IEEE Trans actio nso n Wireles sCo mmu nicatio ns
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Multihop Multi-band Cognitive Architecture for Next Generation Cellular Networks Hrishikesh Venkataraman 1, Vikrant Tomar2 , Gabriel-Miro Muntean1 1
Performance Engineering Laboratory, School of Electronic Engineering, Dublin City University, Ireland 2 Indian Institute of Technology (IIT), Bombay, India Email:
[email protected],
[email protected]
Abstract—Lately, due to to the tremendous success of mobile communications, the cellular band is very crowded while a large portion of the electromagnetic spectrum assigned for other services is used sporadically. Cognitive Radio is seen as a new communication paradigm to exploit the existing radio spectrum opportunistically to improve the utilization of the radio spectrum. In this paper, a novel multihop multi-band cognitive radio architecture for cellular network is proposed which makes use of a number of cognitive gateways in order to enable simultaneous usage of spectrum resources within the same cell. It is mathematically demonstrated that the carrier-to-interference ratio is maximized when there are six cognitive gateways placed equidistantly from each other. This two-hop cognitive architecture provides a significant increase in the system capacity and exponentially decreases the resource allocation delay, as compared to single-hop and a standard two-hop routing technique.
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Index Terms—Frequency reuse, cognitive gateways, cognitive radio, multi-band, multihop, spectrum sensing
I. I NTRODUCTION
Over the last few years, wireless communication systems have experienced a rapid evolution. Now-a-days, mobile phones are widely used for various applications, and have become much more sophisticated. However, the demand for more bandwidth and higher data rate is increasing at a phenomenal rate. It is anticipated that next Generation (xG) cellular networks would support richer and more diverse multimedia services, such as VoIP, mobile Internet, video conferencing and mobile gaming, all of which require increased data rates, even up to 1 Gbps [1]. However, the current cellular networks, such as Global System for Mobile/Wideband Code-Division Multiple Access (GSM/WCDMA) and WiMAX (Wireless interoperability for Microwave Access) networks, can support only up to 100 Mbps data rate. In addition, they may not be economically feasible to cater to the higher data-rates and strict quality of service (QoS) requirements of future mobile communication services. There are increasing interests on investigating xG cellular network structures for higher data rates and increased coverage and scalability [2]. Furthermore, it is questionable whether the xG systems would be able to cope with the increasing spectrum demands even with their evolved transmission techniques. In order to satisfy the growing demands, we need to conceive new and more efficient ways to utilize the available limited spectral resources [3].
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IEEE Trans actio nso n Wireles sCo mmu nicatio ns
Fig. 1.
Next generation multihop cellular network with cognitive gateways
The radio electromagnetic spectrum is regulated by government bodies and is assigned to license holders or services on a long term basis for large geographical regions. In November 2002, the Federal Communications Commission (FCC), US published a report prepared by the Spectrum Policy Task Force (SPTF) aimed at improving the way in which the radio electromagnetic spectrum is managed in US [4]. This report noted that some frequency bands in the radio spectrum, especially the mobile bands, are heavily occupied most of the time, whereas, other frequency bands are only partially occupied or largely unoccupied (e.g., the TV bands). These fragmented frequency bands which are not used by the licensed user at any given time and a specific geographic location are known as spectrum holes [5]. Spectrum utilization can be improved by enabling secondary users to access these spectrum holes, unoccupied by the primary user. Cognitive Radio (CR) has been proposed as means to promote the efficient use of the spectrum by exploiting the existence of spectrum holes. The CR principle is to utilize opportunistically various spectrum holes eventually hopping from one to another, so as not to effect the primary user activity [6], [7].
IEEE Trans actio nso n Wireles sCo mmu nicatio ns
Fig. 1 shows a possible and most probable next generation cognitive radio based cellular network architecture. It consists of the base station (BS) at the center of the cell, the mobile stations (MSs) and the relays that not only serve as repeaters/access-points, but also act as a cognitive device capable of sensing the available spectrum in the environment. All the wireless devices in the network communicate with other devices in its neighborhood, and also with the relays/cognitive gateways (CGs). The CG bridges an important gap between the BS and the different wireless users in the network. Cognitive radio is a new paradigm in the area of wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently without interfering with licensed users. This paper presents a new multihop multi-band cognitive architecture (MMC) which enhances the spectral efficiency and the overall performance of the existing cellular networks by making use of a number of CGs. The novelty of the MMC architecture lies in an efficient design for spectrum sensing and spectrum sharing in cellular networks. A detailed mathematical analysis is carried out in order to determine the number of cognitive gateways (CGs) that would maximize the carrier to interference ratio (C/I) of the communicating receivers. In addition, the time required for resource allocation and the Shannon capacity of the MMC-based cellular network is compared against a single-hop cellular design.
