LITMIR.BIZ

Chapter 1 Introduction

This book targets the field of dynamic spectrum access (DSA), which can also be referred to as dynamic spectrum awareness, dynamic spectrum management (DSM), or cooperative spectrum management. The book does not attempt to explain or summarize what is already established in standardization efforts, such as the dynamic spectrum access network (DySPAN), also known as the Institute of Electrical and Electronics Engineers (IEEE) P1900,¹ or the Wireless Innovation Forum (WINNF) Spectrum Access System (SAS). Rather, it's goal is to help engineers design the most suitable DSA approach for whatever wireless communications system is being built. DSA is needed for a wide range of civilian and military communications systems to dynamically optimize spectrum use. A form of DSA can be used for licensed and unlicensed spectrum bands in a wide variety of systems. DSA is presented in this book with a wider context than cognitive radios. There are many commercial and military communications systems that are not necessarily categorized as cognitive systems, but use these techniques to dynamically manage scarce spectrum resources. In today's ever‐increasing appetite for bandwidth, every extra Hertz a wireless system can use means an increased rate of transmission in bits per second (bps) over the air (OTA). Different types of communications systems are evolving to add incremental DSA capabilities. For example, military communications systems are moving towards DSA with a mixed use of new cognitive waveforms and legacy noncognitive waveforms.

      DSA is being deployed and enhanced in many commercial and defense systems. Cellular long‐term evolution (LTE) and fifth‐generation (5G) literature shows myriad DSA approaches that are combined with beam forming, multiple input and multiple output (MIMO) antennas, and arbitration by the base station for efficient spectrum use. The United States (US) Defense Advanced Research Projects Agent (DARPA) next‐generation (XG) program is one example of defense initiatives for developing cognitive DSA radios. In both commercial and defense systems, DSA techniques are still evolving. There will always be room for further enhancement of DSA techniques in existing systems and for the development of better DSA techniques in new systems.

      One goal of this book is to decouple DSA from cognitive radios and cognitive networks as covered in Part 1. The literature often presents DSA as the only drive behind cognitive radios. Even the US Federal Communications Commission (FCC) early definition of cognitive radio is based on the radio being able to opportunistically use unlicensed spectrum. There could be systems that lack the definition of being cognitive systems, which can use a form of DSA. Also, as cognitive wireless communications systems evolve, they should not be viewed as solely DSA systems. Chapter 4 explains the use of a form of “cognitive” reactive routing, which is built on top of DSA. The use of this reactive routing technique with military wireless communications systems enhances these systems' low probability of detection (LPD) and low probability of interception (LPI) capabilities.

This book is meant for a reader who has basic knowledge of wireless communications and wireless networks, and has an interest in the design and implementation of the physical layer and medium access control (MAC) layer of wireless communication systems to include cognitive radios and cognitive networks. Regardless of whether the system under design is being targeted to use a licensed spectrum band or an opportunistic spectrum band, there is a need to consider DSA. It is more obvious that a system built to use opportunistic spectrum would need DSA capabilities. However, the design of systems such as cognitive mobile ad‐hoc networks (MANETs) with allocated frequency bands has to consider the cooperative use of the allocated frequency bands to maximize the effective bandwidth of the formed network.

      This book presents the most generic model to consider for DSA design. This model represents a large‐scale collection of heterogeneous hierarchical MANETs that use a mix of licensed and unlicensed spectrum bands. With this model, DSA becomes a set of cloud services that can span from the network edge to the network core and to a single centralized point (root) in the network core. With this generic model, DSA decisions can be made anywhere in the network. An entity making a DSA decision can use local information, information shared with and obtained from peer nodes in a cooperative distributed manner, or information obtained from lower or higher hierarchical entities. With this model, the most studied comprehensive case of DSA, which is the cellular 5G DSM, can be seen as a special case of this generic model. The first chapter in the second part of this book introduces this generic model followed by a chapter dedicated to 5G DSM.

1.1 Summary of DSA Decision‐making Processes

      One aspect of optimizing DSA performance is to turn every node in every network into a spectrum sensor. The technology of spectrum sensing has evolved very well lately where a small size chip can perform comprehensive spectrum sensing techniques with minimal requirements on the node size, weight, and power (SWaP). The IEEE DySPAN standardization has a working group that defined the interface between a sensing hardware and the node module that is responsible for interfacing to the sensing hardware. This node module can be a distributed cognitive agent or a mere information collection agent.

      Spectrum sensing can be tabulated under two main categories. The first category is augmented sensing where specialized spectrum sensing hardware/software is used as mentioned above. The second category is same‐channel in‐band sensing. With same‐channel in‐band sensing, the physical layer metrics of a received communication signal are leveraged to generate spectrum sensing information. This is a form of piggybacking of spectrum sensing over the ongoing communications signal, which should only require some processing of the physical layer metrics to obtain valuable spectrum sensing information. A comprehensive DSA solution may rely on both augmented sensing and same‐channel in‐band sensing. The advantages of same‐channel in‐band sensing, even in the presence of a specialized spectrum sensing hardware for augmented sensing, are detailed in this book.

      There are two main components of the DSA design to consider. The first component pertains to obtaining spectrum sensing information from a sensor and being able to configure the sensor on what frequency bands to sense and what parameters to send to the distributed agent interfacing to the sensor. The second, and more challenging, DSA component is what to do with the obtained spectrum sensing information. This is sometimes referred to as decision fusion (DF). DF is a critical part of designing DSA capabilities where the design has to consider the following decision‐making types: