# Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks
## 1 Introduction  
With the escalating expansion of wireless network infrastructures and exponential growth in traffic rate, a considerable amount of worldwide energy is consumed by information and communication technologies (ICT) of which more than 70% is being used by radio access hardware and radio frequency [1] which, in turn, increases the overall energy consumption of the system and the production of CO\({}_{2}\) emissions that are threat to global warming. This has given rise to the concept of green communications, the latter is a new research trend which aims to develop innovative methods for the reduction of the total power needed to operate future mobile communication systems and to identify appropriate network architectures and radio technologies which facilitate the required power reduction [2]. The development of enabling technologies for green communications is currently a priority research area, in which there are several major international projects being carried out such as the European EARTH project [3], the international Green Touch consortium [4] and the Mobile VCE Green Radio (GR) [5]. Multiple-input multiple-output (MIMO) communication systems can allow higher data rates compared to single-antenna-aided systems, this is because they have greater spectral efficiency [6]. Hence, MIMO systems are expected to be a prominent part of the 5G standard [7, 8]. However designing reliable and energy efficient MIMO detectors is a challenging task, because of the complexity of the implementation of the signal detection at the receiver due to the interfering sub-streams [9]. Sphere decoding (SD) is a popular MIMO detection scheme, thanks to its ability to achieve near-maximum likelihood (ML) performance. On the other hand, SD exhibits a variable complexity depending on the channel condition, which makes it not well suited for real-time hardware applications. Meanwhile, K-Best detectors exhibit a fixed complexity irrespective of channel conditions; therefore, they have received significant interest in recent years [10, 11]. However, the relatively high complexity still required for K-best MIMO detectors imposes great demands on energy consumption and causes drainage of the battery, which may render its use unfeasible in green communication schemes [8]. A number of energy efficient hardware designs of K-best MIMO detector have been proposed [12; 13; 14]. [PERSON] and [PERSON] in [13] employed a relaxed sorting operation technique to achieve a significant reduction in the power consumption of the MIMO detector. In [14], the authors adopted a sort-free approach to path extension in order to minimize energy costs. More recently, an adaptive hardware scheme was proposed in [12], where favorable channel conditions are exploited to reduce the energy consumption of the MIMO detection hardware, which was achieved through the use of reconfigurable MIMO detection architecture controlled by the signal to noise ratio (SNR). Although these schemes proved to reduce power consumption of the MIMO detecting hardware, they may still not be suitable candidates for green communication networks, as they do not take into consideration the transmission power requirements. In fact, the transmission power accounts for more than 50% of the overall power consumed in wireless cellular networks [2], which is expected to rise significantly in 5G wireless networks because of the increase in signal frequencies [7; 8]. In the context of this work, 5G networks refer to communication systems, which operate at frequencies higher than the conventional microwave frequencies, typically more than 6 GHz. At higher frequencies, the path losses and environment attenuation factors become significantly higher than those experienced in sub-6 GHz frequencies. Therefore, higher transmission power, as well as more sophisticated signal processing, are needed for 5G systems. Additionally, at higher carrier frequencies, 5G networks are expected to have significantly higher bandwidths than the currently operating systems such as 3G and 4G.