| 1. |
- Al-Hraishawi, Hayder, et al.
(författare)
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Multi-Cell Massive MIMO Uplink With Underlay Spectrum Sharing
- 2019
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : IEEE. - 2332-7731. ; 5:1, s. 119-137
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Tidskriftsartikel (refereegranskat)abstract
- The achievable rates are investigated for multicell multi-user massive multiple-input multiple-output (MIMO) systems with underlay spectrum sharing. A general pilot sharing scheme and two pilot sequence designs (PSDs) are investigated via fully shared (PSD-1) and partially shared (PSD-2) uplink pilots. The number of simultaneously served primary users and secondary users (SUs) in the same time-frequency resource block by the PSD-1 is higher than that of PSD-2. The transmit power constraints for the SUs are derived to mitigate the secondary co-channel interference (CCI) inflicted at the primary base-station (PBS) subject to a predefined primary interference temperature (PIT). The optimal transmit power control coefficients for the SUs with max-min fairness and the common achievable rates are derived. The cumulative detrimental effects of channel estimation errors, CCI and intra-cell/inter-cell pilot contamination are investigated. The secondary transmit power constraint and the achievable rates for the perfect channel state information (CSI) case become independent of the PIT when the number of PBS antennas grows unbounded. Therefore, the primary and secondary systems can be operated independent of each other as both intra-cell and inter-cell interference can be asymptotically mitigated at the massive MIMO PBS and secondary base-station. Nevertheless, the achievable rates and secondary power constraints for the imperfect CSI case with PSD-1 are severely degraded due to the presence of intra-cell and inter-cell pilot contamination. These performance metrics depend on the PIT even in the asymptotic PBS antenna regime. Hence, the primary and secondary systems can no longer be operated independently for imperfect CSI with PSD-1. However, PSD-2 provides an achievable rate gain over PSD-1 despite the requirement of lengthier pilot sequences of the former than that of the latter.
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| 2. |
- Bhar, Chayan, et al.
(författare)
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Resource-Efficient QoS-Aware Video Streaming Using UAV-Assisted Networks
- 2024
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - 2332-7731. ; 10:2, s. 649-659
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Tidskriftsartikel (refereegranskat)abstract
- Emerging video services are associated with stringent quality-of-service (QoS) and high data-rate requirements. Moreover, the presence of data-rate-hungry mobile users in future networks necessitate sophisticated design strategies. The deployment of unmanned aerial vehicle access point (UAP)-assisted networks (UANs) has been proposed to ensure high data-rates to mobile users. Moreover, UAPs can be equipped with energy-efficient caches to facilitate video delivery with stringent QoS. However, the mobility of users and UAPs may cause temporal variations in the QoS experienced by users. This paper conducts an extensive performance evaluation of a UAN, by studying the effect of user behavior, mobility of users and UAPs, and a temporal variation of video popularity on the QoS. The QoS is measured in terms of the delay experienced by the users. To that end, a time-dependent queueing model and its associated fluid approximation models are derived, which are illustrated to be reasonably accurate in an appropriate asymptotic regime. A detailed analysis of these models reveals that low delay, i.e., high QoS, can be ensured in UANs. Finally, a reinforcement-learning (RL) approach based on these models is utilized to minimize the number of deployed UAPs and the playout buffer size while guaranteeing a certain QoS.
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| 3. |
- Biswas, Sinchan, et al.
