Home > Spectrum Sharing > Overview of Spectrum Sharing Techniques

In xG networks, one of the main challenges in open spectrum usage is the spectrum sharing. Spectrum sharing can be regarded to be similar to generic medium access control (MAC) problems in existing systems. However, as we will investigate in this section, substantially different challenges exist for spectrum sharing in xG networks. The coexistence with licensed users and the wide range of available spectrum are two of the main reasons for these unique challenges. we delve into the specific challenges for spectrum sharing in xG networks, overview the existing solutions and discuss open research areas. In order to provide a directory for different challenges during spectrum sharing, we first enumerate the steps in spectrum sharing in xG networks. The challenges and the solutions proposed for these steps will then be explained in detail. The spectrum sharing process consists of five major steps.

1. Spectrum sensing: An xG user can only allocate a portion of the spectrum if that portion is not used by an unlicensed user. In Section 4, the solutions and the challenges for this problem, i.e., spectrum sensing, are described. Accordingly, when an xG node aims to transmit packets, it first needs to be aware of the spectrum usage around its vicinity.

2. Spectrum allocation: Based on the spectrum availability, the node can then allocate a channel. This allocation not only depends on spectrum availability, but it is also determined based on internal (and possibly external) policies. Hence, the design of a spectrum allocation policy to improve the performance of a node is an important research topic.

3. Spectrum access: In this step, another major problem of spectrum sharing comes into picture. Since there may be multiple xG nodes trying to access the spectrum, this access should also be coordinated in order to prevent multiple users colliding in overlapping portions of the spectrum.

4. Transmitter-receiver handshake: Once a portion of the spectrum is determined for communication the receiver of this communication should also be indicated about the selected spectrum. Hence, a transmitter-receiver handshake protocol is essential for efficient communication in xG networks.Note that the term handshake by no means restricts this protocol between the transmitter and the receiver. A third party such as a centralized station can also be involved.

5. Spectrum mobility: xG nodes are regarded as‘‘visitors’’ to the spectrum they allocate. Hence, if the specific portion of the spectrum in use is required by a licensed user, the communication needs to be continued in another vacant portion. As a result, spectrum mobility is also important for successful communication between xG nodes. The existing work in spectrum sharing in xG networks aims to provide solutions for each step explained above. The existing solutions constitute a rich literature for spectrum sharing in xG networks.

The existing solutions for spectrum sharing in xG networks can be mainly classified in three aspects: i.e., according to their architecture assumption, spectrum allocation behavior, and spectrum access technique as shown in Fig. 12.

In this section, we describe these three classifications and present the fundamental results that analyze these classifications. The analysis of xG spectrum sharing techniques has been investigated through two major theoretical approaches. While some work uses optimization techniques to find the optimal strategies for spectrum sharing, game theoretical analysis has also been used in this area. The first classification for spectrum sharing techniques in xG networks is based on the architecture, which can be described as follows:

• Centralized spectrum sharing: In these solutions, a centralized entity controls the spectrum allocation and access procedures . With aid to these procedures, generally, a distributed sensing procedure is proposed such that each entity in the xG network forward their measurements about the spectrum allocation to the central entity and this entity constructs a spectrum allocation map.

• Distributed spectrum sharing: Distributed solutions are mainly proposed for cases where the construction of an infrastructure is not preferable. Accordingly, each node is responsible for the spectrum allocation and access is based on local (or possibly global) policies. The second classification for spectrum sharing techniques in xG networks is based on the access behavior. More specifically, the spectrum access can be cooperative or non-cooperative as explained below:

• Cooperative spectrum sharing: Cooperative (or collaborative) solutions consider the effect of the node’s communication on other nodes . In other words, the interference measurements of each node are shared among other nodes. Furthermore, the spectrum allocation algorithms also consider this information. While all the centralized solutions can be regarded as cooperative, there also exist distributed cooperative solutions.

• Non-cooperative spectrum sharing: Contrary to the cooperative solutions, non-cooperative (or non-collaborative, selfish) solutions consider only the node at hand . These solutions are also referred to as selfish. While non-cooperative solutions may result in reduced spectrum utilization, the minimal communication requirements among other nodes introduce a tradeoff for practical solutions.

