Jamming Suppression in Massive MIMO Systems

Akhlaghpasand, Hossein; Björnson, Emil; Razavizadeh, S. Mohammad

doi:10.1109/tcsii.2019.2902074

Cited by 47 publications

(43 citation statements)

References 14 publications

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“…We consider a square cell of 250 m × 250 m composed of one BS located at the cell center and K users that are randomly distributed in the cell with a minimum distance of 25 m from the BS. We assume a coherence block of T = 200 samples with a pilot length τ = K. For uncorrelated Rayleigh fading channels, it was shown in the literature [11], [12] that the massive MIMO uplink is robust against jamming attacks with a MMSE-like estimator and a ZF-like detector. Hence, here we compare the performance of the MS approach with a framework including a MMSE estimator and a ZF detector (with label "MMSE-ZF") as a benchmark.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…In [11], a receiver is proposed that exploits channel estimates of both legitimate user and jammer to improve the spectral efficiency (SE). In [12], we propose a framework including a novel MMSE based jamming suppression (MMSE-JS) estimator for channel training and a zero-forcing jamming suppression (ZFJS) detector for uplink combining. Zhao et al suggest two anti-jamming algorithms based on cooperation between the transmitter and receiver with perfect channel state information [13].…”

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confidence: 99%

“…In general, the jammer's strategy depends on its objective and its available information. Accordingly, in [5], [6], [11], [12], [15], the authors suppose that the pilot jamming attack is a combination of several pilot sequences of the pilot set, while in [4], [7]− [10], [14], [16]− [18], the jammer selects a target user's pilot sequence as the jamming pilot.…”

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confidence: 99%

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Jamming-Robust Uplink Transmission for Spatially Correlated Massive MIMO Systems

Akhlaghpasand

Björnson

Razavizadeh

2020

IEEE Trans. Commun.

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In this paper, we consider how the uplink transmission of a spatially correlated massive multiple-input multipleoutput (MIMO) system can be protected from a jamming attack. To suppress the jamming, we propose a novel framework including a new optimal linear estimator in the training phase and a bilinear equalizer in the data phase.The proposed estimator is optimal in the sense of maximizing the spectral efficiency of the legitimate system attacked by a jammer, and its implementation needs the statistical knowledge about the jammer's channel. We derive an efficient algorithm to estimate the jamming information needed for implementation of the proposed framework. Furthermore, we demonstrate that optimized power allocation at the legitimate users can improve the performance of the proposed framework regardless of the jamming power optimization. Our proposed framework can be exploited to combat jamming in scenarios with either ideal or non-ideal hardware at the legitimate users and the jammer. Numerical results reveal that using the proposed framework, the jammer cannot dramatically affect the performance of the legitimate system.

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Section: Numerical Experimentsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Jamming-Robust Uplink Transmission for Spatially Correlated Massive MIMO Systems

Akhlaghpasand

Björnson

Razavizadeh

2020

IEEE Trans. Commun.

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“…Alongside, the physical layer security of massive MIMO systems recently reaps a great deal of research interests 10‐12 . Thanks to the tremendous spatial degree‐of‐freedoms provided by large‐scale antenna arrays, massive MIMO systems are expected to against passive eavesdropping.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, Do et al 11 resorted to estimate the jammer channel by virtue of unused pilot sequences and used it to design a jamming‐resistant receiver. Inspired by this insight, Akhlaghpasand et al 12 extended the work of Reference 11 to multiple legitimate users and updated the jammer channel estimation scheme in a more efficient manner. Typically, those above contributions were intended to mitigate the jamming attack from the perspective of estimator and receiver design.…”

Section: Introductionmentioning

confidence: 99%

Sum‐rate maximization in uplink cell‐free massive multiinput multioutput system with jamming

Zhang

Zhou

Cao

et al. 2020

Trans Emerging Tel Tech

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This article takes the first look at jamming attacks on both uplink training and data transmission phases in uplink cell‐free massive multi‐input multi‐output systems. Closed‐form rate expressions for that system are derived, which provide us with the opportunities to analyze the achievable rate in the presence of jamming. To compensate for the sum‐rate degradation caused by jamming, we propose an iterative sum‐rate maximization power control algorithm without redesigning the estimator and receiver. Specifically, by virtue of the Lagrange multiplier methodology, the proposed algorithm can determine the closed‐form solution to each iteration and thus, benefiting from less runtime. Simulation results reveal the effectiveness of the proposed algorithm.

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Reducing the jamming effects in multicellular Massive MIMO systems

Gholami‐Dadkan

Abbasi‐Moghadam

Kalantarinejad

2022

Int J Communication

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Summary As Massive multiple‐input multiple‐output (Ma‐MIMO) is one of the critical 5G technologies, security aspects of this technology in terms of powerful distributive jamming attacks for improving the total system performance are so important. In this paper, we evaluate the sum spectral efficiency (SE) of Ma‐MIMO systems in the multicell scenario concerning jamming effects. First, the closed‐form expression for sum SE using two detectors maximum ratio combining (MRC) and zero‐forcing (ZF) in the presence of a jammer was obtained. Next, the effects of jamming power, the number of base station antennas, the signal‐to‐noise ratio (SNR), and coherence block lengths on the system performance were obtained. Finally, to improve the signal‐to‐noise and interference ratio (SINR) of cell edge users, the problem of optimizing the fairness trade‐off between the central legitimate users and the cell edge legitimate users is modeled by using log barrier functions. We propose an alternating algorithm to solve the non‐convex problem. Numerical results showed that by considering the same interference power and the power of the legitimate users, the sum SE improved with proposed schemes and also by increasing the number of antennas at BS. Also, the ZF outperforms MRC with increasing the number of antennas. Finally, it is shown that by using the optimal legitimate users instead of overall legitimate users, the system performance in terms of fairness sum SE trade‐off improved.

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Jamming Suppression in Massive MIMO Systems

Cited by 47 publications

References 14 publications

Jamming-Robust Uplink Transmission for Spatially Correlated Massive MIMO Systems

Jamming-Robust Uplink Transmission for Spatially Correlated Massive MIMO Systems

Sum‐rate maximization in uplink cell‐free massive multiinput multioutput system with jamming

Reducing the jamming effects in multicellular Massive MIMO systems

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