Survey Open Research Issues In Dynamic Spectrum Access Networks Computer Science Essay

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The current wireless networks are characterized by a fixed spectrum assignment policy. The license users do not utilize the spectrum all the time. In this way large amount of spectrum is not used efficiently and goes unused. The spectrum is a very limited and the inefficiency in the spectrum usage gives birth to a new communication paradigm i.e. to access the existing wireless spectrum opportunistically. This new paradigm is referred to as Next Generation Networks, Cognitive Radio (CR) Networks as well as Dynamic Spectrum Access (DSA). The novel functionalities and current research challenges of CRN are explained and open research issues are discussed.


The common approach to spectrum allocation is based on statically allocating long-term licenses on portions of the spectrum to providers and their users by government agencies. In [1], according to the FCC temporal and geographical variation in utilization of assigned spectrum ranges from 15 to 85 percent. This type of spectrum allocation leads to underutilization of spectrum as the number of users to access the spectrum has increased dramatically over the years. To address this problem FCC has approved the usage of unlicensed devices in licensed band. Therefore, the option of reusing assigned spectrum when it is temporarily (and locally) available is frequently referred to as Dynamic Spectrum Access (DSA) which tries to increase the efficiency of spectrum usage. The key enabling technology of DSA networks is cognitive radio (CR) technology, which provides the capability to share the wireless channel with licensed users in an opportunistic manner. A ''Cognitive Radio'' is a radio that can change its transmission parameters based on interaction with the environment in which it operates [1]. The concept of DSA is to identify spectrum holes or white spaces and use them to communicate as shown in Fig 1. White spaces change over time, a cognitive radio is used to "jump" from one chunk of spectrum to another. The novel characteristic of cognitive radio transceiver is a sensing capability of the RF front-end in a large range of frequencies.

There are unique challenges imposed in CRN due to the high fluctuation in the available spectrum and different quality of service (QoS) requirements of applications. In order to address these challenges, each CR user in the CR network must:

• Determine the portions of spectrum available

• Select the best available channel

• Coordinate access to this channel with other users

• Vacate the channel when a licensed user is detected

Fig 1[1]

These capabilities can be realized through spectrum management functions that address four main challenges: spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. The CR network communication components and their interactions are shown in Fig. 2. It is evident from the significant number of interactions that the CR network functionalities necessitate a cross-layer design approach. It is quite evident that spectrum sensing and spectrum sharing cooperate with each other for better spectrum efficiency. Keeping in view the dynamic nature of the underlying spectrum, application, transport, routing, medium access and physical layer functionalities are carried out in a cooperative way in spectrum management and spectrum mobility functions.

Fig .2[1]

This paper presents the definition, architecture and functions of the DSA. In sec 2 we will present the architecture and functions of the DSA Networks. Spectrum Sensing, spectrum management, spectrum mobility and spectrum sharing and their challenges are discussed in section 3. 4, 5 & 6 respectively. Finally, we explain the upper layer issues in Section 7 and conclude the paper in Section 8.

DSA network architecture:

The components of DSA network architecture are shown in the figure 3 and all possible scenarios are considered.

Primary Network: A network which has an exclusive right to a certain spectrum band or licensed network. The components of the primary network are:

Primary user (or licensed user)

Primary base-station (licensed base-station)

Next Generation Network / DSA Network: They do not have license to operate in a desired band. However, spectrum is opportunistically accessed. They can be deployed both as an infrastructure network and an adhoc network as shown. The components of a DSA network are as follows:

DSA Network user (unlicensed user, secondary user)

DSA Network Base Station.

Spectrum Broker is a central network entity that plays a role in sharing the spectrum resources among different DSA networks

Fig 3 [1]

2.1) DSA network Functions:

The DSA Network has the capability to operate in both licensed band as well as in unlicensed band.

Licensed Band: In a licensed band the DSA user will first sense a spectrum hole with the help of cognitive communication techniques in the licensed band. Although the main purpose of DSA network is to determine the best available spectrum, DSA functions in the licensed band are mainly aimed at the detection of the presence of primary users. Different techniques have been proposed which are discussed in detail in the latter section for spectrum sensing. The most important issue is to avoid interference with the primary user in this band. As soon a primary user is detected the CR user should vacate the band and find a new band for communication, this is called spectrum handoff.

Unlicensed Band: Since there are no license holders, all network entities have the same right to access the spectrum bands. Multiple networks can exist in the same area and communicate using the same portion of the spectrum without any handoff.