However, energy detector has its own drawbacks [8]. Firstly, frequency detection using energy detector is highly susceptible to the changing noise level. Secondly, it does not differentiate between modulated signal, noise and interference. Hence, it cannot benefit from adaptive signal processing for canceling the interferer. Finally, an energy detector does not work for spread spectrum signals. Hence, the third method, the cyclostationary solution is the preferred spectrum sensing technique [9]. Recently, there have been improvements to the classic cyclostationary detection to sense spectrum holes [10]. In addition, Wild and Ramchandran [11] suggested a local oscillator leakage power method for detecting and sensing the available spectrum, whereas, Sahai et.al [12] have showed that the presence of known pilot signal enables detection of weak signals of primary and secondary users. Kim et al. [13] proposed an OFDM based system architecture for CR networks which uses adaptive traffic allocation in time and frequency to minimize interference with the primary users. Another interference avoidance scheme for OFDM based cognitive radios has been proposed in [14] by Zhou et al., wherein, they have proposed an interference avoidance algorithm for OFDM based cognitive radio on reconfigurable architecture. The signaling and spectrum synchronization issues for spectrum pooling systems have been addressed by Weiss and Jondral in [15].
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Section II discusses the motivation and the related work. Section III describes the proposed MMC system architecture and elaborates on the functionality of the proposed CR-based radio communication system. Section IV discusses the performance evaluation of the MMC system stand-alone and in comparison with the existing two-hop network and a single-hop network. Finally, Section V concludes the paper. II. R ELATED W ORK
In this context, there are still two major problems that hinder the development of new more efficient CR-based wireless systems. Firstly, the demand of adopting an entirely new architecture would result in overhauling the entire network design, which in turn would require huge investments. Hence, this is not feasible from both practical and financial reasons [16]. Secondly, there is a need to process multi gigahertz wide bandwidth and reliably detect the presence of primary users over a wide-band, and this has not yet been achieved [17]. These have been the major drawbacks in the practical implementation of CR-based solutions.
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A. Cognitive Radio - CR Over the recent years, there have been significant developments in the area of enhancing spectrum efficiency. Researchers have proposed different solutions but it seems that CR-based solutions are some of the most promising ones. However, they are relatively complex as they involve spectrum sensing, spectrum sharing and spectrum management. Additionally, there are issues such as interference avoidance, synchronization, traffic allocation, frequency shifting, synchronization, etc [8]. In terms of spectrum sensing, there are three different methods for signal detection. The most common is the matched filter technique, which is an optimal way for any signal detection. However, its main drawback is that it requires an effective demodulation of a signal, and hence, requires apriori knowledge of the signal at both PHY and MAC layers. A second method is based on energy detection, basically a non-coherent detection method.
In order to over come these challenges, this paper proposes to enhance the existing cellular network architecture with CR, multihop and multiband features. The newly obtained MMC architecture includes a multi-band network with several small-size local oscillator in the cognitive nodes which also act as relays for multihop cellular transmissions. There have been some significant work in the area of ‘multihop’ and multi-band’ independently over the past decade or so. B. Multihop and Relays Multihop cellular networks have been an active area of interest in the recent years. The multihop cellular architecture offers many additional advantages, such as increased network throughput, scalability and coverage [18], [19]. To maintain a specific link quality, the required transmit power of the
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communicating link should increase with an increase in the data rate. Providing very high data rate services in the future will either require high transmission power or shorter transmission distance between the communicating links. One simple way to push high data rate with limited bandwidth is to increase the density of cells/ BSs (i.e., smaller cells and more number of BSs would satisfy the increasing data rate demand). However, increasing the number of BSs increases the system cost substantially [20]. A multihop system solves these power limitations using relays without demanding any significant change in the existing infrastructure [21], which in turn does not increase the system cost much. In addition, applying relaying in an infrastructure-based network does not need complicated distributed routing algorithms as compared to ad hoc networks, while at the same time retaining the flexibility of being able to move the relays, as the traffic patterns change over time. A detailed overview of such systems can be found in [22].
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C. Multi-band Wireless Networks
Currently, there are tri-band and quad-band mobile phones in the market [23], [24]. However, if we consider the next generation networks, designing solutions which scan the entire width of the spectrum (for example, from 800 MHz to 2.4 GHz) is not simple [25]. In addition, the size and power requirements of the local oscillator increase with an increase in the oscillating frequency range making it unsuitable for portable mobile devices.
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The problem of processing multi gigahertz wide bandwidth can be addressed by dividing the entire band into a number of smaller bands distributed discretely in the entire spectrum. Each of the sub-bands could have equal width. For example, if 25 MHz sub-bands for either uplink or downlink are considered, and the total spectrum of operation to range from 800 MHz to 2.4 GHz, then the number of sub-bands to be used for communication, including uplink and downlink, is up to 32. Considering the rapidly evolving VLSI and ULSI technology, and the shrinking size of the chips and transistors, accommodating several small local oscillators on a next generation mobile device, each of which scans a small narrowband spectrum seems to be very much achievable. III. MMC N ETWORK A RCHITECTURE A. Architecture Overview
A MMC architecture is composed of BSs, CGs and MSs. Fig. 2 illustrates the overall MMC based system, composed of a number of hexagonal cells. Each hexagonal cell in the cellular network is divided into two regions: the inner region and the outer region. In the inner region, the BS communicates with the MS directly if there is a resource available, whereas in the outer layer, the BS always communicates with the MS through the CG. Each hexagonal cell has an edge length, r, and the inner region has a radius, τ r, where 0 ≤ τ ≤ 1. The
Fig. 2.
