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Reconfigurable Software Defined Radio in 5G M o b i l e C o m m u n i c a t i o n S y s t e m s

Cognitive Radio Spectrum Sensing Framework Based on Multi-Agent Architecture for 5G Networks Zhenjiang Zhang, Wenyu Zhang, Sherali Zeadally, Yanan Wang, and Yun Liu

Abstract Due to the fixed frequency spectrum division policy, the radio spectrum resource is becoming increasingly scarce because many licensed frequency bands are not always fully utilized, and unlicensed users have no permission to use them. Cognitive radio has emerged as a promising solution for efficient radio spectrum utilization. One of the most important techniques of CR is spectrum sensing, which provides the real-time occupancy of available frequency bands for secondary users. However, current CR frameworks require SU terminals to conduct spectrum sensing and upload their sensing results to a fusion center (FC). This approach leads to many architectural challenges such as high design complexity, increase in hardware costs, inefficient resource usage, and high energy consumption. To address these challenges, we present a novel CR spectrum sensing framework by introducing a new functional entity called the spectrum agent (SA) to perform spectrum sensing tasks for SUs. We describe in detail the architecture and spectrum sensing mechanism of this new framework which provides a seamless integration of CR with next-generation (5G) cellular networks.

Introduction

Zhenjiang Zhang, Wenyu Zhang, Yanan Wang, and Yun Liu are with Beijing Jiaotong University. Sherali Zeadally is with the University of Kentucky.

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Nowadays, wireless communication is extensively used in personal wear, home life, work, studying, and entertainment, as well as industry, agriculture, medical care, education, transportation, finance, and other industry sectors. However, wireless networks still face multiple challenges, which include a wide range of data rates [1], increased indoor or hotspot traffic, high traffic data asymmetry, huge numbers of subscribers, energy consumption [2], and low latency requirements in crowded areas. To address these challenges, different types of cellular networks (e.g., low-powered femtocells typically deployed in residential districts and enterprise zones, and higher-powered picocells used for wider outdoor coverage or filling in macrocell coverage holes

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[3]), and various network systems (e.g., WiFi and device-to-device, D2D) are being seamlessly integrated into a holistic system leading to a promising paradigm for fifth generation (5G) mobile communication systems. The primary goal is to serve users with different quality of service (QoS) requirements in a spectrum in an energy-efficient manner [4]. Due to the limited spectrum availability, the assignment of radio spectrum resources is determined by national regulatory bodies such as the Federal Communications Commission (FCC) [5], which supports a fixed spectrum policy. As a result, the available radio spectrum for new wireless services and applications is limited. In fact, some frequency bands have been allocated to certain industries, but these industries cannot effectively utilize the entire frequency bands, resulting in low spectrum efficiency of the allocated spectrum and much waste of spectrum resources. In particular, with the emergence of 5G wireless communication systems as dense and heterogeneous networks, we are also witnessing a corresponding growth in the demand for wireless radio spectrum resources, causing a severe shortage of such resources. Thus, the need to improve the spectrum utilization has been attracting a lot of attention recently. Cognitive radio (CR) technology is a promising solution that attempts to alleviate the problem of scarce radio spectrum resources by using novel radio spectrum resource management techniques [6]. Spectrum sensing is one of the most important techniques of CR, providing real-time occupancy of available frequency bands for secondary users (SUs), without causing any interference to primary users (PUs). As a result, through spectrum sensing, SUs can reliably check the frequency band being used by a PU and change the radio parameters to exploit the unused part of the spectrum. After an SU takes up the spare frequency band, the SU should continue to monitor activities of the PU. Because the PU has the right of priority in the licensed frequency bands, once it reclaims the frequency band, the SU must vacate the band unconditionally and is redirect-