(författare)
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Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting
- 2019
-
Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 5:2, s. 382-399
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Tidskriftsartikel (refereegranskat)abstract
- This paper focuses on the problem of sensing throughput optimization in a fading multiple access cognitive radio (CR) network, where the secondary user (SU) transmitters participate in cooperative spectrum sensing and are capable of harvesting energy and sharing energy with each other. We formulate the optimization problem as a maximization of the expected achievable sum-rate over a finite horizon, subject to an average interference constraint at the primary receiver, peak power constraints, and energy causality constraints at the SU transmitters. The optimization problem is a non-convex, mixed integer non-linear program (MINLP) involving the binary action to sense the spectrum or not, and the continuous variables, such as the transmission power, shared energy, and sensing time. The problem is analyzed under two different assumptions on the available information pattern: 1) non-causal channel state information (CSI), energy state information (ESI), and infinite battery capacity and 2) the more realistic scenario of the causal CSI/ESI and finite battery. In the non-casual case, this problem can be solved by an exhaustive search over the decision variable or an MINLP solver for smaller problem dimensions, and a novel heuristic policy for larger problems, combined with an iterative alternative optimization method for the continuous variables. The causal case with finite battery is optimally solved using a dynamic programming (DP) methodology, whereas a number of sub-optimal algorithms are proposed to reduce the computational complexity of DP. Extensive numerical simulations are carried out to illustrate the performance of the proposed algorithms. One of the main findings indicates that the energy sharing is more beneficial when there is a significant asymmetry between average harvested energy levels/channel gains of different SUs.
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| 4. |
- Elbir, Ahmet M., et al.
(författare)
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A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO
- 2021
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Huang, Shaocheng, et al.
(författare)
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Learning Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems
- 2021
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 7:1, s. 120-132
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Tidskriftsartikel (refereegranskat)abstract
- Millimeter Wave (mmWave) communications with full-duplex (FD) have the potential of increasing the spectral efficiency, relative to those with half-duplex. However, the residual self-interference (SI) from FD and high pathloss inherent to mmWave signals may degrade the system performance. Meanwhile, hybrid beamforming (HBF) is an efficient technology to enhance the channel gain and mitigate interference with reasonable complexity. However, conventional HBF approaches for FD mmWave systems are based on optimization processes, which are either too complex or strongly rely on the quality of channel state information (CSI). We propose two learning schemes to design HBF for FD mmWave systems, i.e., extreme learning machine based HBF (ELM-HBF) and convolutional neural networks based HBF (CNN-HBF). Specifically, we first propose an alternating direction method of multipliers (ADMM) based algorithm to achieve SI cancellation beamforming, and then use a majorization-minimization (MM) based algorithm for joint transmitting and receiving HBF optimization. To train the learning networks, we simulate noisy channels as input, and select the hybrid beamformers calculated by proposed algorithms as targets. Results show that both learning based schemes can provide more robust HBF performance and achieve at least 22.1% higher spectral efficiency compared to orthogonal matching pursuit (OMP) algorithms. Besides, the online prediction time of proposed learning based schemes is almost 20 times faster than the OMP scheme. Furthermore, the training time of ELM-HBF is about 600 times faster than that of CNN-HBF with 64 transmitting and receiving antennas.
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| 6. |
- Imtiaz, Sahar, et al.
(författare)
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Coordinates-Based Resource Allocation Through Supervised Machine Learning
- 2021
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 7:4, s. 1347-1362
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Tidskriftsartikel (refereegranskat)abstract
- Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies. Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions. In such cases, an estimate of the terminals' position information provides an alternative to estimating the channel condition. In this work, we propose a coordinates-based resource allocation scheme using supervised machine learning techniques, and investigate how efficiently this scheme performs in comparison to the traditional approach under various propagation conditions. We consider a simple system setup as a first step, where a single transmitter serves a single mobile user. The performance results show that the coordinates-based resource allocation scheme achieves a performance very close to the CSI-based scheme, even when the available user's coordinates are erroneous. The performance is quite consistent, especially when complex learning frameworks like random forest and neural network are used for resource allocation. In terms of applicability, a training time of about 4 s is required for coordinates-based resource allocation using random forest algorithm, and the appropriate resource allocation is predicted in less than 90 mu s with a learnt model of size <1 kB.
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| 7. |
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| 8. |
- Khosravi, Sara, et al.
