اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدRL-Based Interference Mitigation in Uncoordinated Networks with Partially Overlapping Tones
140
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Partially-overlapping tones (POT) are known to help mitigate co-channel interference in uncoordinated multi-carrier networks by introducing intentional frequency offsets (FOs) to the transmitted signals. In this paper, we explore the use of (POT) with reinforcement learning (RL) in dense networks where multiple links access time-frequency resources simultaneously. We propose a novel framework based on Q-learning, to obtain the (FO) for the multi-carrier waveform used for each link. In particular, we consider filtered multi-tone (FMT) systems that utilize Gaussian, root-raised-cosine (RRC), and isotropic orthogonal transform algorithm (IOTA) based prototype filters. Our simulation results show that the proposed scheme enhances the capacity of the links by at least 30% in additive white Gaussian noise (AWGN) channel at high signal-to-noise ratio (SNR), and even more so in the presence of severe multi-path fading. For a wide range of interfering link densities, we demonstrate substantial improvements in the outage probability and multi-user efficiency facilitated by (POT), with the Gaussian filter outperforming the other two filters.
قيم البحث
اقرأ أيضاً
Internet of things (IoT) is one of main paradigms for 5G wireless systems. Due to high connection density, interference from other sources is a key problem in IoT networks. Especially, it is more difficult to find a solution to manage interference in
uncoordinated networks than coordinated system. In this work, we consider 3D topology of uncoordinated IoT network and propose interference mitigation scheme with respect to 3D antenna radiation pattern. In 2D topology network, the radiation pattern of dipole antenna can be assumed as onmi-directional. We show the variance of antenna gain on dipole antenna in 3D topology, consider the simultaneous use of three orthogonal dipole antennas, and compare the system performance depending on different antenna configurations. Our simulation results show that proper altitude of IoT devices can extensively improve the system performance.
In this paper, we study partially overlapping co-existence scenarios in cognitive radio environment. We consider an Orthogonal Frequency Division Multiplexing (OFDM) cognitive system coexisting with a narrow-band (NB) and an OFDM primary system, resp
ectively. We focus on finding the minimum frequency separation between the coexisting systems to meet a certain target BER. Windowing and nulling are used as simple techniques to reduce the OFDM out-of-band radiations, and, hence decrease the separation. The effect of these techniques on the OFDM spectral efficiency and PAPR is also studied.
A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. In this paper, the interference mitigation problem is tackled as a regression problem. Considering the complex
-valued nature of radar signals, the complex-valued convolutional neural network is utilized as an architecture for implementation, which is different from the conventional real-valued counterparts. Meanwhile, as the useful beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared to the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.
We propose a digital interference mitigation scheme to reduce the impact of mode coupling in space division multiplexing self-homodyne coherent detection and experimentally verify its effectiveness in 240-Gbps mode-multiplexed transmission over 3-mode multimode fiber.
In this paper, we propose the joint interference cancellation, fast fading channel estimation, and data symbol detection for a general interference setting where the interfering source and the interfered receiver are unsynchronized and occupy overlap
ping channels of different bandwidths. The interference must be canceled before the channel estimation and data symbol detection of the desired communication are performed. To this end, we have to estimate the Effective Interference Coefficients (EICs) and then the desired fast fading channel coefficients. We construct a two-phase framework where the EICs and desired channel coefficients are estimated using the joint maximum likelihood-maximum a posteriori probability (JML-MAP) criteria in the first phase; and the MAP based data symbol detection is performed in the second phase. Based on this two-phase framework, we also propose an iterative algorithm for interference cancellation, channel estimation and data detection. We analyze the channel estimation error, residual interference, symbol error rate (SER) achieved by the proposed framework. We then discuss how to optimize the pilot density to achieve the maximum throughput. Via numerical studies, we show that our design can effectively mitigate the interference for a wide range of SNR values, our proposed channel estimation and symbol detection design can achieve better performances compared to the existing method. Moreover, we demonstrate the improved performance of the iterative algorithm with respect to the non-iterative counterpart.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
