We integrate the two models of Cognitive Radio (CR), namely, the
conventional Sense-and-Scavenge (SS) Model and Symbiotic Cooperative
Relaying (SCR). The resultant scheme, called
SS-SCR, improves the efficiency of spectrum usage and
reliability of the transmission links. SS-SCR is enabled by a
suitable cross-layer optimization problem in a multihop multichannel CR network.
Its performance is compared for different PU activity patterns with those
schemes which consider SS and SCR separately
and perform disjoint resource allocation. Simulation results depict the
effectiveness of the proposed SS-SCR scheme. We also indicate
the usefulness of cloud computing for a practical deployment of the scheme.
The emerging Cognitive Radio (CR) technology is an attempt to
alleviate the inefficient utilization of the spectrum, created by the current
Command-and-Control spectrum access policy. It temporarily
allows unused portions of the spectrum (spectrum holes or
white-spaces), owned by the licensed users, known as
primary users (PUs), to be accessed by unlicensed users,
known as secondary users (SUs), without causing intrusive
interference to the former’s communication [1]. This is the Sense-and-Scavenge (SS)
Model of conventional CR. A CR node is characterized by an adaptive,
multi-dimensionally aware, autonomous radio system empowered by advanced
intelligent functionality, which interacts with its operating environment and
learns from its experiences to reason, plan,
and decide future actions to meet various needs [2].
In the SS model of CR, the temporal PU activity patterns have a
significant influence on the opportunities for the SUs. The source traffic for
the PU alternates between ON (busy) and OFF (idle) periods. The ON/OFF activity
is characterized by suitable statistical models, for predictive estimation of
the patterns. Exponential [3–6] and log-normal [3–5]
distributions are popularly used in the literature to model the ON (and OFF)
times of the PU activity. Measurements have also revealed that successive ON and
OFF periods are independent, though in some cases long-term correlations exist
[4].
1.2. Symbiotic Cooperative Relaying
An interesting paradigm that has surfaced in the research surrounding CR is a
symbiotic architecture, which improves the efficiency of spectrum usage and
reliability of the transmission links [7–12]. According to
this model, which we refer to as Symbiotic Cooperative Relaying
(SCR), the PU seeks to enhance its own communication by leveraging
other users in its vicinity, having better channel conditions, as cooperative
relays for its transmission and in return provides suitable remuneration to
them. The SU nodes, being scavengers of the licensed PU spectrum, are potential
candidates as relays, since they are idling when the PU transmission is in
progress. Besides, they have cognitive capabilities, which give a large amount
of flexibility of reconfiguration and resource allocation during the cooperative
relaying process. The cooperation from the SU network results in enhanced
transmission rate of the PU, which translates into reduced transmission time for
the same amount of information bits of the PU as that transmitted on its direct
link. Then, the time saved can be offered to the SUs for their own communication
as a reward for cooperating with the PU (with a fixed rate demand). The SUs can
achieve their communication in the time incentive without the
need for spectrum sensing. In our previous work, we have formulated a
cross-layer design to enable the SCR scheme, called
Cognitive Relaying with Time Incentive (CRTI), for an
Orthogonal Frequency Division Multiplexing-(OFDM-) based multi-hop CR network,
with special emphasis on the MAC layer coordination protocol [13]. We have also proposed that it is
possible to reward the SUs with incentive frequency bands, that is,
Cognitive Relaying with Frequency Incentive (CRFI) [12, 14]. Some unique challenges are faced when the SCR
scheme is enabled on the spectra of multiple PUs; we have addressed these in
prior work as well [15, 16].
In case of SCR, the PU is assumed to have a constant occupancy
state throughout the frame duration (in a frame-based communication); that is,
it does not exhibit intermittent ON/OFF periods. During those frames when
SCR is enabled, the PU should definitely be ON.
1.3. The <italic>SS-SCR</italic> Scheme
In this paper, we integrate the two aforementioned models of CR, namely, the
Sense-and-Scavenge (SS) model of conventional CR and
Symbiotic Cooperative Relaying (SCR). We refer to this
composite scheme as SS-SCR. SS-SCR entails a
multiple PU scenario, with each PU having its own distinct bandwidth of
operation. On the PUs’ spectra having a weak direct link,
SCR is enabled, while, on the rest of the PUs’
bands, SS is enabled. Since most present day wireless
technologies such as IEEE 802.16 [17] and
802.22 [18] are based on OFDM, the
multichannel multi-hop networks, thus created, pose a more challenging
environment for deployment of the SS-SCR scheme, as opposed to
simplistic two-hop or single-channel scenarios addressed in the literature
(discussed in Related Literature). Optimum resource (time,
bandwidth, power) allocation, which can be achieved by leveraging the channel
diversity abundantly available in a multichannel network, will improve spectral
efficiency and in turn maximize the transmission opportunities for both the PUs
and SUs. With this objective, we present our original contributions in this
paper, which are summarized as follows.
