On the Spectrum Sensing, Beam Selection and Power Allocation in Cognitive Radio Networks Using Reconfigurable Antennas

Yazdani, Hassan; Vosoughi, Azadeh

doi:10.1109/ciss.2019.8692896

Cited by 12 publications

(17 citation statements)

References 25 publications

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“…Channel Training Data Transmission Prior probabilities of H 0 and H 1 π 0 , π 1 Probabilities of channel being sensed idle or busy T f Duration of frame employed by SUtx Tsen Duration of channel sensing and monitoring phase…”

Section: Channel Sensing and Monitoringmentioning

confidence: 99%

“…Algorithm 1: Our proposed iterative algorithm for solving (P2) 1: Initialize Tsen ∈ 0, Tf − Ttrain , µ1, λ, ϑ. 2: Set P0 = 0.…”

Section: Outage and Symbol Error Probabilitiesmentioning

confidence: 99%

“…While underlay systems do not require spectrum sensing to detect PU's activities, they demand coordination between PUs and SUs (to obtain channel state information (CSI) of PU links at SUs) that is not always feasible. On the other hand, interweave or opportunistic CR systems utilize spectrum sensing to enable SUs to use a Parts of this research were presented at 53nd Annual Conference on Information Sciences and Systems (CISS), 2019 [1]. This research is supported by the NSF under grant ECCS-1443942.…”

Section: Introduction a Overview And Backgroundmentioning

confidence: 99%

“…The ESPAR antenna structure and its beampatterns, (a) The ESPAR antenna structure, (b) Beampatterns of an ESPAR with 8 parasitic elements, assuming the Gaussian radiation pattern in(1).…”

mentioning

confidence: 99%

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Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems With Limited Feedback Using ESPAR Antennas

Yazdani

Vosoughi

Gong

2020

IEEE Trans. Cogn. Commun. Netw.

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We consider an opportunistic cognitive radio (CR) system consisting of a primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx), where SUtx is equipped with an electrically steerable parasitic array radiator (ESPAR) antenna with beam steering capability for sensing and communication, and there is a limited feedback channel from SUrx to SUtx. Taking a holistic approach, we develop a framework for integrated sector-based spectrum sensing and sector-based data communication. Upon sensing the channel busy, SUtx determines the beam corresponding to PU's orientation. Upon sensing the channel idle, SUtx transmits data to SUrx, using the selected beam corresponding to the strongest channel between SUtx and SUrx. We formulate a constrained optimization problem, where SUtx-SUrx link ergodic capacity is maximized, subject to average transmit power and interference constraints, and the optimization variables are sensing duration, thresholds of channel quantizer at SUrx, and transmit power levels at SUtx. Since this problem is non-convex we develop a suboptimal computationally efficient iterative algorithm to find the solution. Our numerical results quantify the capacity improvement provided by the ESPAR antenna and demonstrate that our CR system yields lower outage and symbol error probabilities, compared with a CR system that its SUtx has an omni-directional antenna.Index Terms-Beam selection, cognitive radio, constrained ergodic capacity maximization, discrete power allocation, ESPAR antenna, imperfect channel sensing, error-free bandwidth limited feedback channel, reconfigurable antennas.

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Section: Channel Sensing and Monitoringmentioning

confidence: 99%

“…Algorithm 1: Our proposed iterative algorithm for solving (P2) 1: Initialize Tsen ∈ 0, Tf − Ttrain , µ1, λ, ϑ. 2: Set P0 = 0.…”

Section: Outage and Symbol Error Probabilitiesmentioning

confidence: 99%

Section: Introduction a Overview And Backgroundmentioning

confidence: 99%

“…The ESPAR antenna structure and its beampatterns, (a) The ESPAR antenna structure, (b) Beampatterns of an ESPAR with 8 parasitic elements, assuming the Gaussian radiation pattern in(1).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems With Limited Feedback Using ESPAR Antennas

Yazdani

Vosoughi

Gong

2020

IEEE Trans. Cogn. Commun. Netw.

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“…Let assume that phase-shifter resolution B is chosen that the Hermitian matrixS defined in (24), and accordingly the normalizedS, is positive definite and its inverse exists as well. Here, to derive the upper bound, we start by finding an upper bound for (S −1 ) , as following.…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

mmWave Lens-Based MIMO System for Suppressing Small-Scale Fading and Shadowing

Almasi

Amiri

Jafarkhani

et al. 2020

IEEE Trans. Wireless Commun.

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In this paper, we propose a generalized millimeter-Wave (mmWave) reconfigurable antenna multiple-input multipleoutput (RA-MIMO) architecture that takes advantage of lens antennas. The considered antennas can generate multiple independent beams simultaneously using a single RF chain. This property, together with RA-MIMO, is used to combat smallscale fading and shadowing in mmWave bands. To this end, first, we derive a channel matrix for RA-MIMO. Then, we use rate-one space-time block codes (STBCs), together with phaseshifters at the receive reconfigurable antennas, to suppress the effect of small-scale fading. We consider two kinds of phase shifters: i) ideal which is error-free and ii) digital which adds quantization error. The goal of phase-shifters is to convert a complex-valued channel matrix into real-valued. Hence, it is possible to use rate-one STBCs for any dimension of RA-MIMO. We investigate diversity gain and derive an upper bound for symbol error rate in cases of ideal and digital phase-shifters. We show that RA-MIMO achieves the full-diversity gain with ideal phase-shifters and the full-diversity gain for digital phaseshifters when the number of quantization bits is higher than one. We investigate RA-MIMO in the presence of shadowing. Our analysis demonstrates that, by increasing the dimension of RA-MIMO, the outage probability decreases which means the effect of shadowing decreases. Numerical results verify our theoretical derivations.

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Learning-Based Resource Optimization in Ultra Reliable Low Latency HetNets

Yousefvand

Hamidouche

Mandayam

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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In this paper, the problems of user offloading and resource optimization are jointly addressed to support ultrareliable and low latency communications (URLLC) in HetNets. In particular, a multi-tier network with a single macro base station (MBS) and multiple overlaid small cell base stations (SBSs) is considered that includes users with different latency and reliability constraints. Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS. In the considered scheme, SBSs bid to serve URLLC users under their coverage at a given price, and the MBS decides whether to serve each user locally or to offload it to one of the overlaid SBSs. Since the JUR optimization is NP-hard, we propose a low complexity learningbased heuristic method (LHM) which includes a support vector machine-based user association model and a convex resource optimization (CRO) algorithm. To further reduce the delay, we propose an alternating direction method of multipliers (ADMM)based solution to the CRO problem. Simulation results show that using LHM, the MBS significantly decreases the spectrum access delay for users (by ∼ 93%) as compared to JUR, while also reducing its bandwidth and power costs in serving users (by ∼ 33%) as compared to directly serving users without offloading.

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On the Spectrum Sensing, Beam Selection and Power Allocation in Cognitive Radio Networks Using Reconfigurable Antennas

Cited by 12 publications

References 25 publications

Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems With Limited Feedback Using ESPAR Antennas

Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems With Limited Feedback Using ESPAR Antennas

mmWave Lens-Based MIMO System for Suppressing Small-Scale Fading and Shadowing

Learning-Based Resource Optimization in Ultra Reliable Low Latency HetNets

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