Spectrum Sensing:

The main purpose of spectrum sensing is to find spectrum holes to provide more spectrum access opportunities to DSA network users without interfering with the primary network user. The current technology of radio frequency (RF) front-ends is unable to sense and transmit at the same time, which decreases the opportunities to transmit leading to sensing efficiency problem [2].

One of the most efficient ways of spectrum holes detection is to detect the primary users which are receiving data within the communication range of a DSA user [1]. Practically, however, it is challenging for a cognitive radio to have a direct measurement of a channel between a primary receiver and a transmitter. Since cognitive radio (CR) networks are responsible for detecting the transmission of primary networks and avoiding interference to them, DSA networks should intelligently sense the primary band to avoid missing the transmission of primary users. Thus, sensing accuracy has been considered as the most important factor to determine the performance of CR networks. Hence, recent research has been focused on improving the sensing accuracy for interference avoidance [2].

For the detection of spectrum holes different techniques have been developed. We will go through these techniques in the following subsection Detection and then in the next section we will discuss the open research issues in this area.

3.1) Detection:

In [1], three spectrum sensing methods are discussed: Transmitter Detection, Cooperative Detection & Interference based detection.

Transmitter detection:

Transmitter detection approach is based on the detection of the weak signal from a primary transmitter through the local observations of a DSA user. Three schemes are generally used for the transmitter detection: Match Filter detection, Energy Detection & Feature Detection. Match Filter Detection has a fast sensing time. However, the matched filter necessitates not only a priori knowledge of the characteristics of the PU signal but also the synchronization between the PU transmitter and the CR user. If this information is not accurate, then the matched filter performs poorly. Furthermore, CR users need to have different multiple matched filters dedicated to each type of the PU signal, which increases the implementation cost and complexity [1]. Energy detector is optimal to detect the unknown signal if the noise power is known. In the energy detection, CR users sense the presence/absence of the PUs based on the energy of the received signals. The performance of energy detection is susceptible to uncertainty in noise power. In addition, while the energy detector can only determine the presence of the signal but cannot differentiate signal types. Feature detection determines the presence of PU signals by extracting their specific features such as pilot signals, cyclic prefixes, symbol rate, spreading codes, or modulation types from its local observation. The main advantage of the spectral correlation function is that it differentiates the noise energy from modulated signal energy [3].

Cooperative detection:

In most cases a DSA network is physically separated from the primary network so there is no interaction between them. Thus, with the transmitter detection, the DSA user cannot avoid the interference due to the lack of the primary receiver's information. Moreover, the transmitter detection model cannot prevent the hidden terminal problem, in [1]. Cooperative detection shares the sensing information from other users for more accurate detection. Cooperative detection can be implemented either in a centralized or in a distributed. While cooperative approach provides more accurate sensing they impose overhead traffic. Furthermore, the primary receiver uncertainty problem caused by the lack of the primary receiver location knowledge is still unsolved in the cooperative sensing. The multi-path fading and shadowing effects can be reduced so that the detection probability is improved in a heavily shadowed environment [10].

Interference Based Detection:

A new model introduced by the FCC called the interference temperature model [1]. The concept of interference temperature is identical to that of noise temperature. It is a measure of the power and bandwidth occupied by interference. The idea is that by taking a single measurement, a cognitive radio can completely characterize both interference and noise with a single number. Interference and noise behave differently, interference is typically more deterministic and uncorrelated to bandwidth, whereas noise is not. For a given geographic area, the FCC would establish an interference temperature limit. This value would be a maximum amount of tolerable interference for a given frequency band in a particular location and as long as DSA user does not exceed this level it is allowed to carry its operation in the band. Any unlicensed transmitter utilizing this band must guarantee that their transmissions added to the existing interference must not exceed the interference temperature limit at a licensed receiver. As a more interference appears, the noise floor changes and is increased at certain points.

Although all these efforts enable CR users to enhance the sensing accuracy, but the current hardware limitations of CR users should monitor the spectrum continuously through the radio frequency (RF) front-end to avoid interference, [2]. But practically the RF front-end cannot differentiate between the signals i.e. PU/ DSA signal. [8]. for this reason CR users necessitate a periodic sensing structure where sensing and transmission operations are performed in a periodic manner with separate observation period and transmission period. In this structure, CR users should stop their transmissions during the sensing time to prevent false alarms triggered by unintended CR signals.

This periodic sensing structure introduces the Interference avoidance & Sensing efficiency

There is a tradeoff between interference and sensing efficiency. For interference avoidance, the observation time needs to be long enough to achieve sufficient detection accuracy, i.e., longer observation time leads to higher sensing accuracy, and hence to less interference. But as the observation time becomes longer, the transmission time of CR users will be decreased. Conversely, while a longer transmission time enhances the sensing efficiency, it causes higher interference due to the lack of sensing information. Hence, observation time and transmission time are the sensing parameters that mainly influence both the spectrum efficiency and interference avoidance. Thus, the proper selection of these sensing parameters is the most critical factor influencing the performance of CR networks [2].

3.2) Sensing Challenges:

There exist several open research challenges that need to be investigated for the development of the spectrum sensing function:

Detection capability: One of the main requirements of DSA networks is the detection of the primary users in a very short time novel spectrum sensing algorithms need to be developed such that the number of samples needed to detect the primary user is minimized within a given detection error probability.

Optimization of cooperative sensing: Cooperative sensing introduces another crucial issue. By requesting the sensing information from several CR users, the user that initiates the cooperative sensing, improves the accuracy but also increases the network traffic. However, this also results in higher latency in collecting this information due to channel contention and packet re-transmissions. Thus, it is required to consider these factors which must be optimized for correct and efficient sensing.

Spectrum management:

In DSA networks, the unused spectrum bands will be spread over wide frequency range including both unlicensed and licensed bands. These unused spectrum bands detected through spectrum sensing show different characteristics according to not only the time varying radio environment but also the spectrum band information such as the operating frequency and the bandwidth. DSA networks should decide on the best spectrum band to meet the QoS requirements over all available spectrum bands, new spectrum management functions are required for DSA networks, considering the dynamic spectrum characteristics. We classify these functions as spectrum sensing, spectrum analysis, and spectrum decision.

Spectrum Analysis:

In[1], in order to describe the dynamic nature of DSA networks, each spectrum hole should be characterized considering not only the time-varying radio environment and but also the primary user activity and the spectrum band information such as operating frequency and bandwidth. Hence, it is essential to define parameters such as interference level, channel error rate, path-loss, link layer delay, and holding time that can represent the quality of a particular spectrum band.

Spectrum Decision:

Once all available spectrum bands are characterized, appropriate operating spectrum band should be selected for the current transmission considering the QoS requirements and the spectrum characteristics.

4.3) Management Challenges:

Cooperation with reconfiguration: Once the available spectrum bands are characterized, the most appropriate spectrum band should be selected by considering the QoS requirements (sustainable rate, delay, jitter, average session time, acceptable loss rate, etc) and the spectrum characteristics. However, according to the reconfigurable transmission parameters such as modulation type, error control scheme, and communication protocol, these spectrum characteristics change significantly. Sometimes, with only reconfiguration, CR users can maintain the quality of the current session. For example, even if SNR is changed, bit rate and bit error rate (BER) can be maintained by exploiting an adaptive modulation, instead of changing spectrum and route. Hence, there is a need for a joint spectrum decision and reconfiguration framework so as to find the optimal combination of the spectrum band and parameter configuration according to applications with diverse QoS requirements [1].

Spectrum decision over heterogeneous spectrum bands: Currently, certain spectrum bands are already assigned to different purposes while some bands remain unlicensed. Thus, the spectrum used by DSA networks will most likely be a combination of exclusively accessed spectrum and unlicensed spectrum. In case of licensed bands, the DSA users need to consider the activities of primary users in spectrum analysis and decision in order not to influence the primary user transmission. Conversely, in unlicensed bands, since all the DSA users have the same spectrum access rights, sophisticated spectrum sharing techniques are necessary. In order to decide the best spectrum band over this heterogeneous environment, DSA network should support spectrum decision operations on both the licensed and the unlicensed bands considering these different characteristics, [1].

Spectrum Mobility:

Spectrum mobility is the process when a DSA user changes its frequency of operations. The target of a user is to capture the best available spectrum or to operate in the best available frequency band.

Spectrum handoff:

In DSA Networks the user changes its frequency of operation for two reasons i.e. when the current channel conditions become worse or a primary user appears. So the spectrum mobility gives rise to the spectrum handoff.

In the spectrum mobility each time DSA user changes its frequency of operation the network protocols are going to shift from one mode of operation to another. The main goal is to make sure that the transitions are made smoothly and as soon as possible such that the applications running on a DSA Network perceive minimum performance degradation. .

Spectrum Mobility Challenges:

At a particular time, several frequency bands may be available, so good algorithms are required to decide the best available spectrum based on the channel characteristics of the available spectrum and the requirements of the applications used by DSA user.