(författare)
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Learning-based Handover in Mobile Millimeter-wave Networks
- 2021
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers Inc.. - 2332-7731. ; 7:2, s. 663-674
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Tidskriftsartikel (refereegranskat)abstract
- Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data rates in the future cellular and wireless networks. The need for directional communication between base stations (BSs) and users in mmWave systems, that is achieved through beamforming, increases the complexity of the channel estimation. Moreover, in order to provide better coverage, dense deployment of BSs is required which causes frequent handovers and increased association overhead. In this paper, we present an approach that jointly addresses the beamforming and handover problems. Our solution entails an efficient beamforming method with a few number of pilots and a learning-based handover method supporting mobile scenarios. We use reinforcement learning algorithm to learn the optimal choices of the backup BSs in different locations of a mobile user. We show that our method provides an almost constant rate and reliability in all locations of the user’s trajectory with a small number of handovers. Simulation results in an outdoor environment based on narrow band cluster mmWave channel modeling and real building map data show the superior performance of our proposed solution in achievable instantaneous rate and trajectory rate.
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| 9. |
- Lagunas, E., et al.
(författare)
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Resource Allocation for Cognitive Satellite Communications With Incumbent Terrestrial Networks
- 2015
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 1:3, s. 305-317
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 10. |
- Lei, Wanlu, et al.
(författare)
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Deep reinforcement learning-based spectrum allocation in integrated access and backhaul networks
- 2020
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 6:3, s. 970-979
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Tidskriftsartikel (refereegranskat)abstract
- We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deployment and dynamic environment. The available spectrum is divided into several orthogonal sub-channels, and the donor base station (DBS) and all IAB nodes have the same spectrum resource for allocation, where a DBS utilizes those sub-channels for access links of associated user equipment (UE) as well as for backhaul links of associated IAB nodes, and an IAB node can utilize all for its associated UEs. This is one of key features in which 5G differs from traditional settings where the backhaul networks are designed independently from the access networks. With the goal of maximizing the sum log-rate of all UE groups, we formulate the spectrum allocation problem into a mix-integer and non-linear programming. However, it is intractable to find an optimal solution especially when the IAB network is large and time-varying. To tackle this problem, we propose to use the latest DRL method by integrating an actor-critic spectrum allocation (ACSA) scheme and deep neural network (DNN) to achieve real-time spectrum allocation in different scenarios. The proposed methods are evaluated through numerical simulations and show promising results compared with some baseline allocation policies.
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| 11. |
- Liang, Ying-Chang, et al.
(författare)
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Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks
- 2020
-
Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2332-7731. ; 6:4, s. 1242-1255
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Tidskriftsartikel (refereegranskat)abstract
- The heterogenous wireless services and exponentially growing traffic call for novel spectrum- and energy-efficient wireless communication technologies. Recently, a new technique, called symbiotic radio (SR), is proposed to exploit the benefits and address the drawbacks of cognitive radio (CR) and ambient backscattering communications (AmBC), leading to mutualism spectrum sharing and highly reliable backscattering communications. In particular, the secondary transmitter (STx) in SR transmits messages to the secondary receiver (SRx) over the RF signals originating from the primary transmitter (PTx) based on cognitive backscattering communications, thus the secondary system shares not only the radio spectrum, but also the power, and infrastructure with the primary system. In return, the secondary transmission provides beneficial multipath diversity to the primary system, therefore the two systems form mutualism spectrum sharing. More importantly, joint decoding is exploited at SRx to achieve highly reliable backscattering communications. In this article, to exploit the full potential of SR, we provide a systematic view for SR and address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTxs RF signals using reconfigurable intelligent surfaces. Emerging applications, design challenges and open research problems will also be discussed.
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| 12. |
- Lin, Pin-Hsun, et al.