We propose a scheme for enabling SS-SCR by means of
a suitable cross-layer optimization problem which addresses power
control, scheduling, and routing. Though the work can readily be
extended to any number of PUs, currently a simple scenario with two
PUs is assumed—on the spectrum of one we enable
SS, while on the other we enable
SCR. The SS-SCR scheme jointly
considers the resource allocation on both the PUs’ bands to
maximize the overall spectral efficiency and mutual benefits of both
entities under concern, namely, the PU and SU.
For comparison, we also describe two schemes which consider the
SS and SCR separately, and the
resource allocation on each of the PUs’ bands is disjoint.
All the schemes are investigated under various PU ON/OFF traffic
models.
We propose the use of cloud computing to enhance the performance of
SS-SCR in practical CR networks.
To detail our work, the paper has been organized as follows. Section 2 presents related background literature.
Section 3 describes the system model and
communication scenario. Section 4
methodically explains the generalized cross-layer optimization problem. In
Section 5 we propose the
SS-SCR scheme, while in Section 6 we describe the problems for the SS and
SCR schemes separately considered. Section 7 provides a note on the practical
implementation. Section 8 illustrates the
use of cloud computing for SS-SCR. In Section
9, we present simulation results and
their detailed analysis. Section 10
concludes the paper.
2. Related Literature
Conventionally, there are two approaches to spectrum sharing in CR [19]: underlay approach, in
which the SUs and the PU access the same frequency band by the use of sophisticated
spread spectrum techniques, and overlay approach, in which the SUs
access the licensed spectra when the PU is not using it. The SS
model pertains to the overlay approach—the SUs sense the
spectrum to detect a white space and utilize it for their own communication.
Surrounding the concept of SCR for CR, many schools of thought have
evolved to accommodate substantially different technologies and solutions. Simeone
et al. [7, 8] have used game theoretic tools to analyze the performance of
cooperation in a CR network, wherein the PU leases the owned spectrum to an ad hoc
network of SUs in exchange for cooperation in the form of transmission power from
the SUs. The model proposed by J. Zhang and Q. Zhang [9] is more rational; when the PU’s demand is satisfied, it is
willing to enhance its benefit in any other format, for instance, by collecting a
higher revenue from the SU. Xue et al. [10]
have considered a single full-duplex amplify-and-forward (AF) SU relay to assist the
PU transmission. Gong et al. [11] have
analyzed the power and diversity gains obtained by AF relaying of the PU’s
data by multiple cooperating SUs. All of the aforementioned works in the literature
have considered either a single-relay node or single-channel CR networks. The
authors have also contributed significantly towards SCR schemes for
multichannel multi-hop networks [12–16]. The cross-layer
formulations in this work are inspired by those of Shi and Hou [20], Zhang et al. [21], and some references therein. While Shi et al. aim at
maximizing the sum throughput of the SUs in a multi-hop multichannel CR network, in
the proposed SS-SCR scheme, the objective is to perform a joint
resource allocation on both the PUs band (SS and
SCR) for maximizing the net spectral efficiency. As far as the
previous works of the authors are concerned, the concept of CRTI
[13] involves a cross-layer optimization
problem for a single source, that is, PU Tx, for throughput maximization. The
approaches to CRFI [13,
14] are totally different in their
objective—that of achieving a specified throughput for the PU while using the
least number of frequency bands. Techniques for CRTI for multiple
PUs [15, 16] describe the maximization of the time incentive for the SUs, while
utilizing multiple PUs spectra optimally. Two methods have been proposed for the
same, the formulations for which are distinct, and different from those in the
literature [20, 21].
This work differs from the above in the fact that it is a hybrid
architecture: it integrates the conventional SS model with
SCR, for a multiple PU scenario.
3. System Description
We consider a CR system with a network of cognitive SUs and two PU transceivers
(Figure 1). Each PU has its own distinct
bandwidth of operation. The available bandwidth is divided into frequency flat
subchannels by deploying OFDM. The band-sets of the two PUs are denoted by 𝕄1 and 𝕄2, respectively. On the band-set of PU 1,
conventional CR mode of operation, that is, SS, is enabled. The SUs
are continuously sensing the spectrum for a transmission opportunity; when PU 1 is
OFF, the SUs use its spectrum for their own communication. The activity of
PU Tx 1 is detected by all the SU nodes by cooperative
spectrum sensing [22]. Band-set 𝕄1 is also referred to as the SS
band.
