On cognitive radio systems with directional antennas and imperfect spectrum sensing

Yazdani, Hassan; Vosoughi, Azadeh

doi:10.1109/icassp.2017.7952825

Cited by 21 publications

(19 citation statements)

References 13 publications

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“…Subsequently, it needs to dynamically control its transmit power during its operation in the network (as self-optimization). To address these two issues, we consider a macrocell network overlaid with small cells and focus on autonomous distributed power control, which is a key element of self-organization since it improves network throughput [10]- [14] and minimizes energy usage [15]- [17]. We rely on local measurements, such as signal-to-interference-plus-noise ratio (SINR), and the use of machine learning to develop a SON framework that can continually improve the above performance metrics.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks

Amiri

Almasi

Andrews

et al. 2019

IEEE Trans. Wireless Commun.

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Self-organizing networks (SONs) can help manage the severe interference in dense heterogeneous networks (HetNets). Given their need to automatically configure power and other settings, machine learning is a promising tool for data-driven decision making in SONs. In this paper, a HetNet is modeled as a dense two-tier network with conventional macrocells overlaid with denser small cells (e.g. femto or pico cells). First, a distributed framework based on multi-agent Markov decision process is proposed that models the power optimization problem in the network. Second, we present a systematic approach for designing a reward function based on the optimization problem. Third, we introduce Q-learning based distributed power allocation algorithm (Q-DPA) as a self-organizing mechanism that enables ongoing transmit power adaptation as new small cells are added to the network. Further, the sample complexity of the Q-DPA algorithm to achieve -optimality with high probability is provided. We demonstrate, at density of several thousands femtocells per km 2 , the required quality of service of a macrocell user can be maintained via the proper selection of independent or cooperative learning and appropriate Markov state models.

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Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks

Amiri

Almasi

Andrews

et al. 2019

IEEE Trans. Wireless Commun.

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“…Utilizing ESPAR antennas for opportunistic spectrum sharing systems is a highly promising solution to enhance the performance of secondary links, while satisfying the constraints set by the primary links [28]. • This work is different from our preliminary works in [33]- [35], where we have considered a simpler constrained optimization problem (with different optimization variables, including continuous transmit power and direction of antenna steering of SU tx ), assuming that SU tx knows the direction (angle) corresponding to PU's activities. It is also different from our work in [1], where we have assumed that SU tx knows the location of SU rx and PU, and, perfect CSI of SU tx -SU rx link is available and used for transmit power adaptation.…”

Section: F Our Contributions and Paper Organizationmentioning

confidence: 93%

“…Starting with the continuous valued ν * and its corresponding continuous valued transmit power P (ν * ), we can write the expressions for the instantaneous capacity C 0,0 and C 1,0 in (4) as [33]…”

Section: A Formalizing (P1) With Modified Objective Function and Conmentioning

confidence: 99%

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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“…In those works, spectrum sensing seeks spectrum holes in the time domain so that SUs exploit them for transmitting their data. Different from the bulk of the literature, in this paper we assume the SUs are equipped with steerable directional antennas which allow them to use spatial spectrum holes [8]- [11] to increase spectrum utilization, specially in cognitive satellite networks [12]. The directional antennas can identify and enable transmission and reception across spatial domain and further enhance spectrum utilization, compared with omni-directional antennas.…”

Section: Introductionmentioning

confidence: 99%

On Optimal Sensing and Capacity Trade-off in Cognitive Radio Systems with Directional Antennas

Yazdani

Vosoughi

2018

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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We consider a cognitive radio system, in which the secondary users (SUs) and primary users (PUs) coexist. The SUs are equipped with steerable directional antennas. In our system, the secondary transmitter (SUtx) first senses the spectrum (with errors) for a duration of τ , and, then transmits data to the secondary receiver (SUrx) if spectrum is sensed idle. The sensing time as well as the orientation of SUtx's antenna affect the accuracy of spectrum sensing and yield a trade-off between spectrum sensing and capacity of the secondary network. We formulate the ergodic capacity of secondary network which uses energy detection for spectrum sensing. We obtain optimal SUtx transmit power, the optimal sensing time τ and the optimal directions of SUtx transmit antenna and SUrx receive antenna by maximizing the ergodic capacity, subject to peak transmit power and outage interference probability constraints. Our simulation results show the effectiveness of these optimizations to increase the ergodic capacity of the secondary network.

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On cognitive radio systems with directional antennas and imperfect spectrum sensing

Cited by 21 publications

References 13 publications

Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks

Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks

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

On Optimal Sensing and Capacity Trade-off in Cognitive Radio Systems with Directional Antennas

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