CPC-based backward-compatible network access for LTE cognitive radio cellular networks

Liu, Ling; Zhou, Yiqing; Tian, Lin; Shi, Jinglin

doi:10.1109/mcom.2015.7158271

Cited by 35 publications

(24 citation statements)

References 8 publications

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“…In the second time slot, the j th ST forwards the previously received signal from the i th PT towards the i th PR, while also serving its own SR, by utilizing a multiuser MIMO technique. The received signal at the i th PR can be expressed as y (2) i, j = P p,s A (2) i, j u (2) i + P s,s A (2) i, j u (2) j + n (2) i ,…”

Section: System Modelmentioning

confidence: 99%

“…where the matrix A (2) i, j of size N p × M s denotes the channel between the j th ST (relay) and the i th PR, with entries following the i.i.d. complex Gaussian distribution CN (0, η (2) i, j ).…”

Section: System Modelmentioning

confidence: 99%

“…An FD transceiver can attain higher data rates, compared to its half-duplex (HD) counterpart, owing to its concurrent transmission and reception capabilities [1]. Another popular paradigm to improve spectral efficiency is the cognitive radio network, where an unlicensed secondary user (SU) shares the spectrum allocated to a licensed primary user (PU) [2]. The cooperation between PUs and SUs can provide an additional spectral efficiency gain.…”

Section: Introductionmentioning

confidence: 99%

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Full-Duplex Amplify-and-Forward Relay Selection in Cooperative Cognitive Radio Networks

Ali

Mirza

Zhang

et al. 2019

IEEE Trans. Veh. Technol.

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This paper investigates relay selection policies in a full-duplex (FD) based underlay cooperative cognitive radio network. We consider a scenario consisting of multiple pairs of primary users (PUs) with access to the spectrum, and multiple pairs of secondary users (SUs) with no dedicated spectrum access. Each pair (PU or SU) comprises of a unique set of transmitter and receiver. The secondary transmitter (ST) supports the FD transmission with the simultaneous transmission of the information to its own receiver and reception of the designated PU signal for relaying. Firstly, we formulate an optimization problem to maximize the PU rate by optimizing the transmission power at the relay (i.e., ST), while keeping the SU rate fixed. Secondly, we propose two low-complexity relay selection schemes based on the sum rate maximization, namely, centralized successive and distributed prioritized matching schemes. Numerical results show that the proposed schemes perform close to exhaustive selection schemes.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Full-Duplex Amplify-and-Forward Relay Selection in Cooperative Cognitive Radio Networks

Ali

Mirza

Zhang

et al. 2019

IEEE Trans. Veh. Technol.

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“…This is because the number of TV channels is limited (approximately 50∼70, each with a bandwidth 6 − 8MHz, within a total spectrum range between 54 − 862MHz [22]). In this spectrum range, spectrum sensing time is indeed acceptable, whereas most IEEE 802.22 equipments are for indoor installation and, hence, their power consumption is not an actual problem [23]. Nonetheless, more sophisticated CR services, such as the IEEE 1900.4 standard [24], are designed to use spectrum resources from multiple radio-access-technology (RAT) heterogeneous primary networking systems, e.g., cellular systems.…”

Section: A Protocol Descriptionmentioning

confidence: 99%

Simultaneous Spectrum Sensing and Data Reception for Cognitive Spatial Multiplexing Distributed Systems

Miridakis

Tsiftsis

Alexandropoulos

et al. 2017

IEEE Trans. Wireless Commun.

