Secure Communication in Spectrum-Sharing Massive MIMO Systems With Active Eavesdropping

Timilsina, Santosh; Baduge, Gayan Amarasuriya Aruma; Schaefer, Rafael F.

doi:10.1109/tccn.2018.2833848

Cited by 38 publications

(28 citation statements)

References 40 publications

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“…where P Smax is the maximum SU transmit power and I P is the PIT, which is the maximum interference level that the PBS can endure without hindering its performance. The transmit power constraint in (19) is designed by only considering the same cell secondary CCI. However, under a cooperative secondary system, a more stringent transmit power constraint on the secondary transmissions can be defined by considering the both inter-cell and intra-cell secondary CCI as…”

Section: Secondary Transmit Power Constraintsmentioning

confidence: 99%

“…In [17], the wireless energy harvesting for cognitive massive MIMO systems is studied by deriving the fundamental energy-rate trade-off. In [18], [19], the achievable secrecy rates of single-cell cognitive massive MIMO systems with passive/active eavesdroppers are investigated. In [20], [21], the achievable rates of cognitive MIMO relay systems with underlay spectrum sharing are derived, and thereby, the detrimental effects of inter/intra cell pilot contamination are investigated.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Cell Massive MIMO Uplink With Underlay Spectrum Sharing

Al-Hraishawi

Baduge

Ngo

et al. 2019

IEEE Trans. Cogn. Commun. Netw.

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The achievable rates are investigated for multicell multiuser massive multiple-input multiple-output (MIMO) systems with underlay spectrum sharing. A general pilot sharing scheme and two pilot sequence designs (PSDs) are investigated via fully-shared (PSD-1) and partially-shared (PSD-2) uplink pilots. The number of simultaneously served primary users (PUs) and secondary users (SUs) in the same time-frequency resource block by the PSD-1 is higher than that of PSD-2. The transmit power constraints for the SUs are derived to mitigate the secondary co-channel interference (CCI) inflicted at the primary base-station (PBS) subject to a predefined primary interference temperature (PIT). The optimal transmit power control coefficients for the SUs with max-min fairness, and the common achievable rates are derived. The cumulative detrimental effects of channel estimation errors, CCI and intra-cell/inter-cell pilot contamination are investigated. The secondary transmit power constraint and the achievable rates for the perfect channel state information (CSI) case become independent of the PIT when the number of PBS antennas grows unbounded. Therefore, the primary and secondary systems can be operated independent of each other as both intra-cell and inter-cell interference can be asymptotically mitigated at the massive MIMO PBS and secondary base-station (SBS). Nevertheless, the achievable rates and secondary power constraints for the imperfect CSI case with PSD-1 are severely degraded due to the presence of intra-cell and inter-cell pilot contamination. These performance metrics depend on the PIT even in the asymptotic PBS antenna regime. Hence, the primary and secondary systems can no longer be operated independently for imperfect CSI with PSD-1. However, PSD-2 provides achievable rate gains over PSD-1 despite the requirement of lengthier pilot sequences of the former than that of the latter.

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Section: Secondary Transmit Power Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Cell Massive MIMO Uplink With Underlay Spectrum Sharing

Al-Hraishawi

Baduge

Ngo

et al. 2019

IEEE Trans. Cogn. Commun. Netw.

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“…are the small scale Rayleigh fading and where D m,m = β m , m = {1, ..., K} and ϑ are the path loss from BS-to-user and BS-to-eavesdropper channels, respectively. The path loss is modeled as β m = l −r m and ϑ = d −r , where r is the path loss exponent factor, l m is the distance between the BS and user m, and d is the distance between the BS and eavesdropper [5].…”

Section: System Modelmentioning

confidence: 99%

“…Secure problems in massive MIMO systems have been investigated in References [4][5][6][7][8][9][10][11]. The authors in Reference [4] studied the impact of residual hardware impairment (HWI) on massive MIMO secure communication and showed the HWI has a negative impact on information security.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in Reference [4] studied the impact of residual hardware impairment (HWI) on massive MIMO secure communication and showed the HWI has a negative impact on information security. In Reference [5], S. Timilsina et al studied the effect of active eavesdropping on physical layer security and revealed that active eavesdroppers had a stronger ability to eavesdrop confidential information with higher pilot power. In Reference [6], Z. Chu et al studied a MIMO secrecy channel with SWIPT.…”

Section: Introductionmentioning

confidence: 99%

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Secure Energy-Efficient Resource Allocation Algorithm of Massive MIMO System with SWIPT

Yang

Wang

et al. 2019

Electronics

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In this paper, we consider the resource allocation problem to maximize the minimum (max–min) user’s secure energy efficiency (SEE) in downlink massive multiple-input multiple-output (MIMO) systems with simultaneous wireless information and power transfer (SWIPT). First, transmission power and power splitting ratio are designed to achieve the max–min user’s SEE subject to harvested energy threshold, the constraints of transmission power, and power splitting ratio. Secondly, the optimization problem is non-convex and very difficult to tackle. In order to solve the optimization problem, we converted to a series of parameter optimization subproblems by fractional programming. Then, we employ the first-order Taylor expansion and successive convex approximation (SCA) method to solve parameter optimization problems. Next, a secure energy-efficient resource allocation (SERA) algorithm with the bisection method is proposed to find the max–min SEE of the system. Finally, simulation results show the effectiveness and superiority of the SERA algorithm.

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