(författare)
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Multi-Phase Smart Relaying and Cooperative Jamming in Secure Cognitive Radio Networks
- 2016
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 2:1, s. 38-52
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we investigate cooperative secure communications in a four-node cognitive radio network where the secondary receiver is treated as a potential eavesdropper with respect to the primary transmission. The secondary user is allowed to transmit his own signals under the condition that the primary user's secrecy rate and transmission scheme are intact. Under this setting, we derive the secondary user's achievable rates and the related constraints to guarantee the primary user's weak secrecy rate, when Gelfand-Pinsker coding is used at the secondary transmitter. In addition, we propose a multiphase transmission scheme to include: 1) the phases of the clean relaying with cooperative jamming and 2) the latency to successfully decode the primary message at the secondary transmitter. A capacity upper bound for the secondary user is also derived. Numerical results show that: 1) the proposed scheme can outperform the traditional ones by properly selecting the secondary user's parameters of different transmission schemes according to the relative positions of the nodes and 2) the derived capacity upper bound is close to the secondary user's achievable rate within 0.3 bits/channel use, especially when the secondary transmitter/receiver is far/close enough to the primary receiver/transmitter, respectively. Thereby, a smart secondary transmitter is able to adapt the relaying and cooperative jamming to guarantee primary secrecy rates and to transmit its own data at the same time from relevant geometric positions.
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| 13. |
- Maleki, Sina, et al.
(författare)
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Cooperative Estimation of Power and Direction of Transmission for a Directive Source
- 2016
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - 2332-7731. ; 2:4, s. 343-357
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Tidskriftsartikel (refereegranskat)abstract
- 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 for the two situations: 1) where the source signal is known to the sensors and 2) 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.
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| 14. |
- Manoj, B. R., et al.
(författare)
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Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training
- 2022
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2332-7731. ; 8:2, s. 707-719
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Tidskriftsartikel (refereegranskat)abstract
- The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial attacks. Although there has been much work demonstrating the susceptibility of DL-based classification tasks to adversarial attacks, regression-based problems in the context of a wireless system have not been studied so far from an attack perspective. The aim of this paper is twofold: (i) we consider a regression problem in a wireless setting and show that adversarial attacks can break the DL-based approach and (ii) we analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly. Specifically, the wireless application considered in this paper is the DL-based power allocation in the downlink of a multicell massive multi-input-multi-output system, where the goal of the attack is to yield an infeasible solution by the DL model. We extend the gradient-based adversarial attacks: fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent method to analyze the susceptibility of the considered wireless application with and without adversarial training. We analyze the deep neural network (DNN) models performance against these attacks, where the adversarial perturbations are crafted using both the white-box and black-box attacks.
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| 15. |
- Parera, Claudia, et al.
(författare)
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Transfer Learning for Tilt-Dependent Radio Map Prediction
- 2020
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2332-7731. ; 6:2, s. 829-843
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning will play a major role in handling the complexity of future mobile wireless networks by improving network management and orchestration capabilities. Due to the large number of parameters that can be monitored and configured in the network, collecting and processing high volumes of data is often unfeasible or too expensive at network runtime, which calls for taking resource management and service orchestration decisions with only a partial view of the network status. Motivated by this fact, this paper proposes a transfer learning framework for reconstructing the radio map corresponding to a target antenna tilt configuration by transferring the knowledge acquired from another tilt configuration of the same antenna, when no or very limited measurements are available from the target. The performance of the framework is validated against standard machine learning techniques on a data set collected from a 4G commercial base stations. In most of the tested scenarios, the proposed framework achieves notable prediction accuracy with respect to classical machine learning approaches, with a mean absolute percentage error below 8%.
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| 16. |
- Qian, Liangxin, et al.
(författare)
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Multi-Dimensional Polarized Modulation for Land Mobile Satellite Communications
- 2021
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 7:2, s. 383-397
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Tidskriftsartikel (refereegranskat)abstract
- In this article, a novel multiple-input multiple-out (MIMO) transmission scheme, called generalized polarized enhanced spatial modulation (GPESM), is proposed for dual-polarized land mobile satellite (LMS) communications. We first introduce the enhanced spatial modulation (ESM) technique for dual-polarized LMS communications, in which polarization dimension, spatial dimension and multiple signal constellations are used to transmit information and obtain substantial performance gain. Meanwhile, the theoretical upper bound for the average bit error probability (ABEP) of the proposed GPESM scheme is derived. In order to further improve the reliability of the system, we also propose two novel power allocation (PA) algorithms for GPESM system, which are the optimization-driven approximated max-min distance (AMMD)-based PA algorithm and the data-driven deep neural network (DNN)-based PA algorithm. To achieve an enhanced spatial diversity gain, we consider to apply a reconfigurable intelligent surface (RIS) to the GPESM system as a relay to assist in transmitting information. In this way, the user can receive the information transmitted by the satellite on one hand, and the information sent by the satellite via the RIS relay on the other hand. We also extend the above-mentioned two PA algorithms to the RIS-assisted GPESM systems. Our simulation results show that the RIS-assisted GPESM systems are capable of obtaining high bit error rate (BER) performance gain (up to 10 dB) compared to the standard GPESM system and two PA algorithms can further improve the performance to the systems.
