A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> E. K Nearest Neighbor <s> A powerful machine learning detector based on the k-nearest neighbors (KNN) algorithm is proposed to overcome system impairments. The zero-dispersion link (ZDL), dispersion managed link (DML), and dispersion unmanaged link (DUL) are considered. Meanwhile, an improved algorithm, the distance-weight KNN, is introduced, which outperforms the conventional maximum likelihood-post compensation approach. The numerical results show that KNN is feasible for overcoming various impairments, especially for non-Gaussian symmetric noise, such as laser phase noise and nonlinear phase noise in the ZDL or DML. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> E. K Nearest Neighbor <s> Nonlinear phase noise (known as Gordon–Mollenauer phase noise) results from power fluctuations arising from amplified spontaneous-emission noise of the inline amplifier which is converted into phase fluctuations due to the self-phase modulation effect. It may severely impair the phase of optical signal, and would further degrade the performance of the long-haul optical transmission systems. The case can be compensated partially by mid-span optical phase conjugation(OPC). In this paper, an effective k-nearest neighbor (KNN) detector have been introduced to further improve the performance of OPC system by mitigating non-Gaussian impairments caused by nonlinear phase noise. The effectiveness is investigated in a 112-Gb/s 16QAM optical phase conjugation system with 800-km dispersion-managed link and 1280-km dispersion-shifted-fiber link. The results show that the effective and optimal KNN detector can enhance the overall performance of OPC system, even the asymmetric system. <s> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> E. K Nearest Neighbor <s> We experimentally demonstrate a low-complexity and zero-redundancy fiber nonlinearity mitigation technique in the 16-QAM and 64-QAM coherent optical communication systems based on the non-data-aided (ND) k -nearest neighbors (KNN) algorithm. We measured the bit error rate (BER) performances of the 16-QAM and 64-QAM signals in the back-to-back case and in the single-mode-fiber transmission case and achieved notable BER improvement by the ND-KNN technique. The ND-KNN technique enables us to compensate any nondeterministic transmission impairments and does not require any extra training data, which is powerful to mitigate the fiber nonlinearity impairments in the 16-QAM and 64-QAM coherent optical communication systems. By utilizing the proposed ND-KNN method, we achieved 0.5-dB BER improvement in the 800-km single-mode fiber (SMF) 16-QAM transmission system and approximately 2-dB BER improvement in the 80-km SMF 64-QAM transmission system. The algorithm first utilizes the density parameter of the testing data to extract rapidly the center noiseless data and label them as the classification references and then applies the KNN method to classify the remaining testing data. Therefore, the algorithm is robust to the system noise and can achieve fast convergence. The proposed ND-KNN equalization technique can provide efficient compensation at a low computation cost and zero data redundancy and is promising for real-world application. <s> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> E. K Nearest Neighbor <s> WLAN-based indoor positioning algorithm has the characteristics of simple layout and low price, and it has gradually become a hotspot in both academia and industry. However, due to the poor stability of Wi-Fi signals, received signal strength (RSS) fingerprints of some adjacent reference positions are difficult to evaluate similarity when utilizing traditional distance-based calculation methods. By clustering these RSS fingerprints into one region, the commonly utilized KNN algorithm in the past cannot achieve accurate positioning in the region. For this, we introduce a concept of the insensitive region of the RSS fingerprint and a new algorithm named DBSCAN-KRF. This algorithm can delete noise sample and detect insensitive region, then, different methods are selected to achieve indoor positioning by judging the region of the estimated fingerprint sample, the KNN algorithm is selected when the region is sensitive, and random forest algorithm is selected when the region is insensitive. The experimental results show that the DBSCAN-KRF algorithm is superior while compared with other alternative indoor positioning algorithms. <s> BIB004
Definition: The k Nearest Neighbor (kNN) is a non-parametric ML method (Fig.14.) , which classifies the input based on a majority vote of the k closest training neighbors in the feature space. For example, when = 1, the input is assigned to the nearest neighbor class. The kNN calculates the Euclidean distance from the input point to all of the training set points by where, is the input point, is the training data. Then it sorts all of ( , ) to take k nearest neighbors, the estimated neighbor is classified by a majority vote of its k nearest neighbors BIB004 . In kNN, the function is approximated locally and all computation is deferred until classification. The kNN detection is the same as soft detections such as maximum likelihood based on the probability density function of the received signal BIB002 . Assigning weights to the contributions of the neighbors is one way to improve performance. Fiber OC: The same as SVM, kNN is mostly used as detector in literature, and could deal with effects such as NLPN, ASE noise, SPM. NLPN could be compensated partially by mid-span optical phase conjugation (OPC). kNN detector is effective for the circularly symmetric non-Gaussian impairments, such as ASE noise, I/Q imbalance, and NLPN of dispersion unmanaged link, and could be used to further improving performance of the OPC by mitigating non-Gaussian impairments caused by NLPN BIB002 . However, one of the problems of kNN is the distributed training points might not be uniformly distributed. In this situation the nearest class might have one point, and the farther class might have more points, and finally the input point would be dedicated to the farther class because of more points. As a solution for this problem, Distance Weighted kNN (DW-kNN) detector emerged, which gives a weight related to the distance to each of the surrounding points and then takes a decision; this technique better mitigates fiber impairments, and outperform maximum likelihood post compensation approach BIB001 . However, the kNN and DW-kNN still have some complexity, because they are dependent on the amount of input data and the value of k. As a solution to this problem, the non-data-aided kNN (NDkNN) provides efficient nonlinearity mitigation with low complexity and zero data redundancy BIB003 . The ND-kNN compensates any non-deterministic impairments and does not require any extra training data. It uses the density parameter of the test data to rapidly extract the center noise-less data and label them as the classification references; then applies the kNN to classify the remaining test data. Therefore, it is robust to noise and has fast convergence as well as significant BER improvement in back-to-back and SMF transmission.