Architecture of the proposed MMC system
CG’s are equidistantly placed and are located at a distance of τ r from the BS.
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IEEE Trans actio nso n Wireles sCo mmu nicatio ns
A CG has two fold functionality: 1) It acts as a cognitive radio and senses the spectrum holes, and 2) It serves as a fixed relay, i.e., receives and passes on the signal to and from the BS, neighboring CG’s or the mobile receivers, as shown in Fig. 3.
In principle, the CG could be placed anywhere, including at the BS itself, but this might not be always efficient. This is because, the CG takes spectrum from different sources like TV station, radio station, other cellular operators, etc. Hence, in order to efficiently sense these frequencies and use them as secondary spectrum, the CG should not be too close to the BS of its primary spectrum. Secondly, having a CG at the BS of a multihop cellular network results in an additional infrastructure requirement of having both a CG and a relay node separately. Even if no multihop transmission is considered, having a CG at the BS itself would imply that the receiver sensitivity of the CGs have to be much higher due to the increased transmission distance between the mobile node and the CG. In addition, if there is no multihop, the CG would not have the flexibility of assigning same frequency resource to different users in the adjacent cells.
IEEE Trans actio nso n Wireles sCo mmu nicatio ns
mobile would be a multi-band terminal, and would be able to switch between the different bands as and when signaled by the CG. With advancement in digital signal processing technology, multi-band switching is not only feasible but also a very realistic solution to wards realization of next generation wireless devices. The different functionality of the MMC system architecture that enables the sharing of electromagnetic radio spectrum by means of cognitive radio, is explained in the next section.
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Fig. 3. An example of multiple hop communication through relay CG’s inter cell multihop communication
Fig. 3 shows an inter-cell multihop communication, where the CG acts as a relay between BS and MS. In Fig. 3, the two wireless devices located in the adjacent cells communicate with each other, wherein, the CGs not only act as relays, but also senses the available spectrum and alters the frequency band across each communicating pair. The CGs can be placed on fixed and out-of-public-reach places, for example, lamp posts and roof tops. The signal reaches from the BS to the MS through two or more hops via CGs, depending on the locations of BS and MS. In order to maximize the number of multihop transmissions, the two CGs are located diametrically opposite with respect to the BS. It should be noted that the number of CGs in a cell could be increased beyond two. This would be especially beneficial in case of high traffic load in the system, where equidistant CGs would distribute the traffic load among themselves in an almost equal manner. This in-turn would result in reduced power requirement and time required for resource allocation. However, it should be noted that increasing the number of CGs also increases the system cost.
In order to maximize the system capacity, it is required to find the number of CGs required in each cell and the exact location where these CGs need to be placed in order to maximize the system capacity. Significantly, it should be realized that in order to maximize the system capacity, the CGs should be placed at such a location within the circumference region of 2πτ r so that it receives minimum interference from all other simultaneously communicating transmitters. This translates into maximum possible carrier-to-interference ratio (C/I), experienced at the receiver which in turn results in a maximum capacity of the particular communicating pair. B. Optimum Number of CGs In a multi-cell scenario, with a single communicating pair in every cell, there would be as many interferers as the number of transmitting BSs in the adjacent cells. Hence, in case of a hexagonal representation of cells as illustrated in Fig. 2, there would be 7 simultaneously communicating pairs (one in the center cell + six in the adjacent cells); so n t =7. For any communicating pair, if P T is the transmitting power and dc the transmission distance of the desired communicating pair, then the received power, P R is given by eqn. (1) [26]:
Any local oscillator in the CG senses the spectrum in the aforementioned finite bands and not the entire width of the spectrum, and changes the transmitting/receiving frequency to one of the free bands as and when required. The resulting change in the frequency band is indicated by the CG to the wireless node, prior to the transmission of next data packet. The multi-band model is independent of the multiplexing technique, and hence, would work equally well with both time division duplexing (TDD) and frequency division duplexing (FDD) mode. The end user mobile devices would not be CR enabled, and hence, cannot sense the spectrum. This reduces the cost of the the end-user device. At the same time, the
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PR = PT − (k1 + 10α log10 (dc ))
(1)
where k1 is the propagation constant and α is the path-loss exponent. If there are n t simultaneously communicating pairs in the network that utilize the same radio resource at any time instant, t, then the receiver in each of the n t communicating pairs would experience interference from n t − 1 transmitters. Fig. 4 shows the distance of the interfering transmitter from the neighboring cell to the center cell. Hence, the distance of the interferer from the i th cell, dinti , could be therefore written as: !√ √ ( 3r cos θi − τ r cos φ)2 + ( 3r sin θi − τ r sin φ)2 (2) where θi is the angle between the line joining the BSs and the reference line, as shown in Fig. 4, and φ is the angle made by the line joining the BS → CG communicating pair with the reference line. Hence, the C/I experienced by the receiver of a communicating pair would be given by: dinti =