System model.
On the other hand, on the band-set of PU 2, SCR is enabled. Rather
than using the direct link, the PU Tx 2 relays its
data through the SU network and in return rewards them with a time incentive λt for their own communication. If Cdir is the throughput (bits/sec/Hz) obtained on the
direct link, Crel is the maximized throughput (bits/sec/Hz) obtained
through the SU relay network, then the incentive in a time frame normalized to unity
is λt=1-Cdir/Crel, 0≤λt≤1. On band-set 𝕄2 (also referred to as the SCR
band), PU Tx 2 acts as the source,
PU Rx 2 as the destination, and the SU nodes act as
the relays in the multi-hop relay network (Figure 1). Decode-and-forward multihopping is assumed at each node.
The fading gains for various links are mutually independent and are modeled as zero
mean complex circular Gaussian random variables. The protocol interference model is
assumed [20]. The channel gains are invariant
within a frame but vary over frames (i.e., block-fading channels). We assume that
the channel gains from the PU Tx 2 to SUs, the SUs to
the PU Rx 2, and those among the SUs are good enough
to provide a significantly higher end-to-end throughput as compared to the direct
link of PU 2, resulting in performance gains for both the PU and the SUs on band-set 𝕄2.
4. Problem Formulation: Cross-Layer Optimization
In the subsequent sections we will be describing the proposed
SS-SCR scheme which considers joint resource allocation on both
PUs’ bands, as well as the schemes which are disjoint in their resource
allocation on the two bands. Each scheme will involve solving a sequence of
optimization problems, their objective being maximization of the sum throughput of
the users under consideration (PU or SUs or both) within the given resources (time
slot, frequency bands, power). To efficiently exploit the channel diversities
available in the multi-hop multichannel SU network, we allow flow splitting and
spatial reuse of frequencies outside the interference range of nodes. Each
optimization problem involves a cross-layer view for power allocation, frequency
band scheduling, and routing. A relay with poor channel conditions on all its links
will be eliminated from the routes which strive to achieve maximum throughput; thus
relay selection is automatically achieved by the problem. We describe the basic
structure of such a cross-layer optimization problem which will be suitably adapted
for the various schemes to be described subsequently.
Optimization Problem (P1):
(1)max(xij(m),Pij(m),fij(l))∑l∈𝕃∑j∈Tifij(l)i=s(l). It is subject to the constraints which are
described as follows.
Since our objective (1) is to
maximize the throughput, it is sufficient to maximize the sum of outgoing flows from
the source node [23]. We denote the
communication between each unique transmitter-receiver pair as a session. s(l) and d(l) represent the source and destination of the session l,l∈𝕃, where 𝕃 denotes the set of the sessions.
Bidirectional links are assumed; that is, in the network graph each node i has an transmit/receive set of nodes Ti. fij(l) is the data flow (bits/sec) from node i to node j for session l. Equation (2) indicates that, except for the source and destination nodes, the
inflow into a node is equal to the outflow. Equation (3) ensures that all the flows are nonnegative. Equation (4) refers to the fact that the sum of
the flows on a link cannot exceed the capacity of a link according to
Shannon’s channel capacity theorem [24]. Each link has |𝕄| orthogonal frequency bands, and the net achievable throughput is
the sum throughput of the individual bands. hij(m) denotes the channel power gain on band m, and Pij(m) denotes the corresponding power allocation. We have
assumed unit bandwidth of each band. In (4), the log function contains only σ2 in the denominator due to the use of an
interference model, which ensures that when node i is transmitting to node j on band m, the interference from all other nodes in this band
must remain negligible due to the frequency domain scheduling and interference
constraints. ℕ denotes the node set of the network and 𝔼 denotes the edge set.
Equation (5) suggests that if a node i has used a band m for transmission or reception, it cannot be used by
node i again for any other transmission or reception. Note
that xij(m) is a binary variable which takes the value 1 if and
only if band m is active on link (i,j).
Equation (7) ensure that Pij(m)∈[PTij(m),Ppeak] if the band m is selected and Pij(m)=0 if the band is not selected. The data transmission from node i to node j is successful only if the received transmission
power exceeds a power threshold PT, from which we can calculate the minimum required
transmission power on a band m at node i as PTij(m)=PT/hij(m). Ppeak denotes the maximum power that can be allocated to
any band m, under which we compute the interference set Ijm of a receiving node j. Equation (8) is to ensure that the total power transmitted on all the active bands
at node i does not exceed the power available at the node Pavli.