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| 17. |
- Ruiz, F. J. R., et al.
(författare)
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Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
- 2018
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - 2332-7731. ; 4:2, s. 177-191
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Tidskriftsartikel (refereegranskat)abstract
- New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.
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| 18. |
- Saxena, Vidit, et al.
(författare)
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Optimal UAV Base Station Trajectories Using Flow-Level Models for Reinforcement Learning
- 2019
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2332-7731. ; 5:4, s. 1101-1112
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Tidskriftsartikel (refereegranskat)abstract
- Cellular base stations (BS) and remote radio heads can be mounted on unmanned aerial vehicles (UAV) for flexible, traffic-aware deployment. These UAV base station networks (UAVBSN) promise an unprecendented degree of freedom that can be exploited for spectral efficiency gains as well as optimal network utilization. However, the current literature lacks realistic radio and traffic models for UAVBSN deployment planning and for performance evaluation. In this paper, we propose flow-level models (FLM) for realistically characterizing the UAVBSN performance in terms of a broad range of flow- and system-level metrics. Further, we propose a deep reinforcement learning (DRL) approach that relies on the UAVBSN FLM for learning the optimal traffic-aware UAV trajectories. For a given user traffic density and starting UAV locations, our RL approach learns the optimal UAV trajectories offline that maximizes a cumulative performance metric. We then execute the learned UAV trajectories in a discrete event simulator to evaluate online UAVBSN performance. For M = 9 UAVs deployed in a simulated Downtown San Francisco model, where the UAV trajectories are defined by N = 20 discrete actions, our approach achieves approximately a three-fold increase in the average user throughput compared to the initial UAV placement, while simultaneously balancing traffic loads across the BSs.
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| 19. |
- Shokri-Ghadikolaei, Hossein, 1987-, et al.
(författare)
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A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks
- 2020
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 6:4, s. 1269-1282
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Forskningsöversikt (refereegranskat)abstract
- Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference. Unfortunately, traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms in terms of latency and protocol overhead, while being sensitive to missing channel state information. In this paper, we propose hybrid model-based and data-driven multi-operator spectrum sharing mechanisms, which incorporate model-based beamforming and user association complemented by data-driven model refinements. Our solution has the same computational complexity as a model-based approach but has the major advantage of having substantially less signaling overhead. We discuss how limited channel state …
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| 20. |
- Tsakmalis, A., et al.
(författare)
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Centralized Power Control in Cognitive Radio Networks Using Modulation and Coding Classification Feedback
- 2016
-
Ingår i: IEEE Transactions on Cognitive Communications and Networking. - 2332-7731. ; 2:3, s. 223-237
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Tidskriftsartikel (refereegranskat)abstract
- 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 protocol. The learning process enabler is a cooperative modulation and coding classification (MCC) technique which estimates the modulation and coding scheme 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 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 CPM and the center of gravity CPM whose effectiveness in the proposed simultaneous PC and interference channel learning algorithm is demonstrated through numerical simulations.
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| 21. |
- Tsinos, C. G., et al.
