References of "IEEE Transactions on Cognitive Communications and Networking"
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See detailA Family of Hybrid Federated and CentralizedLearning Architectures in Machine Learning
Elbir, Ahmet M.; Coleri, Sinem; Papazafeiropoulos, Anastasios K. et al

in IEEE Transactions on Cognitive Communications and Networking (2022)

Many of the machine learning tasks focus on cen-tralized learning (CL), which requires the transmission of localdatasets from the clients to a parameter server (PS) entailing hugecommunication overhead ... [more ▼]

Many of the machine learning tasks focus on cen-tralized learning (CL), which requires the transmission of localdatasets from the clients to a parameter server (PS) entailing hugecommunication overhead. To overcome this, federated learning(FL) has been a promising tool, wherein the clients send only themodel updates to the PS instead of the whole dataset. However,FL demands powerful computational resources from the clients.Therefore, not all the clients can participate in training if they donot have enough computational resources. To address this issue,we introduce a more practical approach called hybrid federatedand centralized learning (HFCL), wherein only the clients withsufficient resources employ FL, while the remaining ones sendtheir datasets to the PS, which computes the model on behalf ofthem. Then, the model parameters corresponding to all clientsare aggregated at the PS. To improve the efficiency of datasettransmission, we propose two different techniques: increasedcomputation-per-client and sequential data transmission. TheHFCL frameworks outperform FL with up to20%improvementin the learning accuracy when only half of the clients perform FLwhile having50%less communication overhead than CL since allthe clients collaborate on the learning process with their datasets. [less ▲]

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See detailCooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems
Ortiz Gomez, Flor de Guadalupe UL; Tarchi, Daniele; Martinez, Ramon et al

in IEEE Transactions on Cognitive Communications and Networking (2022), 8(1),

Very high throughput satellite (VHTS) systems are expected to have a huge increase in traffic demand in the near future. Nevertheless, this increase will not be uniform over the entire service area due to ... [more ▼]

Very high throughput satellite (VHTS) systems are expected to have a huge increase in traffic demand in the near future. Nevertheless, this increase will not be uniform over the entire service area due to the non-uniform distribution of users and changes in traffic demand during the day. This problem is addressed by using flexible payload architectures, which allow the allocation of payload resources flexibly to meet the traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to VHTS systems, so in this paper we discuss the use of one reinforcement learning (RL) algorithm and two deep reinforcement learning (DRL) algorithms to manage the resources available in flexible payload architectures for DRM. These algorithms are Q-Learning (QL), Deep Q-Learning (DQL) and Double Deep Q-Learning (DDQL) which are compared based on their performance, complexity and added latency. On the other hand, this work demonstrates the superiority a cooperative multiagent (CMA) decentralized distribution has over a single agent (SA). [less ▲]

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See detailA family of deep learning architectures for channel estimation and hybrid beamforming in multi-carrier mm-wave massive MIMO.
Elbir, Ahmet M.; Mishra, Kumar Vijay; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Transactions on Cognitive Communications and Networking (2021)

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive ... [more ▼]

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment. [less ▲]

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See detailHybrid Analog-Digital Transceiver Designs for Multi-User MIMO mmWave Cognitive Radio Systems
Tsinos, Christos UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Cognitive Communications and Networking (2020)

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See detailCentralized Power Control in Cognitive Radio Networks Using Modulation and Coding Classification Feedback
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Cognitive Communications and Networking (2016), 2(3),

In this paper, a centralized Power Control (PC) scheme and an interference channel learning method are jointly tackled to allow a Cognitive Radio Network (CRN) access to the frequency band of a Primary ... [more ▼]

In this paper, a centralized Power Control (PC) scheme and an interference channel learning method are jointly tackled to allow a Cognitive Radio Network (CRN) access to the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The learning process enabler is a cooperative Modulation and Coding Classification (MCC) technique which estimates the Modulation and Coding scheme (MCS) of the PU. Due to the lack of cooperation between the PU and the CRN, the CRN exploits this multilevel MCC sensing feedback as implicit channel state information (CSI) of the PU link in order to constantly monitor the impact of the aggregated interference it causes. In this paper, an algorithm is developed for maximizing the CRN throughput (the PC optimization objective) and simultaneously learning how to mitigate PU interference (the optimization problem constraint) by using only the MCC information. Ideal approaches for this problem setting with high convergence rate are the cutting plane methods (CPM). Here, we focus on the analytic center cutting plane method (ACCPM) and the center of gravity cutting plane method (CGCPM) whose effectiveness in the proposed simultaneous PC and interference channel learning algorithm is demonstrated through numerical simulations. [less ▲]