Equation (9) ensures that for a
successful transmission on link i to j, on an interfering link k to h, the transmit power on any band m cannot exceed a threshold Ppeak if xij(m)=0, and if xij(m)=1, then ∑k∈IjmPkh(m)hkj(m)≤PI. The complete list of symbols with their description is given in
Table 1.
Notations.
Symbol
Definition
PUTx, PURx
PU transmitter, PU receiver
(i,j)
Edge between nodes i and j
Ti
The set of nodes that node i can transmit
to and receive from
hij(m)
Channel gain on edge (i,j) and band m
xij(m)
Band assignment on edge (i,j) and band m
Pij(m)
Power allocation on edge (i,j) and band m (W)
PTi,j(m)
Detection threshold of band m on edge (i,j) (W)
PI
Interference threshold of a node (W)
Ppeak
Maximum power that can be transmitted on
a frequency band (W)
Pnodei/Pavli
Power available at node i (W)
Ijm
Set of nodes that can interfere with node j
on band m
σ2
Additive white Gaussian noise (AWGN)
variance (W)
ℕ
Node set of the entire network
𝕄
Band set of the entire network
𝔼
Edge set of the entire network
𝕃
Set of SU sessions in the entire network
s(l),d(l)
Source of session l, destination of session l
fij(l)
Flow on edge (i,j) and band m for session l (bps)
In the above optimization problem hij(m), σ2, PT, PI, Ppeak, and Pavli are all constants, while xij(m), Pij(m), and fij(l) are the optimization variables. The formulation is a mixed
integer nonlinear programming problem (MINLP). Based on the discussion
on similar problems in [20, 21] and the references therein, we conjecture
that the given problem is NP-hard. We are thus motivated to investigate a linear
formulation, which will greatly simplify the problem (which is observed in terms of
reduced computation time during simulation). This entails employing three tangential
supports to the log term in (4), as
its approximation [20]. The tangential
supports are drawn at points 1, 2, and 3 on the log curve (Figure
2), namely, (0,0), (β,f(β)), and (Ppeak,f(Ppeak)). β denotes the x-coordinate of the point of intersection of the
tangents drawn at points 1 and 3. The solution to the log relaxed problem provides
an upper bound to the original maximization problem P1.
Approximating the log function.
A Feasible Centralized Solution
We suggest an approach to obtain a feasible suboptimum solution to the original
problem by decoupling the operations of power allocation and band scheduling and
that of flow computation. The solution consists of two steps.
The power allocation and band scheduling (Pijm,xijm) are obtained from the log relaxed problem with
tangential supports. This solution, however, may violate the flow
constraints.
The above (Pijm,xijm) are substituted in the original problem, which is
then solved only with respect to fij as the optimization variable. The
overall result represents a feasible solution to the original
problem P1.
5. The SS-SCR Scheme
As described earlier, PU 1’s activity is changing on band-set 𝕄1 (SS band), providing intermittent
periods for the SUs to communicate; on band-set 𝕄2 (SCR band), PU 2 is ON and
relaying its data through the SU network. It is on this band that a time
incentive will be offered to the SUs in return for their cooperation.
In SS-SCR, we solve a joint resource allocation problem on both the
PUs’ bands; that is, 𝕄1∪𝕄2, in every such time interval that PU 1’s activity changes.
There are totally four possibilities (Figure 3): PU 1 is OFF and PU 2 is relaying on 𝕄2, PU 1 is ON and PU 2 is relaying on 𝕄2, PU 1 is OFF and SUs are using the time
incentive on 𝕄2, and PU 1 is ON and SUs are using the time
incentive on 𝕄2. Cross-layer optimization problems are formulated
for the aforementioned possibilities, as follows.
PU 1 is OFF and PU 2 is relaying on 𝕄2. In this case, the joint problem
entails maximizing the sum throughput of the SUs and PU 2; the SUs want
to make the best utilization of the OFF time of PU 1, while PU 2 wants
to maximize its throughput through the SU network so that in turn it can
maximize the time incentive offered to the cooperating
SUs. The complete band-set 𝕄1∪𝕄2 and the total node power budget Pnodei are available for the problem.
PU 1 is ON and PU 2 is relaying on 𝕄2. PU 2 can maximize its throughput
through the SU network only on 𝕄2 with the total node power budget Pnodei.
PU 1 is OFF and SUs are using the time incentive on 𝕄2. The SUs can now use the complete
band-set 𝕄1∪𝕄2 with the total node power budget Pnodei to maximize their sum throughput.
PU 1 is ON and SUs are using the time incentive on 𝕄2. The SUs can only use 𝕄2 with the total node power budget Pnodei to maximize their sum throughput.
