Introduction

The role of software in mobile communication systems has increased over time. For the upcoming fifth generation (5G) mobile networks, the concept of software-defined networking (SDN) can ease network management by enabling anything as a service. Software-defined radio (SDR) enables radio virtualization, where several radio components are implemented in software. Cognitive radio (CR) goes one step further by using a software-based decision cycle to self-adapt the SDR parameters and consequently optimize the use of communication resources. This proposal leads to the possibility of having real-time communication functionalities at virtual machines in cloud computing data centers, instead of deploying specialized hardware. Network functions virtualization (NFV) claims to provide cloud-based virtualization of network functionalities. The perspective is that all these software-based concepts should converge while 5G networks are being designed. A new breakthrough will be achieved when all these software paradigms are applied to the physical layer (PHY), where its functionalities are defined and controlled by software as well.

A flexible PHY design is particularly beneficial considering the diverse applications proposed for 5G [1]. In fact, these applications typically have conflicting design objectives and face extreme requirements. Broadband communication will play an important role in, for instance, offering video streaming services with high resolution for TV and supporting high-density multimedia such as 4K and 3D videos in smartphones. Data rates up to 10 gigabits per second (Gbps) are therefore being targeted in 5G. The Tactile Internet [2] enables one to control virtual or real objects via wireless links with haptic feedback. This implies that the end-to-end latency constraint in 5G must be dropped by at least one order of magnitude compared with current fourth generation (4G) technologies. The Internet of Things (IoT) is aimed at connecting a massive amount of devices. Wireless sensor networks need to provide in-service monitoring at low cost and with a long battery life. Smart vehicles improve safety and actively avoid accidents by exchanging their driving status, such as position, breaking, acceleration, and speed, with surrounding vehicles and infrastructure via challenging doubly dispersive channels. Overall, this demands asynchronous multiple access, ultra-low latency, and ultra-high reliability.

Currently, the information and communications technology (ICT) industry sector accounts for about 2–6% of the energy consumption worldwide, and a significant portion of this is contributed by the wireless and mobile communications industry. With the proliferation of wireless data applications, wireless technology continues to increase worldwide at an unprecedented growth rate. This has resulted in an increased number of installed base stations and higher demand on power grids and device power usage, causing an increased carbon footprint worldwide. Current wireless industry therefore needs to embrace eco-friendly green communication technologies at different levels – from components, circuits, and devices to protocols, systems, and networks. Since the rate of improvement in power efficiency of hardware devices lags data traffic growth in both the radio access and core networks, network scaling will be increasingly tied to energy consumption in future wireless protocols, systems, and networks. Hence, it is crucial to develop green technologies for wireless systems and networks to improve energy efficiency and reduce CO2 emissions. Again, from the perspective of network operators, energy is a significant portion of their OPEX (Operational Expenses). Therefore, green radio technologies will help to reduce the operating costs of wireless networks.

Green ICT has become a critical agenda item around the world. In this context, many organizations and standard bodies throughout the world including the European Commission (EC), US Environmental Protection Agency, US Department of Energy, ISO, IEC, ITU-T, ETSI, ATIS, and the IEEE are working towards the vision of green communication networks.

Motivation

Mobile communication has gained significant importance in today's society. As of 2010, the number of mobile phone subscribers has surpassed 5 billion [ABI10], and the global annual mobile revenue is soon expected to top $1 trillion [Inf10]. While these numbers appear promising for mobile operators at first sight, the major game-changer that has come up recently is the fact that the market is more and more driven by the demand for mobile data traffic [Cis10]. This is simply because Moore's law in semiconductors leads to continuously more powerful mobile devices with larger storage capacity, which in the era of Web 2.0 require regular synchronization with the Internet. Consequently, Moore's law can also be found in the increase of data rates in wireless communications, as illustrated in Fig. 1.1. The main challenge, however, is that mobile users tend to expect the fast and cheap Internet access that they are used from their fixed lines (e.g. ADSL), but anytime and anywhere while being on the move. This puts mobile operators under the pressure to respond to the increasing traffic demand and provide a more homogeneous quality of experience (QoE) over the area (often referred to as improved fairness), while continuously decreasing cost per bit - and addressing the more and more crucial issue of energy efficiency [FMBF10].

In this chapter, we now look into algorithm implementation aspects connected to CoMP. In Section 10.1, the issue of numerically robust and flexible multicell precoding is addressed, while Section 10.2 looks into the performance of interference rejection combining filters under practical conditions.

Robust and Flexible Base Station Precoding Implementation

As we have seen in Sections 6.3 and 6.4, spectral efficiency can be significantly increased if downlink CoMP schemes based on multi-cell joint signal processing are applied. Here, multiple base stations (BSs) perform a joint and coherent transmission towards multiple user equipments (UEs). Especially cell-edge users with symmetric links gain from the joint transmission (JT) with minimized inter-cell and inter-user interference compared to conventional cellular mobile networks.

In a cellular system, a spatial downlink resource allocation algorithm may switch between a single link transmission comprising one BS and one UE, a single BS multi-user scenario or finally a multi-cell joint transmission. The precoder must be able to cope with different system setups or matrix dimensions respectively. Such an implementation requires a higher signal processing flexibility as compared to non-cooperative setups. Physical link parameters like line-of-sight (LOS), non-LOS, or correlated transmit and receive antenna patterns influence the CoMP multi-user MIMO (MU-MIMO) eigenvalue spread and finally the spatial diversity and multiplexing gains.

Furthermore, in real-time hardware, the link parameters are related to finite precision effects.