(dc )−α C/I = "nt −1 −α i=1 dinti
(3)
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IEEE Trans actio nso n Wireles sCo mmu nicatio ns 300 , 900 , 1500 , 2100 , 2700 , 3300
Values of θ
TABLE I D IFFERENT VALUES OF θ
values of φ where
00 , 300 , 600 , 900 , 1200 , 1500 , 1800
Derivative of C/I equals zero
2100 , 2400 , 2700 , 3000 , 3300 and 3600
TABLE II D IFFERENT VALUES OF φ WHERE THE DERIVATIVE OF C/I EQUATES TO ZERO
d (C/I) dφ2 2
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+
Fig. 4. Calculation of θ and φ between the communicating pairs in the adjacent cells
where i& {1,... n t }. The Shannon capacity of the network at any time instant expressed in bps/Hz would therefore be given by: Nt #
log2 (1 + C/I)bps/Hz
(4)
1
The Shannon capacity calculated per Hertz is dependent only on the C/I as is evident from eqn. (4). Hence, the behavior of Shannon capacity is same as that of C/I. For a given transmission distance, the C/I depends on the interfering distances, which in-turn depends on the different θ i ’s and φ’s. Hence, the next step is to find the angle, θ and φ, in when the C/I, and thereby, the Shannon capacity would be maximized. The maximum and minimum values of C/I can be obtained by differentiating C/I with respect to φ. d(C/I) √ = 3ατ (1−α) r(2−α) dφ
"nt −1 i=1
&
k k1
'" nt −1
(2 sin(φ − θi ) (3 nt −1 i=1 dinti
−(α+2)
i=1 dinti '"
"nt −1
dint−(α+4) sin2 (φ−θi ) i '" (2 nt −1 d int i i=1 ) "nt −1 −(α+2) cos(φ − θi ) i=1 dinti '" (2 nt −1 d int i i=1
k2
i=1
√ where, k = 3ατ 1−α r2−α , k1 √ 2 k2 = (α + 2) 3τ r .
=
(6) √ 2 3ατ r2 and
Substituting the different values of φ, where the first derivative is zero, into eqn. (6), the maximum value of C/I is obtained at φ = 00 , 600 , 1200 , 1800, 2400 , 3000 and minimum values of C/I at φ = 300 , 900 , 1500 , 2100 , 2700, 3300 .
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It can be observed from the above mathematical analysis that irrespective of the length of the BS → CG link (i.e., irrespective of the value of factor, τ ), the C/I, and hence, the Shannon capacity attains a maximum and minimum value at six equidistant locations respectively, and the location where the maximum and minimum capacity is attained changes alternately with 30O . This provides an extremely significant result:
−(α+2)
sin(φ − θi ) (5) %2 nt −1 −α d inti i=1
dinti $"
On equating d(C/I) = 0, different values of φ are obtained dφ for which C/I is either a maxima or minima. If the coverage area of a cell is represented as an hexagon, as is normally done, each cell would be surrounded by six adjacent cells. Hence, the different values of θ and the values of φ where the derivative of C/I equates to zero are given in Table I and Table II respectively: On taking the second derivative of C/I, eqn. (6) is obtained:
1) In a two-hop multi-cellular network with single simultaneously communicating pair in every cell, the system capacity is maximized when there are six equidistant CGs located at equal distance from the BS. These six CGs are separated from each other by an angle of 60 O . 2) The minimum C/I in the cell is attained at mid-angular point between the two CGs, i.e., at an angle of 30 O from the CG.
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Variation of C/I within a Cell in a Multihop Cellular Architecture 12.35
Carrier to Interference Ratio (C/I)
12.34
12.33
12.32
12.31
12.3
12.29
0
30
60
90
120
150 180 210 Phase (Degree)
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300
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360
Fig. 5. Carrier-to-Interference ratio variation in the center cell with angle φ when there is one communicating pair in every cell and the position of BS → CG communication pair is varied over the center cell
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Variation of Shannon Capacity over a Single Cell 4.185 4.1825
Capacity (bps/Hz)
4.18 4.1775 4.175 4.1725 4.17
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30
60
90
120 150 180 210 240 270 300 330 360 Phase (Degree)
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Fig. 7. Multi-cellular architecture with all CGs in the cell located at equidistant distance from the BS
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Fig. 6. Shannon capacity variation in the center cell with angle φ when there is one communicating pair in every cell and the position of BS → CG communication pair is varied over the center cell
In order to illustrate the mathematical results, Fig. 5 and Fig. 6 show the variation of C/I and Shannon capacity when a single pair is communicating in every cell using a given radio resource. It can be observed from Fig. 5 that there are six distinct points where the C/I attains its maximum and minimum values respectively. Also, the maximum and minimum values are not only alternately located but also equally separated by 300 . The variation in Shannon capacity of the wireless architecture also follows the same pattern, as can be seen from Fig. 6. Correlating Fig. 5 and Fig. 4, it can be observed that irrespective of the value of τ , the minimum C/I, and hence, the minimum capacity is at the line that joins the central BS with the BSs of the adjacent cells (θ = 30 0 , 900 , 1500 , 2100 , 2700, 3300 ). Hence, the MMC architecture, employs six CGs equidistantly located. Additionally, in each of the cells, all the CGs are equidistant from the BS.
Fig. 8.
One sector of a hexagonal cell
C. Location of CGs in MMC Each CG in the MMC architecture serves as a relay to the MSs located within a certain region. This region could be denoted by the term - constellation. A typical constellation in the cellular network is elliptical in shape, as shown in Fig. 7. The MSs in each of the constellations communicates through
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the CG. Also, as can be seen from Fig. 7, there is a small area between two elliptical clusters. The MSs in these areas are assigned to one of the clusters, and hence, these MSs would communicate with the BS through the corresponding constellation-head CGs. However, in order to simplify the mathematical analysis, the cluster is still approximated by an ellipse. As can be seen from Fig. 8, every point in the constellation region is uniquely determined by the vector: ( ' β a cos(θ0 )ˆi + b sin(θ0 )ˆj , where 0 ≤ θ0 ≤ 2π is the angle measured from the center of the ellipse to the position of the MS. By varying the value of β from 0 to 1, the whole area of the ellipse is spanned, with b and a being the semi-major axis and semi-minor axis of the ellipse, respectively. 1) Calculation of a & b: Consider a sector of the cell as shown in Fig. 8. For the ease of mathematical analysis, the two edges of the sector are expanded to form an equilateral triangle. √ Hence, the edge length of the equilateral triangle is 2r/ 3. The height of the triangle is of length, r. The radius of the inner layer circle is τ r. As the value of τ changes, the value of a and b also changes. The center of the ellipse is taken as the origin. Fig. 8 shows the elliptical cluster and the distance of the BS from the reference point. The radius of the inner region and the edge of the cell are related by the semi-minor axis of the ellipse by the expression: τ r + 2a = r. Therefore, a = r2 (1 − τ ). From Fig. 8, it can be√figured out that the slope of the tangent to the ellipse is − 3. Hence, by equating the tangent slope of the ellipse to √ 3, eqn. 7 is obtained: * 2+* + √ a x (7) =− 3 2 b y
In the outer region, the farthest point from the CG has θ 0 = π/3 and therefore its distance from CG, d max , is given by r, 2 dmax = 4τ − 6τ + 3 (12) 2 Therefore, the transmitter power of CG is: PTCG = s (dmax )α
(13)
and the transmit power ratio between BS and CG is given by: +α * PTBS 2τ = √ (14) PTCG 4τ 2 − 6τ + 3
In this paper, it is assumed that the transmit power of the BS is 5 times that of CG, i.e., PTBS = 5 × PTCG . This is done in order to maintain a uniformity while carrying out the simulations. Substituting this in eqn. (14), the value of τ obtained is 0.745. Substituting the value of τ in eqn. (12), the maximum transmission distance from CG to the mobile node at the edge of the cell is d max = 0.26r. Also, the semi-major and semi-minor axes of the ellipse is given by: a = 0.13r and b = 0.21r. Hence, the diametrically opposite CGs are separated by 1.5 times the cell edge length, r. Therefore, the resulting interference arising due to the simultaneous use of the spectrum resource by the diametrically opposite CGs would be minimal. It should be noted that changing the ratio of BS to CG transmit power would only change the location of CGs from the BS and the maximum transmission distance of the communicating pairs. However, the functionality of the MMC architecture and the associated communication mechanism remain the same, as explained in the next section.
