# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 1 Introduction  
Millimeter wave (mmWave) communications are envisaged to be integrated in the upcoming generation of mobile networks, namely eMBB, one of the 5G verticals. This has been seen as the solution to increase the overall capacity of mobile radio cells, enabling the support of multi-Gigabit/s transmissions towards mobile equipments. Due to the large available bandwidth (55-66 GHz), mmWave band is, thus, very attractive for future 5G wireless communication systems, which might provide transmission data rates over 10 Gbps and network latency below 1 ms. However, even if a 2 GHz channel in the 60 GHz band is used to transmit data employing both 4 and 16-Quadrature Amplitude Modulation (QAM) modulations, data rate would still be limited to 4 and 8 Gbps, respectively. Therefore, there is demand for improving both system reliability and data rates. In this context, it is introduced in this work a novel software-define radio (SDR) mmWave testbed, aimed to tackle the 5G communication requirements outlined in [1]. With mmWave testbeds importance in mind, work at 60 GHz can be found in the literature [2; 3; 4]. However, such works were conducted with universal software radio peripheral (USRP) that offer limited bandwidths of around 25 MHz capabilities, which do not fulfill the 5G foreseen multi-Gigabits scenarios. To this extent, this work discusses the implementation of a real-time software-defined radio mmWave testbed that can cope with the next wireless generation needs. A software-defined radio (SDR) is a common term given to a system which employs the majority of physical layer functionalities using digital signal processing algorithms implemented in an embeddedsystem with the aid of a specific software. In this context, typically, analog stages, such as mixing, amplification, filtering, modulators/demodulators, and essential to establish a wireless radio link, are digitally implemented rather than using discrete analog hardware components. Therefore, an ideal SDR is composed of very reduced hardware at the RF front-end, i.e., only an antenna and a very high speed sampler that is capable of capturing and digitizing a wideband radio signals [5]. However, relatively large coverage distances might only be attained by the employment of amplifiers prior to both DAC/ADC stages, as depicted in Figure 1. For example, at the receiver, a Low-Noise Amplifier (LNA) must be considered to reduce the converter quantization noise and thus maximize the Signal-to-Noise Ratio (SNR) at the digital domain (after sampling); only then is the signal converted to its binary form. Next, data are processed on a number of dedicated computational units, inside the embedded system, enabling the implementation of crucial methods for demodulation, synchronization, and decoding, which are required to recover the transmitted information from advanced modulation techniques. Nevertheless, such architecture inherits the precision limitation from the digital domain representation, as well as it adds computation complexity to such digital domain. On the other hand, translating hardware discrete analog components functionalities into an embedded platform brings several advantages for a wireless communication system designer [7]:  
1. **Design Flexibility:** With the recent availability of very fast and accurate DACs/ADCs, traditional hardware circuitry such as filtering and analog modulators/demodulators can now be tuned. 2. **Reliability:** In general, hardware component functionalities vary according to temperature, manufacturing variations, and ageing, which might lead to performance losses for the communication system. On the other hand, if such functionalities are implemented in a SDR platform, system's performance is expected to remain the same over the years. 3. **Upgradability:** Upgrading and improving a wireless system that is composed of only discrete components usually means replacing hardware, whereas using SDR a particular system might be upgraded without any additional cost, which lead to a more cost effective solution. 4.