SS-SCR scheme.
To enable SS-SCR, the following parameters should be set in problem
P1 (Table 2).
SS-SCR.
Node set (ℕ)
Band-set (𝕄)
Session set (𝕃)
Pavli (W)
(Ia)^{*}
All SUsPU Tx 2, PU Rx 2
𝕄1⋃𝕄2
SUs, PU 2
Pnodei
(Ib)
All SUsPU Tx 2, PU Rx 2
𝕄2
PU 2
Pnodei
(Ic)
All SUs
𝕄1⋃𝕄2
SUs
Pnodei
(Id)
All SUs
𝕄2
SUs
Pnodei
^{ *} Note: A provision should be made to prevent the SUs
from relaying their data through PU Tx 2 and PU Rx 2 by means of
additional constraints: xij(m)=0, j=PUTx2 and xij(m)=0, i=PURx2.
6. Disjoint Resource Allocation for SS and SCR
In this section, we describe schemes based on disjoint resource allocation on the
SS and SCR bands, considering them as separate
problems.
6.1. Scheme A
This scheme gives priority to the activity on the SS band and
second preference to the SCR band. It is devised for that
situation in which the OFF periods of PU 1 are high. The following steps are
adopted (Figure 4(a)).
First, the SUs’ sum throughput maximization problem is solved
on band-set 𝕄1 (SS band). The SUs
will be sensing for a spectrum opportunity on this band. In the OFF
time of PU 1, they will utilize this band for their own
communication. The total node power budget Pnodei is available for them at each node i.
Secondly, on band-set 𝕄2 (SCR band), PU Tx
2 will relay its data through the SU network with maximized
throughput. Since the communication happens concurrently with the
SU’s communication on 𝕄1, now the power available at each
node i is the node power budget minus the
power consumed in step (IIa), that is, Pnodei-Pconsi. The channel diversity and consequently the higher
throughput obtained from the SU network will diminish the
transmission time for the same number of bits as those transmitted
on the direct link of PU 2. The time saved is offered as an
incentive to the SUs for their own communication.
In the time incentive obtained from PU 2, the SUs
maximize their sum throughput on 𝕄2. The power available at each node i is Pnodei-Pconsi.
Separate SS and SCR: (a) Scheme A (b) Scheme B.
To enable Scheme A, the following parameters should be set in problem
P1 (Table 3).
Scheme A.
Node set (ℕ)
Band-set (𝕄)
Session set (𝕃)
Pavli (W)
(IIa)
All SUs
𝕄1
SUs
Pnodei
(IIb)
All SUs,PU Tx 2, PU Rx 2,
𝕄2
PU 2
Pnodei-Pconsi
(IIc)
All SUs
𝕄3
SUs
Pnodei-Pconsi
6.2. Scheme B
This scheme gives priority to the activity on the SCR band and
second preference to the SS band. It is devised for that
situation in which the ON periods of PU 1 are high. The following steps are
adopted (Figure 4(b)).
First, on band-set 𝕄2 (SCR band),
PU Tx 2 will relay its data through
the SU network with maximized throughput. The total node power
budget Pnodei is available for its communication.
The higher throughput achieved, as compared to the direct link of PU
2, will generate a time incentive for the SUs on 𝕄2.
Next, in the time incentive obtained from PU 2, the
SUs maximize their sum throughput on band-set 𝕄2. The power available at each node i is Pnodei.
Lastly, the SUs’ sum throughput maximization problem is
solved on band-set 𝕄1 (SS band). The SUs
will be sensing for a spectrum opportunity on this band. In the OFF
time of the PU, they will utilize this band for their own
communication. Since this transmission is concurrent with that on 𝕄2, the power available for them at
each node i is minimum of that left after
consumption in the relaying interval and the incentive period, that
is, min(Pnodei-PconsIIIai, Pnodei-PconsIIIbi).
To enable Scheme B, the following parameters should be set in problem P1 (Table
4).
Scheme B.
