In this letter, the performance of a secure massive multiple-input multiple-output (MIMO) system adopting low-resolution digital-to-analog converters (DACs) is analyzed over spatially correlated wireless channels. A tight lower bound for the achievable secrecy rate is derived with artificial noise (AN) transmitted in the null space of the user channels. Using the analytical results, the impact of spatial correlation on the secrecy rate is explicitly evaluated in the presence of low-resolution DACs. The analytical observations reveal that using low-resolution DACs can be beneficial to the secrecy performance compared with ideal DACs, when the channels are strongly correlated and optimal power allocation is not employed.
In this letter, the performance of a secure massive multiple-input multiple-output (MIMO) system adopting low-resolution digital-to-analog converters (DACs) is analyzed over spatially correlated wireless channels. A tight lower bound for the achievable secrecy rate is derived with artificial noise (AN) transmitted in the null space of the user channels. Using the analytical results, the impact of spatial correlation on the secrecy rate is explicitly evaluated in the presence of low-resolution DACs. The analytical observations reveal that using low-resolution DACs can be beneficial to the secrecy performance compared with ideal DACs, when the channels are strongly correlated and optimal power allocation is not employed.
In this paper, we investigate secure transmission in a massive multiple-input multiple-output system adopting low-resolution digital-to-analog converters (DACs). Artificial noise (AN) is deliberately transmitted simultaneously with the confidential signals to degrade the eavesdropper's channel quality. By applying the Bussgang theorem, a DAC quantization model is developed which facilitates the analysis of the asymptotic achievable secrecy rate. Interestingly, for a fixed power allocation factor φ, low-resolution DACs typically result in a secrecy rate loss, but in certain cases, they provide superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically, we derive a closed-form SNR threshold which determines whether low-resolution or high-resolution DACs are preferable for improving the secrecy rate. Furthermore, a closed-form expression for the optimal φ is derived. With AN generated in the null-space of the user channel and the optimal φ, low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for random AN with the optimal φ, the secrecy rate is hardly affected by the DAC resolution because the negative impact of the quantization noise can be compensated by reducing the AN power. All the derived analytical results are verified by numerical simulations.
Since the requirement of secure transmission for massive multiple-input–multiple-output (mIMO) is high, we consider a secure massive MIMO system in presence of multiple digital to analog converters (DACs). We deal with complicated performance analysis of the proposed system including a base station, a legitimate user, and an eavesdropper while these nodes are equipped with multiple antennas. In particular, we intend to present the closed-form expression for the ergodic secrecy rate of the system and also calculate the optimal power allocation for better performance gain. The simulation analysis shows that quantization bits, transmit signal to noise ratio at the base station, and power allocation plays a major role in enhancing the performance of the system.
In this paper we study the secrecy performance of a downlink massive multiple-input multiple-output (MIMO) system in the presence of pilot spoofing attack (PSA). Specifically, the base station (BS), which is equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), exploits the low-complexity random artificial noise (AN) to improve secrecy. With the aid of the additive quantization noise model, the ergodic secrecy rate is derived. Based on the derived results, we study the impact of ADCs' and DACs' resolutions on the secrecy performance for the two different scenarios: the scenarios with and without the knowledge of the PSA power. The optimal power allocation parameter, which allocates power between the information-bearing signal and AN, is derived for the scenario with the knowledge of the PSA power. For ADCs, our theoretical results indicate that high-resolution ADCs are always favorable for improving secrecy, regardless of the availability of the PSA power. However, the impact of DACs' resolution is related to the knowledge of the PSA power. For the scenario without the PSA power, enhancing the resolution of DACs does not necessarily improve the secrecy performance due to the inappropriate power allocation. For the scenario with the PSA power, DACs with different resolutions can achieve the same ergodic secrecy rate in most cases, since the optimal power allocation can make a balance between mitigating the rate loss due to coarse quantization and injecting AN for anti-eavesdropping. Only in the case where the PSA power is small, high-resolution DACs achieve a higher ergodic secrecy rate. Finally, we propose a PSA detection method and study the effect of low-resolution ADCs on the detection performance. It is found that the resolution of ADCs has little effect on the performance of PSA detection.
Low-resolution converters reduce the power consumption of wireless devices and help enable large energy-efficient arrays. In this letter, we evaluate the secrecy rate for the extreme case of 1-bit single-user multiple-input multiple-output systems following two signaling approaches. In the first method, the system uses discrete signaling. In the second, the system combines Gaussian signaling with artificial noise alongside power allocation. In the first case, we give a closed-form expression of the secrecy rate and propose an algorithm that provides near-optimal results for low signal-to-noise ratios (SNRs). In the second scenario, depending on the channel state information, we suggest different precoding plans and derive a secrecy rate lower bound. At low SNR, power is allocated to the main signal. Only at high SNR is optimal power allocation effective. Moreover, knowing the eavesdropper channel greatly improves the system’s secrecy.
In this article, the secure communication in cell-free massive multiple-input multiple-output system with low-resolution analog-to-digital converters is investigated in the presence of an active eavesdropper. Specifically, in this article, the deterioration caused by the analog-to-digital converter imperfections on the accuracy of the channel estimation and secure transmission performance is studied. Besides, the additive quantization noise model is utilized to analyze the impacts of the low-resolution analog-to-digital converters. The minimum mean square error channel estimation results show that there is a nonzero floor caused by the coarse analog-to-digital converters. Then, the closed-form expressions for both the legitimate users achievable ergodic rate and the information leakage to the eavesdropper are derived, respectively. Moreover, tight approximated ergodic secrecy rate expression is also presented with respect to analog-to-digital converters quantization bits, number of antennas, pilot power, and so on. To degrade the impacts of the pilot spoofing attack, an active attack detection approach based on random matrix theory is proposed which can only be operated at one access point. Simulation results are provided to corroborate the obtained results and analyze the impacts of various parameters on system secrecy performance. Also, the superiority of the proposed active attacks detection method is confirmed via simulation results.
To reduce the number of radio-frequency chains of base station, the use of finite resolution analog beamforming is desirbale in massive multiple-input multiple-output system. This article investigates the secure downlink massive multiple-input multiple-output data transmission with artificial noise at base station in the presence of a multi-antenna passive eavesdropper. The achievable user’s ergodic information rate and ergodic capacity of the eavesdropper are analyzed in detail, respectively. With maximum ratio transmission or maximum ratio combining, we derive closed-form expressions for a tight lower bound on ergodic secrecy rate and tight upper bound for secrecy outage probability. Based on these analytical expressions, the effects of various system parameters on secrecy performance, such as power allocation factor, number of eavesdropper’s antennas, number of the user terminals, total transmission power, and finite resolution analog beamforming parameters, are investigated in detail. Also, the optimal power allocation scheme between data and artificial noise signals is achieved in closed form to maximize the ergodic secrecy rate. In addition, we derive the conditions that the secure massive multiple-input multiple-output system need to meet to obtain a positive secrecy rate. Finally, numerical simulation results validate the system’s secrecy performance and verify all the theoretical analytical results.