in parallel. In short blocklength polar codes, our proposed algorithm can achieve up to $13\%$ latency reduction from fast-simplified SC \cite{Sarkis2013} without any performance degradation. Furthermore, it can achieve up to $27\%$ latency reduction if small error-correcting performance degradation is allowed.

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arXiv2019

Distributed Rate Control in Downlink NOMA Networks with Reliability Constraints

Non-orthogonal multiple access (NOMA) has been identified as a promising technology for future wireless systems due to its performance gains in spectral efficiency when compared to conventional orthogonal schemes (OMA). This gain can be easily translated to an increasing number of served users, but imposes a challenge in the system reliability which is of vital importance for new services and applications of coming cellular systems. To cope with these issues we propose a NOMA rate control strategy that makes use only of topological characteristics of the scenario and the reliability constraint. We attain the necessary conditions so that NOMA overcomes the OMA alternative, while we discuss the optimum allocation strategies for the 2-user NOMA setup when operating with equal rate or maximum sum-rate goals. In such scenario we show that the user with the largest target error probability times the ratio between the average receive signal power and the average interference power, should be scheduled to be decoded first for optimum performance. We compare numerically the performance of our allocation scheme with its ideal counterpart requiring full CSI at the BSs and infinitely long blocklength, and show how the gap increases as the reliability constraint becomes more stringent. Results also evidence the benefits of NOMA when the co-interference can be efficiently canceled, specially when the goal is to maximize the sum-rate.

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arXiv2019

Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive Approach

This letter investigates the problem of providing gigabit wireless access with reliable communication in 5G millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) networks. In contrast to the classical network design based on average metrics, a distributed risk-sensitive reinforcement learning-based framework is proposed to jointly optimize the beamwidth and transmit power, while taking into account the sensitivity of mmWave links due to blockage. Numerical results show that our proposed algorithm achieves more than 9 Gbps of user throughput with a guaranteed probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More importantly, there exists a rate-reliability-network density tradeoff, in which as the user density increases from 16 to 96 per km2, the fraction of users that achieve 4 Gbps are reduced by 11.61% and 39.11% in the proposed and the baseline models, respectively.

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arXiv2019

Achieving Ultra-Reliable Communication via CRAN-Enabled Diversity Schemes

Internet of things is in progress to build a smart society, and wireless networks are critical enablers for many of its use cases. In this paper, we present a multi-coordinated transmission scheme to achieve ultra-reliability for critical machine-type wireless communication networks. We take advantage of diversity, which is fundamental for dealing with fading channel impairments, and for achieving ultra-reliable region of operation in order of five 9's as defined by 3GPP standardization bodies. We evaluate an interference-limited network composed of multiple remote radio heads that are allowed to cooperate, by keeping silence thus reducing interference, or by performing more elaborated strategies such as maximal ratio transmission, in order to serve a user equipment with ultra-reliability. We provide extensive numerical analysis and discuss the gains of cooperation by the centralized radio access network.

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arXiv2019

Fixed Rate Statistical QoS Provisioning for Markovian Sources in Machine Type Communication

In this paper, we study the trade-off between reliability and latency in machine type communication (MTC), which consists of single transmitter and receiver in the presence of Rayleigh fading channel. We assume that the transmitter does not know the channel conditions, therefore it would be transmitting information over a fixed rate. The fixed rate transmission is modeled as a two-state continuous-time Markov process, where the optimum transmission rate is obtained. Moreover, we conduct a performance analysis for different arrival traffic originated from MTC device via effective rate transmission. We consider that the arrival traffic is modeled as a Markovian process namely Discrete-Time Markov process, Fluid Markov process, and Markov Modulated Poisson process, under delay violation constraints. Using effective bandwidth and effective capacity theories, we evaluate the trade-off between reliability-latency and identify QoS (Quality of Service) requirement, and derive lower and upper bounds for the effective capacity subject to channel memory decay rate limits.

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arXiv2019

Hybrid Resource Scheduling for Aggregation in Massive Machine-type Communication Networks

Data aggregation is a promising approach to enable massive machine-type communication (mMTC). Here, we first characterize the aggregation phase where a massive number of machine-type devices transmits to their respective aggregator. By using non-orthogonal multiple access (NOMA), we present a hybrid access scheme where several machine-type devices (MTDs) share the same orthogonal channel. Then, we assess the relaying phase where the aggregatted data is forwarded to the base station. The system performance is investigated in terms of average number of MTDs that are simultaneously served under imperfect successive interference cancellation (SIC) at the aggregator for two scheduling schemes, namely random resource scheduling (RRS) and channel-dependent resource scheduling (CRS), which is then used to assess the performance of data forwarding phase.

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arXiv2019

Joint Power Control and Rate Allocation enabling Ultra-Reliability and Energy Efficiency in SIMO Wireless Networks

Coming cellular systems are envisioned to open up to new services with stringent reliability and energy efficiency requirements. In this paper we focus on the joint power control and rate allocation problem in Single-Input Multiple-Output (SIMO) wireless systems with Rayleigh fading and stringent reliability constraints. We propose an allocation scheme that maximizes the energy efficiency of the system while making use only of average statistics of the signal and interference, and the number of antennas $M$ that are available at the receiver side. We show the superiority of the Maximum Ratio Combining (MRC) scheme over Selection Combining (SC) in terms of energy efficiency, and prove that the gap between the optimum allocated resources converges to $(M!)^(1/(2M))$ as the reliability constraint becomes more stringent. Meanwhile, in most cases MRC was also shown to be more energy efficient than Switch and Stay Combining (SSC) scheme, although this does not hold only when operating with extremely large $M$, extremely high/small average signal/interference power and/or highly power consuming receiving circuitry. Numerical results show the feasibility of the ultra-reliable operation when $M$ increases, while greater the fixed power consumption and/or drain efficiency of the transmit amplifier is, the greater the optimum transmit power and rate.

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arXiv2019

Six Key Enablers for Machine Type Communication in 6G

While 5G is being rolled out in different parts of the globe, few research groups around the world $-$ such as the Finnish 6G Flagship program $-$ have already started posing the question: \textit{What will 6G be?} The 6G vision is a data-driven society, enabled by near instant unlimited wireless connectivity. Driven by impetus to provide vertical-specific wireless network solutions, machine type communication encompassing both its mission critical and massive connectivity aspects is foreseen to be an important cornerstone of 6G development. This article presents an over-arching vision for machine type communication in 6G. In this regard, some relevant performance indicators are first anticipated, followed by a presentation of six key enabling technologies.

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arXiv2019

Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, we investigate the problem of path selection and rate allocation for multi-hop self-backhaul millimeter wave (mmWave) networks. Enabling multi-hop mmWave transmissions raises a potential issue of increased latency, and thus, in this work we aim at addressing the fundamental questions: how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, we propose a new system design, which exploits multiple antenna diversity, mmWave bandwidth, and traffic splitting techniques to improve the downlink transmission. The studied problem is cast as a network utility maximization, subject to an upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: path selection and rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a nonconvex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, we provide a comprehensive performance analysis and convergence proofs for the proposed solution. Numerical results show that our approach ensures reliable communication with a guaranteed probability of up to $99.9999\%$, and reduces latency by $50.64\%$ and $92.9\%$ as compared to baseline models. Furthermore, the results showcase the key trade-off between latency and network arrival rate.

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