Node set (ℕ)
Band-set (𝕄)
Session set (𝕃)
Pavli (W)
(IIIa)
All SUs,PU Tx 2, PU Rx 2
𝕄2
PU 2
Pnodei
(IIIb)
All SUs
𝕄2
SUs
Pnodei
(IIIc)
All SUs
𝕄1
SUs
MinPnodei-PconsIIIai,Pnodei-PconsIIIbi
7. A Note on the Practical Implementation
To make the SS-SCR scheme a practical reality, a MAC schedule is
needed to coordinate all the operations. The MAC frame consists of a control
interval in which estimation of the channel states, prediction of PU activity,
solving the optimization problems at a centralized controller, and dissemination of
the decision throughout the network, are conducted [13]. It is followed by the data interval in which the PUs and SUs
communicate using the designated resources. Based on the predicted PU activity, it
can be decided when the different solutions of the joint resource allocation are to
be applied. The prediction may be corroborated with spectrum sensing to protect the
PU 1 from the SU’s interference. The time incentive can be
computed in the control interval itself, to determine when the SUs can access the
SCR band. An important underlying assumption for the successful
execution of the SS-SCR scheme, as well as Schemes A and B (included for
comparison), is that the solution time for the optimization problem on the available
spectrum is less than the spectrum hole created by the inactivity of PU 1.
Discontiguous OFDM (D-OFDM) is used at the physical layer, which allows the relays to
decode only a fraction of the total subcarriers. A control channel is dedicated for
all the signalling that enables and coordinates the entire SS-SCR scheme.
8. Cloud Computing for SS-SCR
In SS-SCR, the SU nodes are involved with the following tasks, (i)
spectrum sensing, (ii) collaborative spectrum sensing decision algorithms, (iii)
machine learning algorithms for PU activity prediction based on recorded history,
(iv) solving the cross-layer optimization problems for resource allocation and (v)
software defined radio (SDR) technologies for reconfiguration. Most of these
operations involve both processing vast volume of data (depending on the network
size and parameters) and processing it fast. The cognitive SU nodes may have limited
computing and storage capability, which may prevent them from realizing their full
potential. In such a situation, shifting some of the operations to the
cloud may drastically improve the performance of the system
[25–27]. Cloud computing is a recent technology
revolution that is shaping the world. However, the decision to exploit the vast
computational resources of the cloud should be governed by the
volume of data and computational complexity, as well as time sensitivity. Primarily
for the tasks of PU activity prediction and solving the cross-layer optimization
(especially in a large network), the cloud may be of great use in
SS-SCR (Figure 5). A low
latency, high-bandwidth, reliable link is needed between the SU network and the
cloud; else the connectivity may become a performance
bottleneck.
Cloud computing for SS-SCR.
9. Simulation Results and Discussion
We have simulated a network with the nodes randomly distributed in an area of 10
square units (Figure 8). Nodes 1 and 9
represent
PU Tx 1-PU Rx 1, on the
band-set of which Sense-and-Scavenge (SS) takes place. Nodes 10 and
11 represent
PU Tx 2-PU Rx 2, on the
band-set of which Symbiotic Cooperative Relaying (SCR) takes place.
Nodes 2 to 8 represent the SU relay nodes.
All the links undergo the Rayleigh multipath fading, defined in the time domain by ∑l=0L-1hlδ(t-lT) where hl is the complex amplitude of path l and L is the number of channel taps. The lth channel coefficient between two nodes with a
distance d between them is distributed as N(0,1/dη), and the frequency domain channel is given by its Fourier
transform. The path loss exponent η=2.5. The AWGN variance σ2=1e-4. A 16 band OFDM system is considered on each link. Bands
1–8 are the SS bands, while 9–16 are the
SCR bands. The OFDM subcarrier bandwidth is unit Hz.
The detection threshold is PT=0.01W, the interference threshold is PI=0.001W, the peak power constraint on each frequency band is Ppeak=0.5W, and the node power constraint is Pnodei=3W (it is the same on each node i).
The environment has been simulated in MATLAB, while the LINGO [28] software has been used to solve the MINLP problem.
Figures 6(a) and 6(b) depict the sum SU throughput (bits/sec/Hz) for the
proposed SS-SCR scheme with respect to 30 independent channel
instances. It is compared with Schemes A and B, which consider SS
and SCR separately, on their respective bands. Each of the values
are averaged over 100 time frames, each of 10 sec duration. Two SU sessions
are assumed, with nodes 2–7 forming the first pair and nodes 3–8
forming the second pair. The ON and OFF periods of PU 1 are each assumed to follow a
log-normal distribution. In Figure 6(a), the
mean ON time of PU 1 (μON) is 2 and the mean OFF time (μOFF) is 8, while the variance of each distribution (σON2=σOFF2) is 10. It is observed that Scheme A performs better (on an
average) than Scheme B since it gives preference to the SUs to communicate on the
SS band, which is free most of the time (mean OFF time of PU 1
is higher). In Figure 6(b), the mean ON time
of PU 1 (μON) is 8 and the mean OFF time (μOFF) is 2, while the variance of each distribution (σON2=σOFF2) is 10. It is observed that Scheme B performs better (on an
average) than Scheme A because it gives preference to PU 2’s relaying and
consequently creates a higher time incentive for the SUs to
communicate, while PU 1 provides few opportunities for the SUs to communicate on its
band (mean OFF time of PU 1 is lower). SS-SCR consistently performs
better than the disjoint SS and SCR schemes, since
the complete band-set, 𝕄1∪𝕄2, is available in every time interval for the SU’s
communication with the total node power budget. Figures 7(a) and 7(b) depict
a similar trend for the exponential distribution of PU 1. In Figure 7(a), μOFF=σOFF=8 and μON=σON=2, while in Figure 7(b), μOFF=σOFF=2 and μON=σON=8.
