arXiv subjects

Halim Yanikomeroglu

Publications and source records attributed to Halim Yanikomeroglu.

At least 253 records · Page 14Linked to original sources

arXiv2020

Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines

In this paper, we focus on energy-efficient UAV-based IoT data collection in sensor networks in which the sensed data have different time deadlines. In the investigated setting, the sensors are clustered and managed by cluster heads (CHs), and multiple UAVs are used to collect data from the CHs. The formulated problem is solved through a two-step approach. In the first step, an efficient method is proposed to determine the minimal number of CHs and their best locations. Subsequently, the minimal number of UAVs and their trajectories are obtained by solving the associated capacitated vehicle routing problem. Results show the efficiency of our proposed CHs placement method compared to baseline approaches, where bringing the CHs closer to the dockstation allows significant energy savings. Moreover, among different UAV trajectory planning algorithms, Tabu search achieves the best energy consumption. Finally, the impact of the battery capacity and time deadline are investigated in terms of consumed energy, number of visited CHs, and number of deployed UAVs.

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arXiv2020

Downlink Coverage and Rate Analysis of an Aerial User in Vertical Heterogeneous Networks (VHetNets)

In this paper, we analyze the downlink coverage probability and rate of an aerial user in vertical HetNets (VHetNets) comprising aerial base stations (aerial-BSs) and terrestrial-BSs. The locations of terrestrial-BSs are modeled as an infinite 2-D Poisson Point Process (PPP), while the locations of aerial-BSs are modeled as a finite 2-D Binomial Point Process (BPP). Our cellular-to-air (C2A) channel model incorporates line-of-sight (LoS) and non-LoS transmissions between terrestrial-BSs and a typical aerial user, while we assume LoS transmissions for all aerial links. We assume that the aerial user is associated with an aerial-BS or terrestrial-BS that provides the strongest average received power. Using stochastic geometry, we derive exact and approximate expressions of the coverage probability and rate in terms of interference power's Laplace transform. The expressions are simplified assuming only LoS transmissions for the C2A channels. This enables easy-to-compute equations with good accuracy at elevated aerial user heights. We find that aerial users hovering at low altitudes tend to connect to aerial-BSs in denser terrestrial environments. Employing directive beamforming at aerial-BSs guarantees an acceptable performance at the aerial user by reducing interference signals received from the aerial-BSs. In denser terrestrial networks, the performance at the aerial user degrades substantially despite beamforming.

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arXiv2020

A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks

The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. Particularly, this paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a profound study of the 6G vision and outlining five of its disruptive technologies, i.e., terahertz communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss their requirements, key challenges, and open research problems.

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arXiv2020

Securing the Inter-Spacecraft Links: Doppler Frequency Shift based Physical Layer Key Generation

We propose a novel physical layer secret key generation method for the inter-spacecraft communication links. By exploiting the Doppler frequency shifts of the reciprocal spacecraft links as a unique secrecy source, spacecrafts aim to obtain identical secret keys from their individual observations. We obtain theoretical expressions for the key disagreement rate (KDR). Using generalized Gauss-Laguerre quadrature, we derive closed form expressions for the KDR. Through numerical studies, the tightness of the provided approximations are shown. Both the theoretical and numerical results demonstrate the validity and the practicality of the presented physical layer key generation procedure considering the security of the communication links of spacecrafts.

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arXiv2020

On the Optimal 3D Placement of a UAV Base Station for Maximal Coverage of UAV Users

Unmanned aerial vehicles (UAVs) can be users that support new applications, or be communication access points that serve terrestrial and/or aerial users. In this paper, we focus on the connectivity problem of aerial users when they are exclusively served by aerial base stations (BS), i.e., UAVBSs. Specifically, the 3D placement problem of a directional antenna equipped UAV-BS, aiming to maximize the number of covered aerial users under a spectrum sharing policy with terrestrial networks, is investigated. Given a known spectrum sharing policy between the aerial and terrestrial networks, we propose a 3D placement algorithm that achieves optimality. Simulation results show the performance of our approach, in terms of number of covered aerial users, for different configurations and parameters, such as the spectrum sharing policy, antenna beamwidth, transmit power, and aerial users density. These results represent novel guidelines for exclusive aerial networks deployment and applications, distinctively for orthogonal and non-orthogonal spectrum sharing policies with terrestrial networks.