Substituting for x in the above equation, one gets:
and
a2 y=√ 3b2 + a2
(8)
√ 2 3b x= √ 2 3b + a2
(9)
Substituting x, y√ and m in the tangent equation y = mx + c, c is given by c = a2 + 3b2 . Also, ( √r3 , −a) passes through the tangent equation. Therefore, substituting it, one gets: √ r τ b= √ 3
(10)
2) Calculation of CG Power with respect to BS Power: The minimum receiver sensitivity (minimum carrier power) for any MS in the cell in order to correctly decode the data is taken as S dB (which corresponds to s Watts). Hence, the farthest point both in the inner region and in the outer region should have this sensitivity level. In the inner region, the farthest point is at a distance τ r from BS. Therefore the transmit power of the BS is expressed as: PTBS = s (τ r )
α
(11)
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IEEE Trans actio nso n Wireles sCo mmu nicatio ns
D. Functionality of the MMC Architecture-Based System A downlink transmission is considered in order to explain the functionality of the MMC system. The BS communicates with all the CGs in its cell by broadcasting a start signal. On receiving the start signal, all CGs start to sense the spectrum for vacant spectrum bands (i.e., spectrum holes). The CG nearest to the BS sends the sensed spectrum data to the BS/wireless node informing it about the available bands to communicate. The range of transmission of a CG is limited to the first tier only and it acts as a relay between: 1) BS and the mobile user or another CG, 2) Two CGs, or 3) Another CG and the mobile receiver. Each CG maintains a table, referred to as spectrum table, based on such shared information. As soon as a CG senses the primary user in a vacant band, it adds this band in its table and broadcasts this information to its first tier neighbor CGs. If a CG/any wireless node is transmitting and the primary user of that band comes in, then the CG would sense the primary user and immediately stop transmitting (or ask the wireless node to stop transmitting) in that band, and switch to the nearest vacant band. It will broadcast a message or frequency
A CG cannot communicate (neither transmit nor receive) within the bands used by the nearby users of the same cell, but it can use the bands of the users that are from the same cell, but distinctly far off from creating significant interference. As shown in Fig. 7, the CGs, X1 and X2 are located at diametrically opposite sides of the BS. In the downlink, the BS can transmit to CG X1 in its constellation in a particular time instant using a given spectrum resource. At the same time, CG X2 can transmit to MSs in the opposite constellation. Similarly, in the next time instant, BS → CG X2 and CG X1 → MS communication could take place using the same spectrum resource.
Time Taken v/s No. of Bands for Cognitive Cellular Architecture 600 20 nodes per cell (MMC−based two−hop) 20 nodes per cell (LLH two−hop) 20 nodes per cell (single−hop) 50 nodes per cell (MMC−based two−hop) 50 nodes per cell (LLH two−hop) 50 nodes per cell (single−hop)
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0
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IV. P ERFORMANCE E VALUATION OF THE MMC S YSTEM
In the MMC architecture, the CG detects the available frequency resource in its vicinity and allocates it to different communicating links, for which the CG would need some definite time interval. Hence, in a multiband cognitive network, there would be some delay in establishing communication
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Time Taken v/s No. of Bands for Cognitive Cellular Architecture 6000 100 nodes per cell (MMC−based two−hop) 250 nodes per cell (MMC−based two−hop) 100 nodes per cell (LLH two−hop) 250 nodes per cell (LLH two−hop)
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A multihop cellular network is simulated in Matlab, with communication between the BS and the MSs in its own cell taking place in a maximum of two hops. 1000 MSs are assumed to be uniformly distributed in 19 cells within a coverage area of 1 km 2 . The MMC architecture described in Section III is considered in the system deployment. There are six CGs in every cell that are equidistant from each other and from the BS. The MSs located further from the CG in the cell communicates with the BS through the CG in two hops. Prominently, the CGs act as both relays and multiband cognitive radios. The CG considers different unutilized frequency bands (both from the cellular network and other secondary bands) for allocating frequency to the communicating links. The frequency of operation of different communicating links is determined by the CG depending on the available frequency resource in the multi-cellular network. The number of frequency bands in the network is varied from 1 to 9. As it is assumed that each band supports one channel, the number of channels available in the system also varies from 1 to 9.
4
Fig. 9. Variation of time delay with the number of frequency bands when the number of communicating nodes per cell is less
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In case of uplink transmission, the transmitters and receivers are reversed, but the governing principle of the spectrum reuse technique remains the same. Thus, a better and a dynamically varying frequency reuse is obtained using the multihop multiband architecture as compared to a traditional cellular network.
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tone to mark its presence in the band that it is going to communicate. The CGs would continue to sense the spectrum in a real-time system and the updated table are synchronized periodically after every small finite interval. It should be noted that it is possible to have simultaneous transmission by both BS and CGs. Hence, the capacity gains would be achieved by exploiting spatial diversity which in turn increases the spectrum reuse efficiency.
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Fig. 10. Variation of time delay with the number of frequency bands when the number of communicating nodes per cell is high
across the communicating links in the system. If the number of channels (unutilized bands) is low, then the multiband cognitive architecture would require more time as the same number of channels have to be reused by many communicating pairs. However, as the number of channels that could be detected and assigned is increased, the CG has additional resources to allocate to the multhop links which in turn reduces the time required for all the links in the system to communicate. In addition, due to the division of the outer-layer into six constellations, it is possible to have simultaneous communication of diametrically opposite CGs which in-turn results in potentially higher system capacity as compared to a simple single-hop cellular design.