The next challenge is to reduce delay and loss during spectrum handoff when a spectrum is chosen.

Novel algorithms are required for the ensured of less performance degradation during transition.

The spectrum handoff scheme should integrate inter-cell handoff and takes all the possibilities of spectrum handoff into consideration.

As the available channels change over time the quality of service maintenance is challenging.

If a user moves from a place to another, the continuous allocation of spectrum is a major challenge in DSA networks.

Spectrum Sharing:

In [11], spectrum sharing is defined as when two systems accessing the same spectrum the primary user in DSA network can share the same spectrum with a DSA user. One example is such a scenario that a cellular provider leasing its unused spectrum to a DSA user when cellular traffic is expected to be significantly lower .The spectrum sharing with primary user and selecting frequency from a wide range of available spectrum are the main reasons of different challenges in open spectrum usage.

The spectrum sharing process consists of five major steps [1].

Spectrum sensing: The solution and the challenges for the problem of spectrum sensing are discussed before in this report.

Spectrum allocation: The channel is allocated based on spectrum availability and on some policies. So an important research topic is the design of a spectrum allocation policy to improve the performance of a node.

Spectrum access: A major problem in spectrum access is the collision by multiple users in overlapping portion of the spectrum. To prevent this collision a solution to this problem is required.

Transmitter-receiver handshake: The transmission-receiver handshake protocol is important to inform the receiver of this communication when a portion of spectrum is determined.

Spectrum mobility: Spectrum mobility is very important for successful communication when a specified portion in use is required by a licensed user.

Spectrum sharing techniques:

The spectrum sharing techniques are classified and the fundamental results about these techniques in DSA networks are described here. The solutions for spectrum sharing in DSA networks are classified in three aspects [1] based on the architecture, access behavior and access technology.

Architectural Based Technique:

The first classification for spectrum sharing techniques in DSA Networks is based on the architecture, which can be described as follows:

Centralized spectrum sharing: The spectrum allocations and the access procedures are controlled by a centralized entity in these solutions [1,12]. Here a central entity in the DSA network constructs a spectrum allocation map after receiving the spectrum allocation measurements from each entity of the DSA network.

Distributed spectrum sharing: In cases where the construction of an infrastructure is not preferred then the proposed solutions are distributed solutions [11]. Here every node in the DSA network is responsible for the spectrum allocation.

Access Behavior based Technique:

The access behavior based technique, which can be cooperative or non-cooperative, is explained below.

Cooperative spectrum sharing: The effect of the communication of a node on other nodes is the main consideration of these solution techniques [1]. The interference measurements of each node are shared among other nodes.

Non-cooperative spectrum sharing: Only the node at hand is the main consideration of non-cooperative solution also known as selfish solution [11]. This selfish solution may reduce the utilization of spectrum.

The simulations show that cooperative approaches outperform non-cooperative approaches as well as closely approximating the global optimum [1].

Spectrum Access Technique:

The spectrum access technique is based on the technology and is explained below.

Overlay spectrum sharing: In this technique the available portion of the spectrum which is not used by primary users is accessed by a DSA user which minimizes the interference to the primary system [13].

Underlay spectrum sharing: This technique acquires a spectrum allocation map and then selects portions of a spectrum. A DSA node transmits power at that portion of a spectrum which is regarded as noise by the licensed user. This technique, when compared to overlay technique, can use more bandwidth [1].

Existing Spectrum Sharing Technique:

Inter-network spectrum sharing and intra-network spectrum sharing are the existing spectrum techniques which are the combination of the three classifications we have discussed above.

Inter-network spectrum Sharing

Some unique challenges in DSA networks are posed in inter-network spectrum sharing.

Centralized Inter-Network Spectrum Sharing

In [13], the Common Spectrum Coordination (CSCC) protocol is proposed for co-existence of IEEE 802.11b and 802.16a networks. This protocol necessitates modification in using both the networks therefore we do not consider this as a complete solution. The assumption in this is that each node is equipped with a cognitive radio, and a low-bit rate, narrow band control radio. Then each node broadcasts the CSCC message with other node and so the co-existence is maintained.

Distributed Inter-Network Spectrum Sharing

D-QDCR (Distributed QoS based dynamic spectrum reservation) is a scheme in which a BS with high priority allocates a spectrum to its user according to the requirements of the user. Here a basic unit Q-frame is allocated by the BS and then it uses control and data channels, allocated to it, for the coordination and data communication between the users. The D-QDCR scheme serves important contribution in distributed inter-network spectrum sharing [1]. The distributed inter-network spectrum sharing provides broader view of spectrum sharing solution including operator policies for determination of spectrum allocation. A major problem is the requirement for a common control channel for the existing solution.

