# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
## 1 Introduction  
Innovative wireless technologies are highly demanded to meet the huge demands of various services and the fast growth of the mobile Internet and Internet of Things (IoT) applications [1; 2; 3; 4; 5]. Non-orthogonal multiple access (NOMA) is an indispensable technology in next-generation wireless systems, achieving manifold capacity gains because of its advantages such as high spectral efficiency, improved cell-edge throughput, low transmission latency, large connection capacity, high achievable data rate and so on [6; 7; 8; 9]. In NOMA, the basic idea is to serve multiple users or devices in the same resource block. Since the signals of multiple users are superimposed in the power domain, the receivers conduct multiuser-detection (MUD) algorithms, such as successive interference cancellation (SIC), to identify individual signals [10; 11; 12]. In NOMA systems, user pairing and resource allocation are important to achieve its potential communication performances [13; 14]. In the present study, we focus on user pairing and assume that thenumber of multiple users in the same resource block simultaneously is two. Hence, precisely, user pairing herein means combining two users to share the same resource block. In NOMA systems, the performance varies depending on user pairings; for instance, signal-to-interference-plus-noise ratio (SINR), throughput, and so on [15; 16]. Thus, the pairing scheme is important and attracts increased attention. There are several studies for pairing schemes, e.g., conventional NOMA (C-NOMA) [17] and uniformed channel gain difference-NOMA (UCGD-NOMA) [18]. The basic concept of C-NOMA and UCGD-NOMA is to pair faraway users with nearby users. In these two methods, users are first divided into two categories based on their distances from the base station: \"near area\" and \"far area\". C-NOMA is the pairing scheme, which pairs the nearest user to the base station in the near area with the farthest user to the base station in the far area, the second-nearest user in the near area with the second-farthest user in the far area. Other users are paired similarly. UCGD-NOMA is the pairing scheme, which pairs the user who is the nearest to the base station in the near area with the user who is the nearest to the base station in the far area, the second-nearest user in the near area with the second-nearest user in the far area, and so on. Likewise, other users are paired. However, these fixed pairing schemes based on distances need exact location information for pairings and may not achieve the optimal user pairing. Consequently, these pairing schemes may find it difficult to achieve optimal and fast user pairing. In [19], user grouping is followed by user pairing. That is, users are fist divided into clusters based on channel gains, then user pairing is performed for each cluster. However, obtaining channel information and the two-stage user pairing process may bring extra communication and decision delay. To fully utilize the potential of NOMA and reduce the communication delay, a highly efficient and fast pairing scheme is demanded. Indeed, machine learning approaches, such as reinforcement learning and deep learning, are examined for NOMA systems [20; 21; 22; 23]. In [20; 22], Q-learning or deep Q-learning has been implemented to NOMA. In [23], user pairing and resource allocation are based on deep learning. In [24], the deep learning method using long short-term memory (LSTM) network has been applied to NOMA systems. However, state information, such as the location of the users, is still necessary to implement these machine learning methods, which may lead to extra communication and decision delays when obtaining state information. Hence, the above-mentioned methods also face difficulties in realizing high-speed and real-time processing in view of future dynamic environments. In the meantime, optics and photonics have been intensively studied for accelerating computing, especially machine learning applications [25; 26; 27; 28].