Variation of System Capacity for Downlink Scenario 1.5
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Fig. 9 shows an exponential decrease in the time required for both one-hop and two-hop transmission when the number of channels that could be assigned by the CG increases. The time required in the MMC architecture is due to the time taken by the CG to allocate the resources, and by the wireless node to get the assigned frequency resource. In a MMC based two-hop transmission, the time required is 8.72 seconds when there are 20 nodes per cell and there are only 4 available channels in the system. However, when the number of channels is increased to 8, the total time taken reduces to 1.12 seconds. Hence, the time required for resource allocation reduces exponentially with an increase in the number of channels. In addition, it can be seen from Fig. 9 that the time required for a multihop multiband cognitive architecture is much lower than that of a single-hop cellular network or a least longest hop (LLH)-based two-hop cellular network. For example, for 50 nodes/cell with 8 channels/cell, the time taken for MMC based architecture is 2.62 seconds, whereas the time required in case of single-hop and LLH-based two-hop network is 55 seconds and 35 seconds respectively - a reduction of a factor of 13 as compared to the LLH routing method and a factor of 21 times as compared to the single-hop design. It should be noted that this time reduction in the LLH and MMC two-hop design do not take into account the marginal increase due to the additional overhead in a two-hop network. However, the overhead delay is usually in the order of few milliseconds. Hence, it is expected that, even after considering overhead, the time taken by the two-hop design would be much less than the single-hop network model.
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Fig. 11. Cumulative distribution function (cdf) of system capacity in the downlink of a multi-cellular network Variation of System Capacity for Uplink Scenario 1.5 Single−hop design Least longest hop (LLH) two−hop selection scheme MMC based two−hop selection scheme
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Another aspect of the MMC architecture is that it is scalable with the increase in the number of nodes in the cellular network. This is very much unlike the simple multihop architecture that has been researched in the recent years [27], wherein, it is been showed that in the absence of a cognitive or a multiband design, the existing scheduling techniques are unscalable, and also, the time delay rises beyond the acceptable margin. However, Fig. 10 shows that the MMC architecture is scalable even when the number of communicating nodes in each cell is increased beyond 100. As the number of communicating nodes are increased, a high number of unutilized resources are required (typically 9 channels for 100-250 nodes per cell) in order to ensure that the end to end delay does not exceed 5-6 seconds. It can be observed that when the number of frequency resource is increased, the time required for the multiband cognitive network decreases at a much faster rate for higher number of nodes than when the number of communicating nodes are less, i.e., the rate of time decrease is faster in Fig. 10 as compared to the one in Fig. 9. This is a very important result. It signifies that in order to reduce the total delay, the CG should have certain number of unutilized frequency resources which it can assign to the communicating nodes, in order to ensure that the time taken for resource assignment is within few seconds. Additionally, the two-hop multiband architecture is compared with the single-hop design and with the LLH routing model designed specifically for two-hop cellular
Single−hop design Least longest hop (LLH) two−hop selection scheme MMC−based two−hop selection scheme
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Fig. 12. Cumulative distribution function (cdf) of system capacity in the uplink of a multi-cellular network
network [28]. Fig. 11 and Fig. 12 shows the simulation results of the cdf (cumulative distribution function) of the system capacity for downlink and uplink scenarios respectively. It is observed in Fig. 11 that in case of downlink transmission, the median of the system capacity is 2.5 times that obtained from the single-hop cellular network with no CG. Similarly, the expected value of the MMC design is 2.25 bps/Hz/cell, which is more than twice obtained from the single-hop cellular network value of 1.12 bps/Hz/cell. Significantly, the system capacity of the MMC design is superior by 0.24 bps/Hz/cell to the LLH method, whose expected value of the system capacity is 2.01 bps/Hz/cell. Similarly, in case of uplink as observed in Fig. 12, the system capacity of MMC design (1.82 bps/Hz/cell) is superior to the LLH method (1.36 bps/.Hz/cell) by 0.46 bps/Hz/cell. The significant improvement in the system capacity observed in the MMC design technique is due to the synchronized resource reuse scheme achievable through
IEEE Trans actio nso n Wireles sCo mmu nicatio ns the presence of six dedicated CGs, as compared to the LLH method, where there is no such dedicated scheme for effective reuse of spectrum resources. V. C ONCLUSIONS AND S UMMARY In this paper, a new multihop multi-band cognitive architecture is proposed for next generation cellular networks. When the primary user becomes active in between a cognitive radio communication, then one or more CGs could change their transmission/reception frequency accordingly and the frequency of the entire system need not be changed. This results in a simple solution for the frequency switching and resource allocation problem, especially in case of asymmetric traffic in cellular networks. In addition, due to the multihop architecture and multiband model, a CG needs to transmit only to its neighboring CG/ mobile terminal which in turn results in reduced transmission power for the CG. Significantly, the wideband slowly responding local oscillators are replaced by a number of small and agile local oscillators in the mobile terminal, each of which scans a small part of the entire spectrum ranging from 800 MHz to 2.4 GHz. It has been observed that the system capacity of the cellular network shows a significant increase using the MMC architecture. In addition, with an increase in the number of available frequency bands, the time taken for allocation of bands to all communicating users decreases exponentially as compared to the other conventional methods.