IEEE Wireless Communications • December 2015

ed to other underutilized white space [7]. This dynamic spectrum access technique can achieve more efficient spectrum utilization. However, in traditional CR networks, every SU should possess cognitive capability [8] (i.e, the ability to detect and collect spectrum information from the surrounding radio channels, e.g., information about the usage of frequency bands, bandwidth, and energy, and identify the best available spectrum). This means that each user terminal needs strong cognitive capability, which is actually difficult to achieve in practice. Next, the radio spectrum range to be detected is across hundreds of megahertz, which requires great technical ability and is also associated with high costs. Additionally, the periodic and frequent detection of spectrum holes also causes greater energy consumption. As 5G networks are poised to become highly dense networks, only some users are needed to perform spectrum sensing to avoid resource wasting and information redundancy. In light of the above issues along with the requirements of dense and heterogeneous 5G networks, this article makes the following research contributions: • We discuss several drawbacks of the current spectrum sensing framework in CR, including high design complexity and costs, additional energy consumption of SUs, and waste of resources of SUs. • Then we propose a novel CR spectrum sensing framework with multiple agents, which can support the majority of functions of the existing CR framework. The main idea here is to replace the cognitive capability of SUs with spectrum sensing along with the analysis performed by spectrum agents (SAs). By utilizing SAs, to a certain extent, we can reduce the energy consumption and minimize the waste of resources by user terminals. • We also propose two spectrum sensing modes (SU mode and SA mode) of SAs when the SU possesses the decision making ability for the available spectrum information. • Finally, we discuss some emerging problems associated with our proposed framework.

Challenges of Spectrum Sensing in Cr Networks The traditional CR spectrum sensing procedure is described in Fig. 1. Through spectrum sensing, multiple SUs scan a wide range of spectrum channels to collect information (e.g., the activities of PUs) about the surrounding radio environment. According to the fusion and decision results from the FC, the SU determines which frequency band is not being used by a PU at a particular time in a particular position, thereby seizing the opportunity to utilize the available spectrum. That is, SUs contain the cognitive capability to perform spectrum sensing. Radio spectrum occupancy information can be obtained through various signal detection and processing techniques, including energy detection, feature detection, and matched filtering and coherent detection. Therefore, CR devices must be able to detect and analyze the frequency


Radio environment

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Figure 1. Conventional CR spectrum sensing procedure [SU: Secondary User; FC: Fusion Center]. bands in a specific area to find the available spectrum holes. Without causing any interference in the existing communication system, cognitive users can determine which frequency bands can be used for data transmission through a frequency scan. Spectrum sensing for spectrum holes only depends on the SUs. Although the transceiver is relatively complex, it has strong adaptability and low cost. Consequently, the main systems do not need to be changed. However, with the emergence of 5G wireless communication, numerous devices and networks will be interconnected, causing constantly growing traffic demand. Considering the fact that heterogeneous and dense networks consist of a mix of different types of cells and devices, the performance of spectrum sensing is quite limited and will face many challenges, as discussed below. High design complexity and costs: Spectrum sensing requires SUs to detect whether the PU is taking up certain frequency bands or not in order to determine the occupancy status of the entire radio spectrum range. However, the detected spectral range exists across hundreds of megahertz, which means that the sensing device must have high hardware capabilities, such as a digital-to-analog converter with high speed and high resolution, a high-speed signal processor, a broadband RF unit, and a single/double link structure, to achieve the required speed of detection and sensitivity [9]. It is difficult to embed a device with such powerful features in a user terminal. Indeed, to date, no terminal manufacturer has produced terminals with such a powerful spectrum sensing function, and it seems that none is likely to do so in the future. With the advent of 5G networks, the number of terminals will grow exponentially, and it is highly unlikely that each user terminal will support the function of spectrum sensing. The large number of users will increase the cost of achieving the CR function, which is not consistent with the market discipline. In fact, if the cost is too high, user terminal equipment manufacturers will be unwilling to place such an expensive frequency spectrum detecting module in their equipment. Additional energy consumption of SUs: One of the main goals of 5G networks is to enable user terminals to always be able to access the networks. The devices supported by 5G networks will involve far more than smartphones and will include smart watches, fitness wristbands, smart home devices, and even those that constitute the Internet of Things (IoT), all of which will need to be supported. For mobile terminals, battery life is the key factor determining the use cycle, but currently user terminals still suffer from poor battery performance. If every user device is to be equipped with a spectrum sensing fea-

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One key idea of the new framework design is to remove the cognitive capability from SUs and let a new communication entity (a spectrum agent SA) perform spectrum sensing. The goal is to reduce the energy consumption and wasting of resources at the user terminal and ultimately improve the overall spectrum efficiency.