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arXiv2020

An Application-Driven Non-Orthogonal Multiple Access Enabled Computation Offloading Scheme

To cope with the unprecedented surge in demand for data computing for the applications, the promising concept of multi-access edge computing (MEC) has been proposed to enable the network edges to provide closer data processing for mobile devices (MDs). Since enormous workloads need to be migrated, and MDs always remain resource-constrained, data offloading from devices to the MEC server will inevitably require more efficient transmission designs. The integration of nonorthogonal multiple access (NOMA) technique with MEC has been shown to provide applications with lower latency and higher energy efficiency. However, existing designs of this type have mainly focused on the transmission technique, which is still insufficient. To further advance offloading performance, in this work, we propose an application-driven NOMA enabled computation offloading scheme by exploring the characteristics of applications, where the common data of the application is offloaded through multi-device cooperation. Under the premise of successfully offloading the common data, we formulate the problem as the maximization of individual offloading throughput, where the time allocation and power control are jointly optimized. By using the successive convex approximation (SCA) method, the formulated problem can be iteratively solved. Simulation results demonstrate the convergence of our method and the effectiveness of the proposed scheme.

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arXiv2020

Learning Power Control from a Fixed Batch of Data

We address how to exploit power control data, gathered from a monitored environment, for performing power control in an unexplored environment. We adopt offline deep reinforcement learning, whereby the agent learns the policy to produce the transmission powers solely by using the data. Experiments demonstrate that despite discrepancies between the monitored and unexplored environments, the agent successfully learns the power control very quickly, even if the objective functions in the monitored and unexplored environments are dissimilar. About one third of the collected data is sufficient to be of high-quality and the rest can be from any sub-optimal algorithm.

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arXiv2020

Faded-Experience Trust Region Policy Optimization for Model-Free Power Allocation in Interference Channel

Policy gradient reinforcement learning techniques enable an agent to directly learn an optimal action policy through the interactions with the environment. Nevertheless, despite its advantages, it sometimes suffers from slow convergence speed. Inspired by human decision making approach, we work toward enhancing its convergence speed by augmenting the agent to memorize and use the recently learned policies. We apply our method to the trust-region policy optimization (TRPO), primarily developed for locomotion tasks, and propose faded-experience (FE) TRPO. To substantiate its effectiveness, we adopt it to learn continuous power control in an interference channel when only noisy location information of devices is available. Results indicate that with FE-TRPO it is possible to almost double the learning speed compared to TRPO. Importantly, our method neither increases the learning complexity nor imposes performance loss.

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arXiv2020

Wireless Networks with Cache-Enabled and Backhaul-Limited Aerial Base Stations

Use of aerial base stations (ABSs) is a promising approach to enhance the agility and flexibility of future wireless networks. ABSs can improve the coverage and/or capacity of a network by moving supply towards demand. Deploying ABSs in a network presents several challenges such as finding an efficient 3D-placement of ABSs that takes network objectives into account. Another challenge is the limited wireless backhaul capacity of ABSs and consequently, potentially higher latency incurred. Content caching is proposed to alleviate the backhaul congestion and decrease the latency. We consider a limited backhaul capacity for ABSs due to varying position-dependent path loss values and define two groups of users (delay-tolerant and delay-sensitive) with different data rate requirements. We study the problem of jointly determining backhaul-aware 3D placement for ABSs, user-BS associations and corresponding bandwidth allocations while minimizing total downlink transmit power. Proposed iterative algorithm applies a decomposition method. First, the 3D locations of ABSs are found using semi-definite relaxation and coordinate descent methods, and then user-BS associations and bandwidth allocations are optimized. The simulation results demonstrate the effectiveness of the proposed algorithm and provide insights about the impact of traffic distribution and content caching on transmit power and backhaul usage of ABSs.