SU throughput versus channel instance (log-normal): (a) high mean OFF time,
(b) high mean ON time.
SU throughput versus channel instance (exponential): (a) high mean OFF time,
(b) high mean ON time.
Flow allocation.
To illustrate the results of the cross-layer optimization problems, the band
assignment and power allocation for a particular channel instance for
SS-SCR (Case Ia) are shown in Table 5. The corresponding flow (bits/sec/Hz) is shown in Figure
8.
Figure 9 demonstrates the average sum SU
throughput with different mean ON and OFF times of the log-normal and exponential
distributions (fixed variance σON2=σOFF2=10). It is observed that when the mean OFF time is higher and ON time
is lower, Scheme A performs better than Scheme B, for reasons described earlier. But
as the OFF time reduces and the ON time increases, the trend reverses. For equal
mean ON and OFF times, both Schemes A and B perform similarly.
SS-SCR is consistently better than the previous two schemes,
but its performance degrades and approaches that of Scheme B as the mean ON time
increases. This is because the band-set of PU 1 is available for too short a
duration for it to exploit the channel diversity. The above discussion holds true
for log-normal and exponentially distributed ON/OFF periods of PU 1.
SU throughput versus mean ON/OFF time: log-normal and exponential.
10. Conclusion
We have proposed a novel SS-SCR scheme to be deployed in CR
networks with multiple PUs, some of which have weak direct links. On the spectra of
such licensed users SCR is enabled, while on the other PUs’
spectra conventional SS is implemented. The hybrid
SS-SCR scheme results in a better utilization of the available
resources (time, bandwidth, power) by means of the formulated cross-layer
optimization problems. Its performance is compared, for different PU activity
patterns on the SS bands, with those schemes which consider
SS and SCR separately and perform disjoint
resource allocation. Simulation results depict that the SS-SCR
scheme with joint resource allocation gives a higher net SU throughput as compared
to the other schemes. Further, the usefulness of cloud computing is
illustrated to realize the full potential of SS-SCR.
Appendix
If Doff is the random variable which describes the OFF
period of the PU activity and if it follows the log-normal distribution, its
probability density function (PDF) is given by foff(t;μ,σ)=1tσ2πe-(lnt-μ)2/2σ2,t>0.
μ and σ denote the mean and standard deviation,
respectively.
In case of the exponential distribution, foff(t;λ)=λe-λt,t≥0.
The mean and standard deviation are both given by 1/λ.
Acknowledgments
This work has been supported in part by the Ministry of Communication and
Information Technology, Government of India, New Delhi. The work has also been
supported by Microsoft Corporation and Microsoft Research India under the Microsoft
Research India PhD Fellowship Award 2009.
