Sensing and Communication Tradeoff for Cognitive Access of Continues-Time Markov Channels

Li, Xin; Zhao, Qianchuan; Guan, Xiaohong; Tong, Lang

doi:10.1109/wcnc.2010.5506649

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Given the limited wireless resources and the shared use of the hardware platform, existing ISAC solutions seek to achieve a favorable performance tradeoff between both functionalities, where spatial multiplexing [2], [3] and time division [4], [5] are two typically employed approaches. Specifically, spatial multiplexing adopts the multiple-input multiple-output (MIMO) technology with sophisticatedly designed waveform and beamforming schemes so as to support dual-functional transmission with various modulation schemes.…”

Section: A Related Work On Integrated Sensing and Communicationmentioning

confidence: 99%

“…This includes 1) Lack of a recognition accuracy model. Conventional designs (e.g., precoder design, [3], beam pattern design [8], time allocation [5]) maximize the SINR performance, which is only applicable to geometry based sensing tasks. As the HMR tasks are deep learning based rather than geometry based, a recognition accuracy model capturing the sensing performance of HMR with respect to the wireless resources, is yet to be explored.…”

Section: B Summary Of Challengesmentioning

confidence: 99%

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Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

Li¹,

Wang²,

Li³

et al. 2021

Preprint

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Integrated sensing and communication (ISAC) is a promising technology to improve the bandutilization efficiency via spectrum sharing or hardware sharing between radar and communication systems. Since a common radio resource budget is shared by both functionalities, there exists a tradeoff between the sensing and communication performance. However, this tradeoff curve is currently unknown in ISAC systems with human motion recognition tasks based on deep learning. To fill this gap, this paper formulates and solves a multi-objective optimization problem which simultaneously maximizes the recognition accuracy and the communication data rate. The key ingredient of this new formulation is a nonlinear recognition accuracy model with respect to the wireless resources, where the model is derived from power function regression of the system performance of the deep spectrogram network. To avoid cost-expensive data collection procedures, a primitive-based autoregressive hybrid (PBAH) channel model is developed, which facilitates efficient training and testing dataset generation for human motion recognition in a virtual environment. Extensive results demonstrate that the proposed wireless recognition accuracy and PBAH channel models match the actual experimental data very well. Moreover, it is found This work was supported by the SUSTech Huawei Cooperation Project under Grant OR1912016.

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Section: A Related Work On Integrated Sensing and Communicationmentioning

confidence: 99%

Section: B Summary Of Challengesmentioning

confidence: 99%

Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

Li¹,

Wang²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…To utilize minimum sensing resources to achieve the maximum utility, it requires to optimally schedule spectrum sensing and data transmission. In several existing work [8][9] [10], spectrum sensing is assumed to be conducted periodically and the objective is to find the optimal sensing duration within one period such that both the interference to the PU and the throughput of CR users are optimized. The tradeoff between sensing time and transmission time is also studied in [11] with the objective to maximize the sensing efficiency.…”

Section: Introductionmentioning

confidence: 99%

Spectrum sensing scheduling in a cost-based framework

Kelkar

Cheng

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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In cognitive radio (CR) networks, spectrum sensing has gained great importance for opportunistic spectrum access.There are many factors that affect the efficiency of spectrum sensing. High sensing accuracy can help reduce the chance of interference to primary user and improve the spectrum utility.However, high sensing accuracy requires a large amount of sensing resources including multiple collaborative CRs and the sensing duration. In this paper, a cost based framework is proposed for spectrum sensing scheduling, in which all these factors are modeled by certain cost or gain of the system. A sequential energy detector is used for accumulating all energy measurements for sensing. Depending on the decision made, the CRs decide whether to wait as the channel is occupied or to start data transmission as there is a spectral hole. The optimal number of CRs, the sensing accuracy levels and the waiting/transmission time are obtained such that the average gain per unit time including both sensing and wait/data transmission stages are maximized. We provide various experimental results to show the effectiveness of the proposed design and the effects of various parameters on the performance are analyzed.Index Terms-Cooperative spectrum sensing, sensing schedul ing, primary user activity pattern, sequential detection.

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“…The common design objective of optimal sensing is to maximize the network throughput by minimizing the sensing overhead under certain performance constraints, e.g., interference to the primary users. To this end, various sensing-related performance tradeoffs have been exploited in many different contexts, such as sensing-throughput tradeoff [3][4][5][6][7][8], overhead-throughput tradeoff [9] and sensing-access tradeoff [10]. In particular, Liang et al in [3] formulated a problem to design the sensing duration to maximize the throughput of the cognitive system under the detection probability constraint.…”

Section: Introductionmentioning

confidence: 99%

Transmitting-Collision Tradeoff in Cognitive Radio Networks: A Flexible Transmitting Approach

Wei

et al. 2011

Proceedings of the 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

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In cognitive radio networks, in order to avoid collision to primary user (PU), cognitive radio (CR) user ought to sense the spectrum periodically to certify that PU is absent. However, the collision to PU also occurs due to imperfect sensing or PU's returning when CR user is transmitting. In this paper, we study a transmitting-collision tradeoff problem in cognitive radio networks. In particular, we investigate a flexible data transmitting scheme so as to minimize the collision to PU under the probability of detection constrains. In addition, by jointly considering both CR user's traffic and PU's state, the probability of collision is derived in detail. Finally, numerical results indicate that the proposed approach can give a superior performance than the fixed frame duration mechanism. The probability of collision for the proposed scheme is lower than conventional mechanism obviously. The conclusion is that the larger traffic parameter of CR user leads to higher collision to PU. By increasing the probability of detection can reduce the collision to PU.

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Sensing and Communication Tradeoff for Cognitive Access of Continues-Time Markov Channels

Cited by 7 publications

References 25 publications

Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

Spectrum sensing scheduling in a cost-based framework

Transmitting-Collision Tradeoff in Cognitive Radio Networks: A Flexible Transmitting Approach

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