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arXiv2020

A Survey of Rate-optimal Power Domain NOMA with Enabling Technologies of Future Wireless Networks

The ambitious high data-rate applications in the envisioned future B5G networks require new solutions, including the advent of more advanced architectures than the ones already used in 5G networks, and the coalition of different communications schemes and technologies to enable these applications requirements. Among the candidate schemes for future wireless networks are NOMA schemes that allow serving more than one user in the same resource block by multiplexing users in other domains than frequency or time. In this way, NOMA schemes tend to offer several advantages over OMA schemes such as improved user fairness and spectral efficiency, higher cell-edge throughput, massive connectivity support, and low transmission latency. With these merits, NOMA-enabled transmission schemes are being increasingly looked at as promising multiple access schemes for future wireless networks. When the power domain is used to multiplex the users, it is referred to as PD-NOMA. In this paper, we survey the integration of PD-NOMA with the enabling communications schemes and technologies that are expected to meet the various requirements of B5G networks. In particular, this paper surveys the different rate optimization scenarios studied in the literature when PD-NOMA is combined with one or more of the candidate schemes and technologies for B5G networks including MISO, MIMO, mMIMO, advanced antenna architectures, mmWave and THz, CoMP, cooperative communications, cognitive radio, VLC, UAV and others. The considered system models, the optimization methods utilized to maximize the achievable rates, and the main lessons learnt on the optimization and the performance of these NOMA-enabled schemes and technologies are discussed in detail along with the future research directions for these combined schemes. Moreover, the role of machine learning in optimizing these NOMA-enabled technologies is addressed.

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arXiv2020

Trajectory Design and Power Allocation for Drone-Assisted NR-V2X Network with Dynamic NOMA/OMA

In this paper, we find trajectory planning and power allocation for a vehicular network in which an unmanned-aerial-vehicle (UAV) is considered as a relay to extend coverage for two disconnected far vehicles. We show that in a two-user network with an amplify-and-forward (AF) relay, non-orthogonal-multiple-access (NOMA) always has better or equal sum-rate in comparison to orthogonal-multiple-access (OMA) at high signal-to-noise-ratio (SNR) regime. However, for the cases where i) base station (BS)-to-relay link is weak, or ii) two users have similar links, or iii) BS-to-relay link is similar to relay-to-weak user link, applying NOMA has negligible sum-rate gain. Hence, due to the complexity of successive-interference-cancellation (SIC) decoding in NOMA, we propose a dynamic NOMA/OMA scheme in which OMA mode is selected for transmission when applying NOMA has only negligible gain. Also, we show that OMA always has better min-rate than NOMA at high SNR regime. Further, we formulate two optimization problems which maximize the sum-rate and min-rate of the two vehicles. These problems are non-convex, and hence we propose an iterative algorithm based on alternating-optimization (AO) method which solves trajectory and power allocation sub-problems by successive-convex-approximation (SCA) and difference-of-convex (DC) methods, respectively. Finally, the above-mentioned performance is confirmed by simulations.

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arXiv2020

Non-Orthogonal Multiple Access in the Presence of Additive Generalized Gaussian Noise

In this letter, we investigate the performance of non-orthogonal multiple access (NOMA), under the assumption of generalized Gaussian noise (GGN), over Rayleigh fading channels. Specifically, we consider a NOMA system with $L$ users, each of which is equipped with a single antenna, and derive an exact expression for the pairwise error probability (PEP). The derived PEP expression is subsequently utilized to derive a union bound on the bit error rate (BER) and to quantify the diversity orders realized by NOMA users in the presence of additive white (AW) GGN. Capitalizing on the derived PEP expression and the union bound, the error rate performance of NOMA users is further evaluated for different special cases of AWGGN. The derived analytical results, corroborated by simulation results, show that the shaping parameter of the GGN $(α)$ has negligible effect on the diversity gains of NOMA users, particularly for large $α$ values. Accordingly, as in the case of additive white Gaussian noise (AWGN), the maximum achievable diversity order is determined by the user's order.

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arXiv2020

Optimal Location of Cellular Base Station via Convex Optimization

An optimal base station (BS) location depends on the traffic (user) distribution, propagation pathloss and many system parameters, which renders its analytical study difficult so that numerical algorithms are widely used instead. In this paper, the problem is studied analytically. First, it is formulated as a convex optimization problem to minimize the total BS transmit power subject to quality-of-service (QoS) constraints, which also account for fairness among users. Due to its convex nature, Karush-Kuhn-Tucker (KKT) conditions are used to characterize a globally-optimum location as a convex combination of user locations, where convex weights depend on user parameters, pathloss exponent and overall geometry of the problem. Based on this characterization, a number of closed-form solutions are obtained. In particular, the optimum BS location is the mean of user locations in the case of free-space propagation and identical user parameters. If the user set is symmetric (as defined in the paper), the optimal BS location is independent of pathloss exponent, which is not the case in general. The analytical results show the impact of propagation conditions as well as system and user parameters on optimal BS location and can be used to develop design guidelines.

