Adaptive Coexistence between Cognitive and Fiber Networks

Adaptive Coexistence between Cognitive and Fiber Networks

Anwer Al-Dulaimi (Brunel University, UK), Saba Al-Rubaye (Brunel University, UK) and John Cosmas (Brunel University, UK)
DOI: 10.4018/978-1-4666-0017-1.ch013
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Cognitive radios are proposed as secondary users of spectrum to provision for the growth in mobile users and services. However, the dynamic changes in the wireless environment and spectrum availability are blocking the success of online communications for future cognitive mesh networks. As a solution, Cognitive Radio over Fibre (CRoF) subnet is developed through combining selected Base Stations (BSs) with the radio over fibre network. These CRoF-BSs attract the neighborhood cognitive BSs to send data through fibre whenever they are unable to formulate their own wireless links. This leads to the network splitting into many zones of services in which cognitive BSs are controlled by CRoF-BS zones. Therefore, a new paradigm for local resource sharing emerges through these architectural network modifications. In this chapter, the multi-zone structure is analyzed in order to formulate the rules of adaptation between the CRoF zones and the traditional cognitive networks.
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With the growth of wireless networks, there will be a future crisis of spectrum availability under the current spectrum allocation scheme. However, actual measurements show that current spectrum allocation makes the spectrum utilization inefficient because a wide range of the allocated frequency bands are rarely used. This gap between regulatory spectrum allocation and actual usage indicates that a new approach of spectrum usage is needed. Cognitive radio becomes an emerging idea to improve the spectrum utilization (Zhang, et al., 2005). The key enabling technology of dynamic spectrum access techniques is cognitive radio (CR) technology, which provides the capability to share the wireless channel with licensed users in an opportunistic manner. CR networks are envisioned to provide high bandwidth to mobile users via heterogeneous wireless architectures and dynamic spectrum access techniques (Akyildiz, et al., 2008). However, the need for CRs is motivated by many factors. Principally, though, the need for cognition is driven by the complexity of the radio systems themselves. The existence of software defined radio (SDRs) capable of implementing a near endless number of different waveforms with different modulation schemes, power levels, error control codes, carrier frequencies, etc., means that controlling the radio becomes a problem of combinatorial optimization (MacKenzie, et al., 2009).

There are two major approaches in cognitive radio: dynamic spectrum allocation and opportunistic spectrum access. For dynamic spectrum allocation, information on spectrum occupation is used for channel allocation and planning on a long-term basis. On the other hand, with opportunistic spectrum access, instantaneous information of channel usage by a primary user is observed and utilized to grant access to secondary users to increase utilization on a short term basis (Niyato & Hossain, 2009).

Earlier research done by (Mangold, et al., 2001) shows that wireless cognitive networks are able to coexist with primary networks, and operate at the same time and location without harmful interference. The Similar goals have been formulated of the coexistence between decentralized cognitive radio and primary radio systems is Specified (Watanabe & Ishibashi, 2007) by introducing two benchmarks, the network performance with and without cognitive radio technology, with the function of detecting radio signals to avoid the primary radio system is studied. A different cooperative transmission has been investigated in (Rohokale, et al., 2010), where the cognitive cooperative diversity is a strong technique which can provide the maximum throughputs. Cooperative opportunistic large array algorithms can improve the reliability as well as the energy efficiency of the communication.

The first significant radio-domain application for such smarter radios was the autonomous sharing of pooled spectrum, which the US Federal Communication Commission (FCC) endorsed relatively soon thereafter, to encourage the development of secondary spectrum markets (Arslan, 2007). In a cognitive network, autonomous and adaptive radios select their operating parameters to achieve individual and network-wide goals. The effectiveness of these adaptations depends on the amount of knowledge about the state of the network that is available to the radios (Komali, et al., 2010). In (Chung and Tsai, 2010), the authors present an effective centralized decision approach to deal with the problem regarding how a spectrum manager should periodically re-allocate the spectrum between two networks in a single cell, while maintaining both the call blocking and dropping probabilities in their acceptable levels, and improving the spectrum efficiency simultaneously.

Additionally, (Huschke & Leaves, 2003;Leaves & Moessner, 2004) take up the idea of the dynamic allocation of spectrum among multiple wireless network systems, first discussed and investigated from a general perspective, including its major advantages and technical issues. On the other hand, resource allocation is a fundamental problem in cognitive radio networks and has been discussed a lot in the recent works (Yuan, et al., 2007; Chen, et al., 2008: Jia, et al., 2008).

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