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See detailOn the Performance Analysis of Underlay Cognitive Radio Systems: A Deployment Perspective
Kaushik, A.; Sharma, Shree Krishna UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Cognitive Communications and Networking (2016), 2(3), 273-287

We study the performance of a cognitive underlay system (US) that employs a power control mechanism at the secondary transmitter (ST) from a deployment perspective. Existing baseline models considered for ... [more ▼]

We study the performance of a cognitive underlay system (US) that employs a power control mechanism at the secondary transmitter (ST) from a deployment perspective. Existing baseline models considered for performance analysis either assume the knowledge of involved channels at the ST or retrieve this information by means of a band manager or a feedback channel; however, such situations rarely exist in practice. Motivated by this fact, we propose a novel approach that incorporates estimation of the involved channels at the ST in order to characterize the performance of the US in terms of interference power received at the primary receiver and throughput at the secondary receiver (or secondary throughput). Moreover, we apply an outage constraint that captures the impact of imperfect channel knowledge, particularly on the uncertain interference. Besides this, we employ a transmit power constraint at the ST to classify the operation of the US in terms of an interference-limited regime and a power-limited regime. In addition, we characterize the expressions of the uncertain interference and the secondary throughput for the case where the involved channels encounter Nakagami-m fading. Finally, we investigate a fundamental tradeoff between the estimation time and the secondary throughput depicting an optimized performance of the US. [less ▲]

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See detailCooperative Estimation of Power and Direction of Transmission for a Directive Source
Maleki, Sina UL; Ciblat, Philippe; Chatzinotas, Symeon UL et al

in IEEE Transactions on Cognitive Communications and Networking (2016)

Reliable spectrum cartography of directive sources depends on an accurate estimation of the direction of transmission (DoT) as well as the transmission power. Joint estimation of power and DoT of a ... [more ▼]

Reliable spectrum cartography of directive sources depends on an accurate estimation of the direction of transmission (DoT) as well as the transmission power. Joint estimation of power and DoT of a directive source using ML estimation techniques is considered in this paper. We further analyze the parametric identifiability conditions of the problem, develop the estimation algorithm, and derive the Cramer-Rao-Bound (CRB) for the two situations: a) where the source signal is known to the sensors, and b) where the sensors are not aware of the source signal but its distribution. Particularly, we devise a specific sensor placement/selection setup for the symmetric antenna patterned sources which leads to identifiability of the problem. Finally, numerical results verifies the efficiency and accuracy of the provided estimation algorithms in this paper. [less ▲]

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See detailResource Allocation for Cognitive Satellite Communications with Incumbent Terrestrial Networks
Lagunas, Eva UL; Sharma, Shree Krishna UL; Maleki, Sina UL et al

in IEEE Transactions on Cognitive Communications and Networking (2015)

The lack of available unlicensed spectrum together with the increasing spectrum demand by multimedia applications has resulted in a spectrum scarcity problem, which affects Satellite Communications ... [more ▼]

The lack of available unlicensed spectrum together with the increasing spectrum demand by multimedia applications has resulted in a spectrum scarcity problem, which affects Satellite Communications (SatCom) as well as terrestrial systems. The goal of this paper is to propose Resource Allocation (RA) techniques, i.e. carrier, power and bandwidth allocation, for a cognitive spectrum utilization scenario where the satellite system aims at exploiting the spectrum allocated to terrestrial networks as the incumbent users without imposing harmful interference to them. In particular, we focus on the microwave frequency bands 17.7-19.7 GHz for the cognitive satellite downlink and 27.5-29.5 GHz for the cognitive satellite uplink, although the proposed techniques can be easily extended to other bands. In the first case, assuming that the satellite terminals are equipped with multiple Low Block Noise Converters (LNB), we propose a joint beamforming and carrier allocation scheme to enable cognitive Space-to-Earth communications in the shared spectrum where Fixed Service (FS) microwave links have priority of operation. In the second case, however, the cognitive satellite uplink should not cause harmful interference to the incumbent FS system. For the latter, we propose a Joint Power and Carrier Allocation (JPCA) strategy followed by a bandwidth allocation scheme which guarantees protection of the terrestrial FS system while maximizing the satellite total throughput. The proposed cognitive satellite exploitation techniques are validated with numerical simulations considering realistic system parameters. It is shown that the proposed cognitive exploitation framework represents a promising approach for enhancing the throughput of conventional satellite systems. [less ▲]

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