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arXiv2020

Polar Coded Faster-than-Nyquist (FTN) Signaling with Symbol-by-Symbol Detection

Reduced complexity faster-than-Nyquist (FTN) signaling systems are gaining increased attention as they provide improved bandwidth utilization for an acceptable level of detection complexity. In order to have a better understanding of the tradeoff between performance and complexity of the reduced complexity FTN detection techniques, it is necessary to study these techniques in the presence of channel coding. In this paper, we investigate the performance a polar coded FTN system which uses a reduced complexity FTN detection, namely, the recently proposed successive symbol-by-symbol with go-backK sequence estimation (SSSgbKSE) technique. Simulations are performed for various intersymbol-interference (ISI) levels and for various go-back-K values. Bit error rate (BER) performance of Bahl-Cocke-Jelinek-Raviv (BCJR) detection and SSSgbKSE detection techniques are studied for both uncoded and polar coded systems. Simulation results reveal that polar codes can compensate some of the performance loss incurred in the reduced complexity SSSgbKSE technique and assist in closing the performance gap between BCJR and SSSgbKSE detection algorithms.

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arXiv2020

Backhaul-Aware Optimization of UAV Base Station Location and Bandwidth Allocation for Profit Maximization

Unmanned Aerial Vehicle Base Stations (UAVBSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) by dynamically moving the supply towards the demand. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAVBS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAVBS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAVBS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.

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arXiv2020

Mobility-assisted Over-the-Air Computation for Backscatter Sensor Networks

Future intelligent systems will consist of a massive number of battery-less sensors, where quick and accurate aggregation of sensor data will be of paramount importance. Over-the-air computation (AirComp) is a promising technology wherein sensors concurrently transmit their measurements over the wireless channel, and a reader receives the noisy version of a function of measurements due to the superposition property. A key challenge in AirComp is the accurate power alignment of individual transmissions, addressed previously by using conventional precoding methods. In this paper, we investigate a UAVenabled backscatter communication framework, wherein UAV acts both as a power emitter and reader. The mobility of the reader is leveraged to replace the complicated precoding at sensors, where UAV first collects sum channel gains in the first flyover, and then, use these to estimate the actual aggregated sensor data in the second flyover. Our results demonstrate improvements of up to 10 dB in MSE compared to that of a benchmark case where UAV is incognizant of sum channel gains.

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arXiv2019

Low-Complexity Detection for Faster-than-Nyquist Signaling based on Probabilistic Data Association

In this paper, we investigate the sequence estimation problem of faster-than-Nyquist (FTN) signaling as a promising approach for increasing spectral efficiency (SE) in future communication systems. In doing so, we exploit the concept of Gaussian separability and propose two probabilistic data association (PDA) algorithms with polynomial time complexity to detect binary phase-shift keying (BPSK) FTN signaling. Simulation results show that the proposed PDA algorithm outperforms the recently proposed SSSSE and SSSgb$K$SE algorithms for all SE values with a modest increase in complexity. The PDA algorithm approaches the performance of the semidefinite relaxation (SDRSE) algorithm for SE values of $0.96$ bits/sec/Hz, and it is within the $0.5$ dB signal-to-noise ratio (SNR) penalty at SE values of $1.10$ bits/sec/Hz for the fixed values of $β= 0.3$.

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arXiv2019

Multi-UAV Data Collection Framework for Wireless Sensor Networks

In this paper, we propose a framework design for wireless sensor networks based on multiple unmanned aerial vehicles (UAVs). Specifically, we aim to minimize deployment and operational costs, with respect to budget and power constraints. To this end, we first optimize the number and locations of cluster heads (CHs) guaranteeing data collection from all sensors. Then, to minimize the data collection flight time, we optimize the number and trajectories of UAVs. Accordingly, we distinguish two trajectory approaches: 1) where a UAV hovers exactly above the visited CH; and 2) where a UAV hovers within a range of the CH. The results of this include guidelines for data collection design. The characteristics of sensor nodes' K-means clustering are then discussed. Next, we illustrate the performance of optimal and heuristic solutions for trajectory planning. The genetic algorithm is shown to be near-optimal with only $3.5\%$ degradation. The impacts of the trajectory approach, environment, and UAVs' altitude are investigated. Finally, fairness of UAVs trajectories is discussed.

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