Deep Neural Network Assisted Approach for Antenna Selection in Untrusted Relay Networks

Yao, Rugui; Zhang, Yuxin; Wang, Shengyao; Qi, Nan; Miridakis, Nikolaos I.; Tsiftsis, Theodoros A.

doi:10.1109/lwc.2019.2933392

Cited by 19 publications

(11 citation statements)

References 16 publications

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“…A training data set size of M = 10 5 is considered and the SOP is plotted by averaging the results from M = 10 6 batches of test data. The parameters for the traditional ML (SVM, k-NN, and NB) and DNN schemes are the same as those in [9] and [13], respectively. For the LSTM parameters, activation functions for all gates are taken to be sigmoid, whereas the hyperbolic tangent is used for the memory cell in accordance with the standard LSTM architecture [18].…”

Section: Resultsmentioning

confidence: 99%

“…DL can handle nonlinear problems and its performance improves with the size of the data, which may not be the case for traditional ML models [12]. The authors in [13] implemented a deep neural network (DNN) scheme for TAS in an untrusted relay network. The authors showed that the DNN performs better than traditional ML schemes and achieves almost the same secrecy rate as the conventional scheme.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recurrent Neural Network Assisted Transmitter Selection for Secrecy in Cognitive Radio Network

Tripathi

Kundu

Dobre

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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In this paper, we apply the long short-term memory (LSTM), an advanced recurrent neural network based machine learning (ML) technique, to the problem of transmitter selection (TS) for secrecy in an underlay small-cell cognitive radio network with unreliable backhaul connections. The cognitive communication scenario under consideration has a secondary small-cell network that shares the same spectrum of the primary network with an agreement to always maintain a desired outage probability constraint in the primary network. Due to the interference from the secondary transmitter common to all primary transmissions, the secrecy rates for the different transmitters are correlated. LSTM exploits this correlation and matches the performance of the conventional technique when the number of transmitters is small. As the number grows, the performance degrades in the same manner as other ML techniques such as support vector machine, k-nearest neighbors, naive Bayes, and deep neural network. However, LSTM still significantly outperforms these techniques in misclassification ratio and secrecy outage probability. It also reduces the feedback overhead against conventional TS.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network Assisted Transmitter Selection for Secrecy in Cognitive Radio Network

Tripathi

Kundu

Dobre

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

View full text Add to dashboard Cite

show abstract

“…Content may change prior to final publication. [175] [163] [167], [176] [177] [178] to help cities plan roads based on the data obtained from road-installed sensors and the data reported from sensor nodes installed in vehicles. Armed with such data, city transport agencies can plan better, and vehicle owners can also reserve parking spots in advance.…”

Section: B Smart Transportmentioning

confidence: 99%

Relaying in the Internet of Things (IoT): A Survey

Uyoata¹,

Mwangama

Adeogun

2021

IEEE Access

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The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions.

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“…For each channel sample, the tag generation is an exhaustive search result of the global optimization design for the joint optimization of relay selection and cooperative beamforming in [28]. Specifically, the label is the index to which relay can achieve the maximum realizable rate under the transmitted power constraints in (6). This process goes on to generate the whole training data set.…”

Section: ) Training Set Preparationmentioning

confidence: 99%

“…Data-driven based joint beamforming, and antenna selection scheme is studied in [5] in which a favorable tradeoff of system performance and computation complexity has been observed. For relay systems, the Transmission Antenna Selection (TAS) scheme is investigated based on data-driven algorithms for entrusted relay networks [6], [7]. Some studies have applied Q-learning to solve the relay selection problem.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven-Based Relay Selection and Cooperative Beamforming for Non-Regenerative Multi-Antenna Relay Networks

Luo

Zhao²,

Zhao

et al. 2021

IEEE Access

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In this paper, an attempt to exploit the benefits of data-driven methods in solving joint relay selection and beamforming for non-regenerative relay networks has been made. The common relay selection and beamforming optimization problem aiming to maximize the receiver's achievable rate under the constraint of relay transmit power is intrinsically hard since the mixed discrete and continuous variables. The direct map from channel state information to select a relay with optimized beamforming weights via data-driven methods often fails to yield good results. To overcome this difficulty, we propose a two-stage algorithm based on data-driven method. Firstly, we convert relay selection to a multi-class classification problem, and a Support Vector Machine(SVM) based data-driven scheme is suggested to determine the best relay. After the relay is selected, we utilize a closed-form solution to obtain the corresponding relay beamforming weights. Since the number of relays is often more than two, the sample imbalance problem exists in the classification problem, considered in our proposed data-driven scheme. The core idea of SVM based classification method is equivalent to training the optimal parameters of the SVM classifier through a large number of offline sample data. In this way, the computation of relay selection can be transferred to offline SVM training. Simulation results demonstrate that the performance of the proposed method is close to that of the global optimal relay selection scheme. Moreover, our proposed scheme has much lower complexities than the international optimal relay selection scheme, especially when the number of relays is large.

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Deep Neural Network Assisted Approach for Antenna Selection in Untrusted Relay Networks

Cited by 19 publications

References 16 publications

Recurrent Neural Network Assisted Transmitter Selection for Secrecy in Cognitive Radio Network

Recurrent Neural Network Assisted Transmitter Selection for Secrecy in Cognitive Radio Network

Relaying in the Internet of Things (IoT): A Survey

Data-Driven-Based Relay Selection and Cooperative Beamforming for Non-Regenerative Multi-Antenna Relay Networks

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