HaykinS.Cognitive radio: brain-empowered wireless
communicationsZhangH.Cognitive Radio Networking for Green Communications and Green
Spectrumhttp://www.comnets.org/keynote.htmlSungK. W.KimS. L.ZanderJ.Temporal spectrum sharing based on primary user activity
predictionRiihijärviJ.NasreddineJ.MähönenP.Impact of primary user activity patterns on spatial spectrum
reuse opportunitiesProceedings of the European Wireless Conference (EW '10)April 2010ita9629682-s2.0-7795445329110.1109/EW.2010.5483445WellensM.RiihijärviJ.MähönenP.Empirical time and frequency domain models of spectrum
useBoualiF.SallentO.Pérez-RomeroJ.AgustíR.Strengthening radio environment maps with primary-user
statistical patterns for enhancing cognitive radio operationProceedings of the 6th International ICST Conference on Cognitive
Radio Oriented Wireless Networks and Communications (CROWNCOM
'11)201125626060307872-s2.0-8005474509810.4108/icst.crowncom.2011.245909SimeoneO.Bar-NessY.SpagnoliniU.ZhangY.ChenH.GuizaniM.Cooperative cognitive radioSimeoneO.StanojevI.SavazziS.Bar-NessY.SpagnoliniU.PickholtzR.Spectrum leasing to cooperating secondary ad hoc
networksZhangJ.ZhangQ.Stackelberg game for utility-based cooperative cognitive radio
networksProceedings of the 10th ACM International Symposium on Mobile Ad Hoc
Networking and Computing (MobiHoc '09)May 200923312-s2.0-7045024723410.1145/1530748.1530753XueP.GongP.CaoN.KimD. K.Symbiotic Architecture for the cognitive radio networks with
amplify-and-forward relaying cooperationProceedings of the 18th Joint Conference on Communications and
Information (JCCI '09)2009Jeju, Korea4954GongP.ParkJ. H.YooJ. M.YuB. S.KimD. K.Throughput maximization with multiuser non-selfish cognitive
relaying in CR networksProceedings of the 4th International Symposium on Wireless and
Pervasive Computing (ISWPC '09)February 20092-s2.0-6524909779610.1109/ISWPC.2009.4800611NadkarT.ThumarV.DesaiU. B.MerchantS. N.Symbiotic cooperative relaying in cognitive radio networks with
time and frequency incentiveSpringer Telecommunications System Journal10.1007/s11235-011-9494-4NadkarT.ThumarV.ShenoyG.MehtaA.DesaiU. B.MerchantS. N.A cross-layer framework for symbiotic relaying in cognitive radio
networksProceedings of the IEEE International Symposium on Dynamic Spectrum
Access Networks (DySPAN '11)May 2011deu4985092-s2.0-7996069640010.1109/DYSPAN.2011.5936240NadkarT.ThumarV.ShenoyG.DesaiU. B.MerchantS. N.Cognitive relaying with frequency incentiveProceedings of the 54th Annual IEEE Global Telecommunications
Conference (GLOBECOM '11)201161342922-s2.0-8485722900110.1109/GLOCOM.2011.6134292NadkarT.ThumarV.ShenoyG.DesaiU. B.MerchantS. N.Cognitive relaying with time incentive: protocol design for
multiple primary usersProceedings of the IEEE 22nd International Symposium on Personal,
Indoor and Mobile Radio Communications (PIMRC '11)201157758261400282-s2.0-8485753809210.1109/PIMRC.2011.6140028ThumarV.NadkarT.ShenoyG.DesaiU. B.MerchantS. N.Cognitive relaying with time incentive: multiple primary
usersProceedings of the IEEE 74th Vehicular Technology Conference (VTC
'11)201160930522-s2.0-8375516921910.1109/VETECF.2011.6093052IEEE 802.16 WiMax, http://www.ieee802.org/16/IEEE 802.22 WRAN, http://www.ieee802.org/22/AkyildizI. F.LeeW. Y.VuranM. C.MohantyS.NeXt generation/dynamic spectrum access/cognitive radio wireless
networks: a surveyShiY.HouY. T.A distributed optimization algorithm for multi-hop cognitive
radio networksProceedings of the 27th IEEE Communications Society Conference on
Computer Communications (INFOCOM '08)April 2008196619742-s2.0-5134915124910.1109/INFOCOM.2007.186ZhangJ.ZhangZ.LuoH.HuangA.A column generation approach for spectrum allocation in cognitive
wireless mesh networkProceedings of the IEEE Global Telecommunications Conference
(GLOBECOM '08)December 2008309130952-s2.0-6724910497010.1109/GLOCOM.2008.ECP.593ZengY.LiangY. C.HoangA. T.ZhangR.A review on spectrumsensing for cognitive radio: challenges and
solutionsDimitri BertsekasP.CoverT. M.ThomasJ. A.GeF.LinH.KhajehA.ChiangC. J.EltawilA. M.BostianC. W.FengW. C.ChadhaR.Cognitive radio rides on the cloudProceedings of the IEEE Military Communications Conference (MILCOM
'10)November 2010144814532-s2.0-7995163329410.1109/MILCOM.2010.5680151KoC. H.HuangD. H.WuS. H.Cooperative spectrum sensing in TV white spaces: when cognitive
radio meets cloudProceedings of the IEEE Conference on Computer Communications
Workshops (INFOCOM WKSHPS '11)April 20116726772-s2.0-7996059764510.1109/INFCOMW.2011.5928897HaradaH.MurakamiH.IshizuK.FilinS.SaitoY.HaN. T.MiyamotoG.HasegawaM.MurataY.KatoS.A software defined cognitive radio system cognitive wireless
cloudsProceedings of the 50th Annual IEEE Global Telecommunications
Conference (GLOBECOM '07)November 20072942992-s2.0-3934911534710.1109/GLOCOM.2007.62LINGO: User’s guideLINDO Systems Inc., 2006