(författare)
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Hybrid Analog-Digital Transceiver Designs for Multi-User MIMO mmWave Cognitive Radio Systems
- 2019
-
Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; , s. 1-1
-
Tidskriftsartikel (refereegranskat)abstract
- Millimeter wave (mmWave) band mobile communications can be a solution to the continuously increasing traffic demand in modern wireless systems. Even though mmWave bands are scarcely occupied, the design of a prospect transceiver should guarantee the efficient coexistence with the incumbent services in these bands. To that end, in this paper, multi-user underlay cognitive transceiver designs are proposed that enable the mmWave spectrum access while controlling the interference to the incumbent users. MmWave systems usually require large-scale antenna arrays to achieve satisfactory performance and thus, it is difficult to support fully digital transceiver designs due to high demands in hardware complexity and power consumption. Thus, in order to develop efficient solutions, the proposed approaches are based on a hybrid analog-digital (A/D) architecture. Transceiver designs are developed for both the uplink and the downlink regime of a multi-user cellular system. Efficient algorithmic solutions are proposed for the design of the analog and the digital counterparts of the precoding and the decoding matrices of the latter systems based on the Alternating Direction Method of Multipliers (ADMM). Simulations show that the performance of the proposed hybrid A/D approaches is very close to the one of the corresponding fully digital transceivers for typical experimental setups.
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| 22. |
- Voicu, Andra M., et al.
(författare)
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Risk-Informed Interference Assessment for Shared Spectrum Bands : A Wi-Fi/LTE Coexistence Case Study
- 2017
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2332-7731. ; 3:3, s. 505-519
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Tidskriftsartikel (refereegranskat)abstract
- Interference evaluation is crucial when deciding whether and how wireless technologies should operate. In this paper, we demonstrate the benefit of risk-informed interference assessment to aid spectrum regulators in making decisions, and to readily convey engineering insight. We apply, for the first time, risk assessment to an open question of inter-technology spectrum sharing, i.e., a Wi-Fi/LTE coexistence study in the unlicensed band, and we demonstrate that this method comprehensively quantifies the interference impact. We perform simulations with our newly publicly available tool and we consider throughput degradation and fairness as example metrics to assess the risk for different network densities, numbers of channels, and deployments. The risk assessment study shows that no regulatory intervention is needed to ensure harmonious technical Wi-Fi/LTE coexistence: for the typically large number of channels available in the 5 GHz band, the risk for Wi-Fi from LTE is negligible. As an engineering insight, Wi-Fi coexists better with itself in dense deployments, but better with LTE in sparse deployments. Also, both main LTE-in-unlicensed variants coexist well with Wi-Fi in general. For LTE intra-technology inter-operator coexistence, both variants typically coexist well in the 5 GHz band, but for dense deployments, implementing listen-before-talk causes less interference.
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| 23. |
- Ye, Yu, et al.
(författare)
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Mobility-Aware Content Preference Learning in Decentralized Caching Networks
- 2020
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; 6:1, s. 62-73
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Tidskriftsartikel (refereegranskat)abstract
- Due to the drastic increase of mobile traffic, wireless caching is proposed to serve repeated requests for content download. To determine the caching scheme for decentralized caching networks, the content preference learning problem based on mobility prediction is studied. We first formulate preference prediction as a decentralized regularized multi-task learning (DRMTL) problem without considering the mobility of mobile terminals (MTs). The problem is solved by a hybrid Jacobian and Gauss-Seidel proximal multi-block alternating direction method (ADMM) based algorithm, which is proven to conditionally converge to the optimal solution with a rate ${O}$ (1/ ${k}$ ). Then we use the tool of Markov renewal process to predict the moving path and sojourn time for MTs, and integrate the mobility pattern with the DRMTL model by reweighting the training samples and introducing a transfer penalty in the objective. We solve the problem and prove that the developed algorithm has the same convergence property but with different conditions. Through simulation we show the convergence analysis on proposed algorithms. Our real trace driven experiments illustrate that the mobility-aware DRMTL model can provide a more accurate prediction on geography preference than DRMTL model. Besides, the hit ratio achieved by most popular proactive caching (MPC) policy with preference predicted by mobility-aware DRMTL outperforms the MPC with preference from DRMTL and random caching (RC) schemes.
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| 24. |
- Özger, Mustafa, et al.
(författare)
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Energy-Efficient Transmission Range and Duration for Cognitive Radio Sensor Networks
- 2021
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Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment.
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