[8] D. Cabric, S. M. Mishra and R. W. Brodersen, “Implementation Issues in Spectrum Sensing for Cognitive Radios,” in Asilomar Conference on Signals, Systems and Computers, 2004. [9] X. Huang, N. Han, G. Zheng, S. Sohn and J. Kim, “Weighted Collaborative Spectrum Sensing in Cognitive Radio,” in 2nd International Conference on Communications and Networking in China, CHINACOM, 22-24 August 2007, pp. 110–114. [10] J. K. Lee, J. H. Yoon and J. U. Kim, “A New Spectral Correlation Approach to Spectrum Sensing for 802.22 WRAN System,” in International Conference on Intelligent Pervasive Computing, 2007, pp. 101–104. [11] B. Wild and K. Ramchandran, “Detecting Primary Receivers for Cognitive Radio Applications,” in New Frontiers in Dynamic Spectrum Access Networks, 2005. IEEE DySPAN, 8-11 November 2005, pp. 124–130. [12] A. Sahai, N. Hoven and R. Tandra, “Some Fundamental Limits on Cognitive Radio,” in Allerton Conference on Communication, Control and Computing, October 2004. [13] N. M. Kim, and et al., “Robust Cognitive Radio Based OFDM Architecture with Adaptive Traffic Allocation in Time and Frequency,” Electronics and Telecommunications Research Institute (ETRI) Journal, vol. 30, no. 1, February 2008. [14] X. Zhou, Z. Zhang, and P. Cheng, “An Interference Avoidance Scheme for OFDM Cognitive Radio Systems,” in International Conference on Communication Technology, ICCT, 27-30 November 2006. [15] T. Weiss and F. K. Jondral, “Spectrum Pooling: An Innovative Strategy for the Enhancement of Spectrum Efficiency,” IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, March 2004. [16] A. Attar, M. R. Nakhai and A. H. Aghvami, “Cognitive Radio Transmission Based on Direct Sequence MC-CDMA,” IEEE Transactions on Wireless Communications, vol. 7, no. 4, pp. 1157–1162, April 2008. [17] D. Niyato and E. Hossain, “Market-equilibrium, competitive, and cooperative pricing for spectrum sharing in cognitive radio networks: Analysis and comparison,” IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4273–4283, November 2008. [18] H. Wu and C. Qao and S. De and O. Tonguz, “Integrated Cellular and Ad hoc Relaying Systems,” IEEE Journal on Selected Areas in Communication, vol. 19, no. 10, pp. 2105–2115, October 2001. [19] H. Venkataraman, H. Haas, S. Yun, Y. Lee and S. McLaughlin, “Performance Analysis of Hybrid Wireless Networks,” in Proceedings of IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC’05), Berlin, Germany, vol. 3, 11-14 September 2005, pp. 1742–1746. [20] R. Ananthapadmanabha, B.S. Manoj and C.S.R. Murthy, “Multihop Cellular Networks: The Architecture and Routing Protocol,” in Proceedings of IEEE International Symposium on Personal Indoor Mobile Radio Communications (PIMRC’01), San Diego, USA, vol. 2, 30 September - 3 October 2001, pp. 78–82. [21] H. Venkataraman, S. Sinanovic and H. Haas, “Cluster-based Design for Two-Hop Cellular Networks,” International Journal for Communications, Networks and Systems (IJCNS), vol. 1, no. 4, November 2008. [22] L. Le and E. Hossain, “Multihop Cellular Networks: Potential Gains, Research Challenges, and a Resource Allocation Framework,” IEEE Communications Magazine, vol. 45, pp. 66–73, September 2007. [23] Y. S. Lin, C. C. Liu, K. M. Li and C. H. Chen, “Design of an LTCC Tri-Band Transceiver Module for GPRS Mobile Applications,” IEEE Transactions on Microwave Theory and Techniques, vol. 52, no. 12, pp. 2097–2103, December 2004. [24] M. Tzortzakakis and R. J. Langley, “Quad-Band Internal Mobile Phone Antenna,” IEEE Transactions on Antennas and Propagation, vol. 55, no. 7, pp. 2097–2103, July 2007. [25] N. T. Tchamov, S. S. Broussev, I. S. Uzunov, and K. K. Rantala, “DualBand LC VCO Architecture With a Fourth-Order Resonator,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 54, no. 3, pp. 277–281, March 2007. [26] 3rd Generation Partnership Project (3GPP), Technical Specification Group Radio Access Network, “Selection procedures for the choice of radio transmission technologies of the UMTS,” 3GPP TR 30.03U, May 1998. Retrieved November 30, 2006, from http://www.3gpp.org/ftp/Specs/htmlinfo/3003U.htm. [27] S. Toumpis and A. J. Goldsmith, “Capacity Regions for Wireless Ad hoc Networks,” IEEE Transactions on Wireless Communications, vol. 2, no. 4, pp. 736–748, July 2003. [28] H. Vishwanathan and S. Mukherjee, “Performance of Cellular Networks with Relays and Centralized Scheduling,” IEEE Transactions on Wireless Communications, vol. 4, pp. 2318 – 2328, September 2005.
In conclusion, the MMC architecture provides a two-fold benefit - increase in the data rate for the communicating users, and higher number of users simultaneously accommodated by the wireless system. ACKNOWLEDGEMENT The authors wold like to thank Irish Research Council for Science Engineering and Technology (IRCSET) for supporting this research work.
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