# Flexible, Brain-Inspired Communication in Massive Wireless Networks
## 1 Introduction  
In recent years, the number of devices connected to the Internet has rapidly increased. Mobile data traffic has grown 17-fold over the past five years [1]. Mobile networks carried 686 petabytes per month in 2012 [1]. According to Cisco predictions that monthly global mobile data traffic will be 77 exabytes by 2022, and annual traffic will reach almost one zettabyte [1]. Moreover, communication of billions of machines and devices that are expected to comprise the Internet of Things (IoT) pose even greater challenges, never encountered before. This means a high density of devices per square meter, and in consequence, short distances between nodes of the network. Furthermore, some number of these devices, e.g., sensors, will be (are) battery powered, and thus, they have to be ultra-energy-efficient. Nowadays, most low-energy wireless systems adopt digital modulation and coding, and the baseband digital signal processing is often performed in software [2]. Thus, besides the energy needed for signal transmission, energy is also required for signal processing at both a transmitter and a receiver. This processing energy may even dominate at short link distances and most often increases when approaching the Shannon capacity limit because of the implementation complexity [3]. The inspiration for the design of new ultra-energy-efficient networks can be the human brain which can be compared to the ultra-dense network. The neurons of a human brain can be compared to nodes, while axons can be compared to links in a network. It is well-known that the current wireless-node power-consumption in wireless local-area and cellular networks is of the order of 0.1-2 W [4] for just one transmission link. In comparison, the incredibly complex human brainworks with less than \(10^{-9}\) W per node (neuron) with up to 10,000 links. Thus, even considering the shorter distances, the human brain for a massive network of neurons and synapses is many orders of magnitude more energy efficient than any human-made network. We believe that future IoT networks should borrow some mechanisms used in the nervous-system communication, since the number of nodes will be massive, and the distances and power levels will be extremely small. One mechanism in a human brain, making it ultra-energy-efficient, is the fact that neurons operate and communicate continuously in time. Moreover, inside each neuron, information incoming from the synapses, is integrated and processed in an analog manner and the neuron then decides whether to fire an action potential. This discrete, binary event is transmitted along the axon to other neurons. Thus, we pose the question whether in some energy-constricted, dense networks (with short-distance links and moderate distortions), the nervous-system-inspired analog transmission can be an alternative to digital transmission, whether adaptive digital and analog modulation is an energy-efficient link-adaptation option, and if so, in what network scenarios. Bio-inspired systems have been studied intensively during the last 15 years, as can be seen from the review papers, see for example [5; 6; 7; 8]. Nevertheless, these papers describe the general concept of bio-inspired systems or summarize the bio-inspired algorithms, e.g., genetic algorithm or ant-colony optimization. In the context of wireless networks, there are few articles that present specific, brain-inspired solutions. For instance, in [9], the authors describe the topologies of a network which can be found in a human brain and show the trade-off between the complexity of the topology and the consumed power. The brain-inspired dynamic spectrum management for cognitive radio has been proposed in [10]. Papers [11; 12] describe the possibility of applying the artificial intelligence (AI) techniques (like machine learning, genetic algorithms, swarm intelligence, ant colony, artificial neural networks, fuzzy system) in the context of 5G networks. Moreover, in [13] the microglia functionality from the human brain is applied in a dense network.