In this paper, we present a WiFi-based intrusion detection system called Wi-Alarm. Motivated by our observations and analysis that raw channel state information (CSI) of WiFi is sensitive enough to monitor human motion, Wi-Alarm omits data preprocessing. The mean and variance of the amplitudes of raw CSI data are used for feature extraction. Then, a support vector machine (SVM) algorithm is applied to determine detection results. We prototype Wi-Alarm on commercial WiFi devices and evaluate it in a typical indoor scenario. Results show that Wi-Alarm reduces much computational expense without losing accuracy and robustness. Moreover, different influence factors are also discussed in this paper.

High peak-to-average power ratio (PAPR) causes nonlinear impairments in intensity modulation direct detection (IM/DD) optical orthogonal frequency division multiplexing (O-OFDM) systems. Selective mapping (SLM) is a well-known effective PAPR reduction technique, but it suffers from high computational complexity due to the bank of inverse fast Fourier transforms (IFFTs) required to generate the set of candidate signals. In this paper, we propose a recombined SLM scheme that can generate up to 2U2 symbol candidates with U IFFTs. The candidate sequences are first partitioned and then recombined to generate new candidate signals, where the addition operation replaces the IFFT block and reduces the computational complexity significantly. Simulations and a real-time end-to-end IM/DD O-OFDM transmission system with line rate 10.5 Gb/s are set up to verify the performance of the proposed scheme. It is demonstrated that compared with conventional SLM, the proposed scheme achieves similar PAPR reduction performance with considerably lower computational complexity and no bit error rate degradation.

An improved neural network-based equalization method is proposed and experimentally demonstrated. The up-to-137 Gb/s transmission of four level pulse amplitude modulation (PAM-4) signals with 25 G class 850 nm optical devices is achieved over an in-house fabricated 40 cm optical backplane. An in-depth investigation is conducted regarding the impact of delayed taps and spans on equalization performance. A performance comparison of the proposed method with the traditional maximum likelihood sequence estimation (MLSE) and decision feedback equalization (DFE) is also undertaken. For the bit rate from 80 to 100 Gb/s, the proposed method achieves an adopted hard-decision forward error correction (HD-FEC) requirement at a received optical power (RoP) of −9 and −8 dBm, while DFE and MLSE cannot meet the HD-FEC requirement. When the bit rate increases from 120 to 137 Gb/s, the proposed equalization method still successfully maintains the acceptable system performance at an RoP of −4 and −2.5 dBm. Furthermore, the specific bit error rate (BER) performances for varied maximum achievable bit rate under different RoPs by applying MLSE and the proposed method are also analyzed. This provides an important potential solution to realize the future data centers.

An important and typical scenario of radio propagation in a railway or subway tunnel environment is the cascaded straight and curved tunnel. In this paper, we propose a joint path loss model for cascaded tunnels at 3.5 GHz and 5.6 GHz frequency bands. By combining the waveguide mode theory and the method of shooting and bouncing ray (SBR), it is found that the curvature of tunnels introduces an extra loss in the far-field region, which can be modeled as a linear function of the propagation distance of the signal in the curved tunnel. The channel of the cascaded straight and curved tunnel is thus characterized using the extra loss coefficient (ELC). Based on the ray-tracing (RT) method, an empirical formula between ELC and the radius of the curvature is provided for 3.5 GHz and 5.6 GHz, respectively. Finally, the accuracy of the proposed model is verified by measurement and simulation results. It is shown that the proposed model can predict path loss in cascaded tunnels with desirable accuracy and low complexity.

In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme. Moreover, we further experimentally study the Tree depth and the number of Decision Tree, which are the two main factors affecting the bit error rate performance. Experimental research afterwards showed that the effect from the number of Decision Tree as 30 on bit error rate (BER) flattens out under 48 Gb/s for the fiber range from 25 km and 75 km SSMF, and the influence of Tree depth on BER appears to be a gentle point when Tree Depth is 5, which is defined as the optimal depth point for aforementioned fiber range. Compared to the autoencoder based on a Fully-Connected Neural Network, our algorithm uses addition operations instead of multiplication operations, which can reduce computational complexity from 10⁸ to 10⁷ in multiplication and 10⁶ to 10⁸ in addition on the training phase.

Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE). In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs) and the pico Cell Range Expansion (CRE) are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC) technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.