# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
### Related Work  
An early study investigated dynamic channel assignment for cellular systems by using neural networks [26]. One of the most popular unsupervised learning methods, \(k\)-means clustering, was used to jointly optimize both the gateway partitioning and the virtual-channel allocation in hybrid optical/wireless networks [27], to group distributed users in networks [28], and to blindly detect symbols [29]. Decision trees and neural networks were applied to enhance the routing performance [30].  
Reinforcement learning techniques have also been found to be helpful in solving multi-armed bandit problems in wireless communications [31; 32], link adaptation [33], and to autonomously manage heterogeneous networks [34]. Other reinforcement learning methods, such as Markov decision processes and Q-learning, have been applied to energy harvesting sensor networks [35], cognitive radio networks [36], and opportunistic access of macro/femtocell heterogeneous networks [37]. Accelerated reinforcement learning algorithms have been used to predict spectrum opportunities in opportunistic spectrum access networks, resulting in improved secondary sum-rate performance [38].  
Supervised learning methods, such as the \(k\)-nearest neighbors algorithm (\(k\)-NN) and the support vector machine (SVM), have been employed to design link adaptation methods for wireless communication networks [39; 40; 41]. There have been extensive studies on not only improving the accuracy or complexity of SVMs [42], but also applying SVM to various classification fields such as signal detection in visible light communications [43]. The efficiency of \(k\)-NN-based beam allocation [44] was also demonstrated in multiuser massive MIMO systems. The whole communication system was interpreted as a machine learning system, i.e., mapping major components in the communication system including the transmitters, channels, and receivers to different layers in a neural network [45; 46]. In [47], the communication system was considered as an autoencoder and the optimization of the communication system was accomplished by training the corresponding neural network.  
Among the attempts to apply machine learning to the communication and signal processing fields, and specifically to the antenna allocation problem considered here, a recent work has applied supervised machine learning-based methods for antenna allocation in multi-antenna point-to-point (single-user) wireless networks [48]. It was shown that the antenna allocation in wireless communications can be converted to a multiclass classification problem. Compared to the conventional optimization-driven methods in [12; 13; 14], the multiclass classification, learning-based method in [48] provided near-optimal communication performance with relatively low online computational complexity.  
It is worth noting that computational complexity is one of the bottlenecks for traditional optimization-driven antenna selection approaches because of the rapidly increasing complexity of exhaustive search algorithms for the larger number of antennas [13; 20]. To reduce computational complexity, seminal studies have proposed optimization-driven antenna selection methods in single-user communication systems [21; 22]. For multiuser communication systems, there have also been many studies attempting to reduce the complexity of antenna selection [23; 24; 25].