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Figure 2. 5G networks with the proposed framework based on spectrum agents. ture, the resulting energy consumption of SUs will increase very rapidly. In addition, when an SU occupies an idle frequency band, the CR network must periodically and instantly detect whether the band is occupied by the PU again. Data transmission and spectrum sensing must be executed alternately because the transmission of data and detection cannot be performed simultaneously. On one hand, we need to spend less time on spectrum sensing in order to improve the throughput of data transmission; on the other hand, we need to make sure that the frequency band of the spectrum sensing is wide enough to prevent disturbing the communication of PUs. Thus, a conflict exists between the data transmission rate and the spectrum sensing time and bandwidth. Waste of resources of SUs: In current CR frameworks, cooperative spectrum sensing greatly improves the sensing performance because of its advantage in diversity gains against multipath channel fading and shadowing. In fact, this multi-user cooperative spectrum sensing model incurs high system overhead because too many SUs may conduct spectrum sensing tasks. In addition, the cumulative interference caused by the simultaneous transmission of multiple CR users is not considered. The 5G network is becoming denser, and the competition for spectrum is also increasing, which means more cognitive users will exist. However, in dense networks, we do not need as many SUs to carry out cooperative spectrum sensing. Instead, we can achieve higher detection accuracy with fewer reliable users. In fact, a large number of users is distributed in the same area where the spectrum-sensing information will be similar, resulting in a substantial waste of resources and data redundancy. Some studies have proposed optimization methods

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such as selecting some of the cognitive users to perform collaborative spectrum sensing, but which cognitive users should be selected becomes another challenge because it is difficult to know the exact spatial distribution and the surrounding spectrum occupancy status.

Proposed CR Spectrum Sensing Framework Based on Multi-Agent Architecture

To support future applications with increasing functionality and higher data rates, 5G networks must improve their spectrum efficiency further. Meanwhile, the integration of highly dense networks and heterogeneous networks [9] is an emerging trend in 5G networks. To address the above challenges of traditional CR and meet the emerging 5G network requirements, we need a dramatic change in the design of traditional CR spectrum sensing. Thus, this article proposes a novel CR spectrum sensing framework based on a multi-agent architecture. One key idea of the new framework design is to remove the cognitive capability from SUs and let a new communication entity (a spectrum agent, SA) perform spectrum sensing. The goal is to reduce the energy consumption and waste of resources at the user terminal and ultimately improve overall spectrum efficiency. As mentioned earlier, future 5G networks will be dense and include heterogeneous networks consisting of multiple types of cells, mixed with D2D and other communication systems, as illustrated in Fig. 2. In contrast to previous CR networks, various kinds of cells will deploy a large number of SAs in future 5G networks. PUs and SUs will still represent the licensed users and


unlicensed users, respectively. However, the SU differs from the cognitive user, not having cognitive capability and no longer performing spectrum sensing. The SU can only send a spectrum request to the SA (as shown by the dotted line in Fig. 2) to dynamically access the licensed frequency bands that are not occupied by PUs without creating interference for the PUs. Instead, the SA can periodically detect the activities of PUs and continuously report the available spectrum information to the agent fusion center (FC), which performs the fusion and decision (e.g., hard or soft decision) of the spectrum sensing information transmitted by SAs using different fusion decision algorithms. Then the FC broadcasts the fusion results to each SA. Because of centralized and distributed spectrum sensing, the FC can serve as a base station or as any other SA. By using the new communication entity, the SA, which performs spectrum sensing, some of the problems mentioned above can be alleviated. It can reduce the design complexity of network equipment and minimize the cost of hardware. Moreover, the SU does not have cognitive capability and no longer performs spectrum sensing, which reduces energy consumption and resource consumption, thereby improving spectrum utilization in 5G networks. In the proposed framework, in addition to macrocells, picocells and femtocells are also deployed to a certain number of spectrum monitoring points that represent the proposed SAs. The goal here is to let SAs cover the entire macrocellular area. In particular, in some dense districts such as office buildings, schools, and railway stations, a larger number of SAs will be assigned. An SA is independent of the number of users, and continuously scans the spectrum usage in the cognitive environment, transmitting real-time detection results to the FC. The SA can also cooperate with other detection points to achieve collaborative spectrum sensing. In addition, it can be used as a relay or an access point to achieve short-range transmission, which corresponds to an emerging trend in 5G. In the proposed framework, several micro base stations or tiny base stations can serve as SAs. As for networks without base stations, such as WiFi and D2D, some SAs can be deployed nearby. However, a full description of a scheme (or schemes) for agent deployment is beyond the scope of this article.

