Direction‐based jamming detection and suppression in mmWave massive MIMO networks

Abstract

In this paper, the problem of physical layer security in the uplink of millimeter-wave massive multiple-input multiple-output (MIMO) networks is studied and a jamming detection and suppression method is proposed. The proposed method is based on directional information of the received signals at the base station antenna array. The proposed jamming detection method can accurately detect both the existence and direction of the jammer using the received pilot signals in the training phase. The obtained information is then exploited to develop a channel estimator that excludes the jammer's angular subspace from received training signals. The estimated channel information is then used for designing a combiner at the base station that is able to effectively cancel out the deliberate interference of the jammer. By numerical simulations, the performance of the proposed jamming detection method is evaluated in terms of correct detection probability and false alarm probability, and its effectiveness when the jammer's power is substantially lower than the user's power is shown. Also, the results show that the proposed jamming suppression method can achieve a very close spectral efficiency as the case of no jamming in the network.

Beam-Slicing for Jammer Mitigation in mmWave Massive MU-MIMO

“…Similarly to our work, references [12], [13] propose to exploit spatially correlated channels in order to suppress jammer interference. In particular, the work in [13] applies a beamspace transform to separate the jammer from the UEs in the angular domain. The beamspace transform [14] is related to the concept of beam-slicing used by SNIPS.…”

“…These works have considered different attacks, such as constant jamming attacks [4], [5], in which the jammer is permanently transmitting, as well as other types of attacks in which the jammer transmits only at specific time instances, such as when the UEs transmit [4] or during pilot transmission [6]. Furthermore, due to the complexity of the jammer problem, some works have devoted themselves only to detecting the presence of a jammer [6], [7], while other works have proposed methods to suppress jamming [4], [5], [8]- [13].…”

“…Unfortunately, the use of low-resolution analog-to-digital converters (ADCs) leaves the BS vulnerable to jammers that could be introduced, for example, by a rogue user equipment (UE) or a malicious transmitter. Previous works [4]- [13] have analyzed the impact of different types of jamming attacks on massive MU-MIMO systems and proposed mitigation methods based on digital equalization. However, none of these works have taken into consideration the compounding challenge of low-resolution data conversion: A jammer can either saturate low-resolution ADCs or (if gain-control is used) widen their quantization range to an extent that drowns the signal in quantization noise, and thereby introduce distortions that are difficult to remove with subsequent digital equalization.…”

Jammer Mitigation via Beam-Slicing for Low-Resolution mmWave Massive MU-MIMO

“…These works have considered different attacks, such as constant jamming attacks [6], [7], in which the jammer is permanently transmitting, as well as other types of attacks in which the jammer transmits only at specific time instances, e.g., when the UEs transmit [6], or during pilot transmission [8]. Moreover, given the complexity of the jammer problem, some works have devoted themselves only to detecting the presence of a jammer [8], [9], while other works have proposed methods to suppress the impact of the detected jammer [6], [7], [10]- [15].…”

“…Unfortunately, the use of low-resolution analog-to-digital converters (ADCs) leaves the BS vulnerable to jammers that could be introduced, for example, by a rogue user equipment (UE) or a malicious transmitter. Previous works [6]- [15] have analyzed the impact of different types of jamming attacks on massive MU-MIMO systems and proposed mitigation methods based on digital equalization. However, none of these works take into consideration the compounding challenge of low-resolution data conversion: A jammer can either saturate low-resolution ADCs or (if gain-control is used) widen their quantization range, which inevitably drowns the useful signals in quantization noise.…”