These two solutions have generally been compared through their spectrum utilization, fairness, and throughput. The utilization and fairness in spectrum access has been investigated in , where the spectrum allocation problem is modeled as a graph coloring problem and both centralized and distributed approaches are investigated. Using this model, an optimization framework is developed. In this framework, secondary users allocate channels according to the interference that will be caused by the transmission. Both cooperative and non-cooperative approaches are considered such that cooperative approaches also consider the effect of the channel allocation on the potential neighbors. The simulation results show that cooperative approaches outperform non-cooperative approaches as well as closely approximating the global optimum. Moreover, the comparison of centralized and distributed solutions reveals that distributed solution closely follows the centralized solution. A similar analysis has also been provided in , where the effects of collaboration in spectrum access is investigated. An important assumption in these work is that secondary users know the location and transmit power of primary users so that the interference calculations can be performed easily. However, such an assumption may not always be valid in xG networks. Game theory has also been exploited for performance evaluation of xG spectrum access schemes. Especially, the comparison between cooperative and non-cooperative approaches has been presented in through game theoretical analysis. In, game theory is exploited to analyze the behavior of the cognitive radio for distributed adaptive channel allocation. It is assumed that users deploy CDMA and determine the operating channel and the coding rate by keeping transmission power constant.
It is shown that the cooperative case can be modeled as an exact potential game, which converges to a pure strategy Nash equilibrium solution. However, this framework has been shown not to be applicable for non-cooperative spectrum sharing and a learning algorithm has been proposed. The evaluations reveal that Nash equilibrium point for cooperative users is reached quickly and results in a certain degree of fairness as well as improved throughput. On the other hand, the learning algorithm for non-cooperative users converge to a mixed strategy allocation. Moreover, thefairness is degraded when non-cooperative approach is used. While this approach results in slightly worse performance, the information exchange required by selfish users is significantly low. Finally, the third classification for spectrum sharing in xG networks is based on the access technology as explained below:

• Overlay spectrum sharing: Overlay spectrum sharing refers to the spectrum access technique used. More specifically, a node accesses the network using a portion of the spectrum that has not been used by licensed users. As a result, interference to the primary system is minimized.

• Underlay spectrum sharing: Underlay spectrum sharing exploits the spread spectrum techniques developed for cellular networks . Once a spectrum allocation map has been acquired, an xG node begins transmission such that its transmit power at a certain portion of the spectrum is regarded as noise by the licensed users. This technique requires sophisticated spread spectrum techniques and can utilize increased bandwidth compared to overlay techniques.

The effects of underlay and overlay approaches in a cooperative setting are investigated in , where non-cooperative users are analyzed using a game theoretical framework. Using this framework, it is shown that frequency division multiplexing is optimal when interference among users is high. As a result, the overlay approach becomes more efficient than underlay when interference among users is high. The lack of cooperation among users, however, necessitates an overlay pproach. The comparative evaluations show that the performance loss due to the lack of cooperation is small, and vanishes with increasing SNR. However, in this framework, the cost and inaccuracies of information exchange between users are not considered. Another comparison of underlay and overlay approaches is provided in . The comparison is based on the influence of the secondary system on the primary system in terms of outage probability and three spectrum sharing techniques have been considered. The first technique (spreading based underlay) requires secondary users to spread their transmit power over the full spectrum such as CDMA or Ultra Wide Band (UWB). The second technique (interference avoidance overlay) requires nodes to choose a frequency band to transmit such that the interference at a primary user is minimized. Also an hybrid technique (spreading based underlay with interference avoidance) is investigated where a node spreads its transmission over the entire spectrum and also null or notch frequencies where a primary user is transmitting. Consequently, first, the interference statistics for each technique are determined for outage probability analysis. Then, the outage probability for each technique is derived assuming no system knowledge, perfect system knowledge, and limited system knowledge. Similar to other existing work, when perfect system knowledge is assumed, the overlay scheme outperforms the underlay scheme in terms of outage probability.
However, when interference avoidance is incorporated into spectrum sharing, the underlay scheme with interference avoidance guarantees smaller outage probability than the pure interference avoidance. In a more realistic case, when limited system knowledge is considered, the importance of the hybrid technique is exacerbated. The overlay schemes result in poor performance due imperfections at spectrum sensing. More specifically, a node can transmit at a channel where a primary user is transmitting. However, when underlay with interference avoidance is used, the interference caused to the primary user is minimized. Another important result is that a higher number of secondary users can be accommodated by the hybrid scheme than the pure interference avoidance scheme. The theoretical work on spectrum access in xG networks reveals important tradeoffs for the design of spectrum access protocols. As expected, it has been shown that cooperative settings result in higher utilization of the spectrum as well as fairness. However, this advantage may not be so high considering the cost of cooperation due to frequent information
exchange among users. On the other hand, the spectrum access technique, i.e., whether it is overlay or underlay, affects the performance in each setting. While an overlay technique focuses on the holes in the spectrum, dynamic spreading techniques are required for underlay techniques for interferencefree operation between primary and secondary systems. Considering the tradeoff between system complexity and performance, hybrid techniques may be considered for the spectrumtechnique. In the following two sections, we explain the existing spectrum sharing techniques that are combinations of the three classifications we have discussed in this section.