Spectrum Sensing Mode As a result of the introduction of SAs for CR networks, spectrum sensing and other working processes will need to change. In the proposed framework, the spectrum sensing process mainly includes four operations: spectrum request, cooperative spectrum sensing, fusion and decision, and spectrum allocation. If the SU possesses the decision making ability for the available spectrum information, SAs may conduct the spectrum sensing operation in two modes: SU mode and SA mode. SU mode: Each SU contains autonomous decision making capability for available frequency bands in this mode, completely independent


SA

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Figure 3. SU mode. of the FC. Similar to the description in Fig. 3, due to the publicly available position information about SAs, if an SU needs to transmit data, it will automatically send the spectrum request to every SA nearby. Upon receipt of the request, each SA performs a spectrum-sensing operation on Pus and makes a local decision based on the spectrum-sensing information. Then the usage information of all the frequency bands will be sent to the SU. When the SU receives all the local results from the SAs, it will make a global decision to select the best frequency band and SA for performing data transmission according to the QoS and other metrics. In this mode, although each SU has the decision making ability, it will not cause a lot of energy consumption and waste of resources because the SU will make a decision only when it sends a request for a frequency band. SA mode: Figure 4 shows the SA mode of the CR framework, in which an SU is a general user terminal that only sends the spectrum request to the SA. This mode consists of two patterns: active and passive. In the active pattern, the order of operations are depicted as in Fig. 4: SAs actively conduct the sensing operation in the cognitive environment and periodically send the sensing information (local decision or original sensing data) to the FC. Meanwhile, the FC carries out the fusion and decision, and then broadcasts the fusion results to all SAs. When the spectrum request from an SU is received by the SA, the latter determines the best frequency band for the SU according to the QoS demand and fusion results of the FC. Generally, this active sensing mode is more suitable for districts with a large number of users. However, in the passive pattern, the operating order is . SAs do nothing unless they receive spectrum requests from the SUs. When receiving a spectrum request sent by an SU, the corresponding SA will assign the sensing tasks to other SAs, and they will perform cooperative spectrum sensing and transmit the sensing information to the FC for a global decision. Then the FC broadcasts the decision results to each agent. Ultimately, the best frequency band based on the fusion results and QoS demand will be selected for the SU. Since the passive sensing mode can save considerable energy and resources, it is suitable for sparse places.

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PU 1

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Figure 4. SA mode. In particular, for the above two modes, when an SU takes up an available frequency band for data transmission, SAs should periodically and frequently carry out spectrum sensing for the current frequency band occupied by the SU and other frequency bands. Frequent detection can ensure that the SU will not cause any interference to the PU once the PU appears at the band again. In this case, the SA promptly informs the SU to abandon this band and provide it with a new available frequency band for communication.

Issues Associated with the Proposed Framework The proposed CR spectrum sensing framework with multiple spectrum agents not only effectively improves spectrum utilization, but to a certain extent reduces energy consumption and resource waste of user terminals and the overhead of the whole network. However, there are still some further issues we must consider in the future. We discuss some of these issues below. SA protocol: The operations of the functional entity, the SA, and the structure of cognitive network communication have changed. It is therefore necessary to develop and formulate a standardized SA protocol. For example, if a user terminal communicates with an SA, the protocol needs to change the data format of the SA and clearly define the control information. In addition, the data packet transmission, services, and interface standards of each layer also need to be further investigated in future work. SA deployment: To meet the spectrum request of each SU in the network, the deployment of SAs should ensure coverage of the entire network. However, this does not mean that it is better to deploy a larger number of SAs because an unreasonably large deployment of SAs will cause unnecessary waste of resources and increase costs. Therefore, we must investigate various other factors such as the number of users and buildings, the signal interference between the adjacent cells, the resource utilization rate, the overhead costs, and other factors. We need to study the various trade-offs and opportunities of these factors to achieve maximum coverage of SAs. SA detection period and delay: After the SU occupies the available frequency band, SAs still need to detect the activities of the PU. Thus, the detection period becomes an important issue. If the cycle is set too long, an SA cannot learn