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A multi-user cognitive (secondary) radio system is considered, where the spatial multiplexing mode of operation is implemented amongst the nodes, under the presence of multiple primary transmissions.The secondary receiver carries out minimum mean-squared error (MMSE) detection to effectively decode the secondary data streams, while it performs spectrum sensing at the remaining signal to capture the presence of primary activity or not. New analytical closed-form expressions regarding some important system measures are obtained, namely, the outage and detection probabilities; the transmission power of the secondary nodes; the probability of unexpected interference at the primary nodes; and the detection efficiency with the aid of the area under the receive operating characteristics curve. The realistic scenarios of channel fading time variation and channel estimation errors are encountered for the derived results.Finally, the enclosed numerical results verify the accuracy of the proposed framework, while some useful engineering insights are also revealed, such as the key role of the detection accuracy to the N. I. Miridakis is with the ). 2 overall performance and the impact of transmission power from the secondary nodes to the primary system. Index TermsCognitive radio, detection probability, imperfect channel estimation, minimum mean-squared error (MMSE), outage probability, spatial multiplexing, spectrum sensing. I. INTRODUCTIONCognitive radio (CR) has emerged as one of the most promising technologies to resolve the issue of spectrum scarcity, caused by the escalating growth in wireless data traffic of nextgeneration networks [1]. One of the principal requirements of CR is the effectiveness of spectrum sharing performed by secondary (unlicensed) nodes, which is expected to intelligently mitigate any harmful interference caused to the primary (licensed) network nodes. This requirement is directly related to the accuracy of spectrum sensing techniques, reflecting the reliable detection of primary transmission(s).On the other hand, placing multiple antennas on each cognitive node represents a fruitful option since the system capacity in terms of data rate can be greatly enhanced. Spatial multiplexing represents one of the most prominent techniques used for multiple input-multiple output (MIMO) transmission systems [2]. For computational savings at the receiver side, there has been a prime interest in the class of linear detectors, such as zero-forcing (ZF) and minimum mean-squared error (MMSE). It is widely known that MMSE outperforms ZF, especially in low-to-medium signal-to-noise (SNR) regions, at the cost of a slightly higher computational burden, since the noise variance is required in this case. In addition, when MIMO technology is combined with distributed antenna systems (DAS), the so-called distributed-MIMO (D-MIMO) transmission is emerged. The success behind D-MIMO relies on the multiplexing gains, which are produced by the classical MIMO transmission, and the diversity gains, which are manifested from the...

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“…On the other hand, investigations show that large portions of spectrum are highly underutilized due to inefficient conventional regulatory policies [1]. Cognitive Radio (CR) is deemed as a highly promising technology to improve the spectrum usage efficiency and has gained more and 5 more attentions in recent years [2,3,4]. CR technology has been proposed as a solution to the underutilization problem by allowing Secondary Users (SUs) to sense radio spectrum environment and opportunistically access licensed frequency, as long as the interference to the Primary Users (PUs) can be kept under their tolerances, such as interference temperature.…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of spectrum and energy efficiency in cognitive radio networks

Wang

2015

Digital Communications and Networks

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In this paper, we discuss the joint improvement of the energy efficiency (EE) and the spectrum efficiency (SE) in OFDM-based cognitive radio (CR) networks. A multi-objective resource allocation task is formulated to optimize the EE and the SE of the CR system simultaneously with the consideration of the mutual interference and the spectrum sensing errors. We first exploit the EE-SE relations and demonstrate that the EE is a quasiconcave function of the SE, based on which the Pareto optimal set of the multi-objective optimization problem is characterized. To find a unique globally optimal solution, we propose a unified EE-SE tradeoff metric to transform the multi-objective optimization problem into a single-objective one which has a D.C. (difference of two convex functions/sets) structure and yields a standard convex optimization problem. We derive a fast method to speed up the time-consuming computation by exploiting the structure of the convex problem. Simulation results validate the effectiveness and efficiency of the proposed algorithms, which can produce the unique globally optimal solution of the original multi-objective optimization problem.

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CPC-based backward-compatible network access for LTE cognitive radio cellular networks

Cited by 35 publications

References 8 publications

Full-Duplex Amplify-and-Forward Relay Selection in Cooperative Cognitive Radio Networks

Full-Duplex Amplify-and-Forward Relay Selection in Cooperative Cognitive Radio Networks

Simultaneous Spectrum Sensing and Data Reception for Cognitive Spatial Multiplexing Distributed Systems

Joint optimization of spectrum and energy efficiency in cognitive radio networks

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