Intra-network Spectrum Sharing

Here the users of a DSA network try to access the available spectrum without interfering with the primary user.

Cooperative Intra-network Spectrum Sharing

In [14], a cooperative local bargaining approach is proposed in which the effected users by mobility event self organize into bargaining groups and adapt their spectrum assignment to approximate a new optimal assignment. Experimental results demonstrate that the proposed bargaining approach provides similar performance as other approaches but with more than 50% of reduction in complexity. In [15], a distributed coordination scheme is proposed which also considers local groups. In this scheme it is shown that very limited number of common channels exists for each of the user while in cooperative local bargaining scheme the common channel may not exist in DSA networks or can be occupied by primary user.

Non-cooperative intra-network spectrum sharing

A device-centric spectrum management scheme (DCSM) is proposed in [16] where users act independently according to preset spectrum rules when local interference patterns are observed. Here five rules are provided that tradeoff performance with implementation complexity and communication costs. Experimental results show that the proposed rule-based approach reduces communication costs from efficient collaborative approaches by a factor of 3-4 while providing good performance [16].

Spectrum Sharing Challenges:

There are many open research issues for the seamless open spectrum operation. In [1], some challenges in spectrum sharing in DSA networks along with some possible solutions are described which are.

Common Control Channel:

CCC facilitates transmitter receiver handshake communication [13] and much other functionality. If a DSA user uses a channel and when a primary user chooses that channel, then the channel has to be vacated without interfering. This is also true for the CCC. So it is infeasible if fixed CCC is implemented [1] and when CCC is not used, the transmitter receiver handshake becomes a challenge. For this challenge, receiver driven techniques may be exploited.

Dynamic Radio Range:

In DSA networks, the neighbors of a node may change as the operating frequency changes where a large portion of the wireless spectrum is considered [1],. The routing decisions are effected by this change. And due to this property, the choice of a control channel needs to be carefully decided.

Upper layer issues:

In DSA networks there are also some upper layer issues related to the network layer and flow and congestion control. Here some challenges are given which are related to these areas.

Routing challenges:

In DSA networks efficient spectrum allocation is a challenging problem, particularly for multi- hops transmissions so novel routing algorithms are required. There are two types of existing solutions based on inter-dependence between route selection and spectrum management. In [18], these two design methodologies are investigated which are a decoupled design where these tasks are carried out independently, and a collaborative design that integrates them into a single task. Experimental results show that the collaborative design offers significant performance improvement compared to the decoupled design [18].

The remaining major challenges and open research issues in DSA networks are summarized below:

Common control channel: As we have discussed that the DSA network is lack of a common control channel (CCC) which is a major problem since the discovery of neighbors or route and the route establishment is done by broadcast messages in traditional routing protocols which will be very difficult due to the lack of a CCC [1]. Hence, solutions are required for this problem in DSA networks.

Intermittent connectivity: In DSA networks the available channel can change with the appearance of a primary user in that channel. So models are required for this purpose. Such a channel based model is proposed in [18] but still more models are required for the connectivity purpose in DSA networks [1].

Re-routing: In DSA networks an established route of a DSA user changes as the availability of spectrum or due to the mobility of the user. Hence re-routing algorithms for routing in DSA networks are required [1].

Transport Layer Challenges:

In DSA networks, due to the wireless link errors and access delays the TCP and UDP protocols are affected. Some solutions have been proposed in [19] to reduce the performance degradation of these protocols but still there are some major issues and problems in the transport layer [1].As we know that TCP depends on round trip time (RTT) and on the packet loss probability which depends on the frequency in use and on the available bandwidth. And as the frequency and bandwidth may change rapidly so the existing TCP and UDP protocols cannot be used. Therefore novel protocols are required.

8. Conclusion:

DSA networks are envisaged to solve the problem of spectrum scarcity by making efficient and opportunistic use of frequencies reserved for the use of licensed users of the bands. To realize the goals of truly ubiquitous spectrum-aware communication, the CR devices need to incorporate the spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility functionalities We investigate the unique challenges in DSA Networks by a bottom-up approach, starting from the capabilities of cognitive radio techniques to the communication protocols that need to be developed for efficient communication. The discussions provided in this survey strongly advocate spectrum-aware communication protocols that consider the spectrum management functionalities.