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about the activities of the PU over a long time. Once the PU occupies the band again, the SA cannot instruct the SU to give up the band in a timely manner, which will cause some interference to the PU. Conversely, if the period is very short, SAs will frequently perform monitoring and constantly update the spectrum usage information, which will increase the communication overheads and may also cause more waste of resources. In addition, SAs require a certain period of time to send the decision results to the SU, which will cause additional delays [11], thereby affecting the communication activities of the PU. Therefore, how to reasonably determine the detection period and how to reduce the delay are the key design challenges that the proposed framework needs to address. Sensing tasks assignment: In the passive sensing mode, when an SA receives a spectrum request from an SU, it will allocate the sensing tasks to other SAs and then perform cooperative spectrum sensing to achieve the optimum detection/false alarm probability. However, an open issue that still remains is how to select which SAs should be assigned to the sensing tasks. That is, the SA that has received the request already should select its cooperative partners to be able to get a higher spectrum utilization rate. In addition, whether the specific position information and channel condition of each SA, as well as the SA’s surrounding environment, influence the assignment of tasks also needs to be further explored. Inter-SA handoff: In dense networks, a large number of SUs are constantly moving. In this case, when the mobile SU moves from an SA cooperation group (all SAs in this group are performing cooperative spectrum sensing) to another group, how to switch to a more suitable channel for information transmission, without causing any interference to other users, still needs further investigation. At the same time, in some special cases, such as switching from a cell with SAs to an area without any SAs and vice versa should also be taken into consideration. SA scalability: Scalability not only requires satisfying the continuous requirements of users but also the ability to meet the emerging needs arising from extensions or upgrades as a result of technological advances. In 5G networks, more microcells with smaller coverage will exist. Each base station may only provide several users with network services. In this case, it is unnecessary to set up independent SAs. Instead, SAs can be embedded in a small base station, as well as a WiFi access point or receiver, to perform the spectrum sensing task. Therefore, how to embed and upgrade SAs without affecting the operation of the original network system is another issue that must be investigated in the future.

Conclusion In this article, we have proposed a novel cognitive radio spectrum sensing framework based on a multi-agent architecture for 5G wireless communication systems. We have removed the cognitive capability from the original cognitive


users and deployed spectrum sensing agents to perform cooperative spectrum sensing and spectrum analysis. In addition, two spectrum sensing modes are explained in detail. In theory, the proposed framework can address some of the spectrum usage issues that currently exist in traditional cognitive radio spectrum sensing and meet the requirements of 5G networks. To a certain extent, it can also alleviate the energy consumption and resource waste problems of the whole network, which will ultimately improve overall spectrum efficiency. Although there are still several outstanding issues that must be explored further with the proposed framework, we believe that it can still make great contributions to the area of cognitive radio for 5G networks through future research.

Acknowledgments We thank the anonymous reviewers for their useful feedback, which helped us improve the quality and presentation of this article.

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[8] E. Axell, G. Leus, and E. G. Larsson, “Spectrum Sensing for Cognitive Radio: State-of-the-Art and Recent Advances,” IEEE Signal Processing Mag., vol. 29, no. 3, May 2012, pp. 101–16. [9] S. Atapattu, C. Tellambura, and H. Jiang, “Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE Wireless Commun. Trans., vol. 10, no. 4, Jan. 2011, pp. 123–41. [10] P. Demestichas, A. Georgakopoulos, and D. Karvounas, “5G on the Horizon: Key Challenges for the Radio-Access Network,” IEEE Vehic. Tech. Mag., vol. 8, no. 3, Sept. 2013, pp. 47–53. [11] X. Hong, J. Wang, and C. X. Wang, Cognitive Radio in 5G: A Perspective on Energy-Spectral Efficiency Tradeoff,” IEEE Commun. Mag., vol. 52, no. 7, July 2014, pp. 46–53.

Biographies Zhenjiang Zhang ([email protected]) received his Ph.D. degree in communication and information systems from Beijing Jiaotong University, China, in 2008. He has been a professor since 2014 in the Department of Electronic and Information Engineering, Beijing Jiaotong University. He has published about 60 professional research papers. His research interests are wireless sensor network techniques, including multisource data fusion, security and privacy, routing, and energy management.

Although there are still several outstanding issues that must be explored further with the proposed framework, we believe that it can still make great contributions to the area of cognitive radio for 5G networks through future research.

W enyu Z hang received his B.E. degree in communication engineering from Beijing Jiaotong University in 2013. He is currently a graduate student in the Department of Electronic and Information Engineering. His interests are cognitive radio and multisource data fusion. Sherali Zeadally received his Bachelor’s degree in computer science from the University of Cambridge, United Kingdom, and his doctoral degree in computer science from the University of Buckingham, United Kingdom. He is an associate professor in the College of Communication and Information at the University of Kentucky. He is a Fellow of the British Computer Society and a Fellow of the Institution of Engineering Technology, United Kingdom. Y anan W ang received her B.E. degree in communication engineering from Hebei University in 2014. Now she is studying for a Master’s degree in the Department of Electronic and Information Engineering, Beijing Jiaotong University. Her major is communication and information systems. Her research interests are spectrum sensing technology in cognitive radio. Yun Liu is a professor in the School of Electronic and Information Engineering, Beijing Jiaotong University, where she received her Ph.D. degree in communication and information systems. She is interested in opinion dyanamics, network/information security, computer communication, and intelligent transportation systems.

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