1 Introduction
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CR has to operate as a secondary service on non-interfering, non-protection basis, alongside other services using the same frequency band.
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CR has to work non-intrusively, has to protect PUs, and vacate the spectrum in case of a PU appearance.
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CR FA has to be fast, adaptive and easy to implement and does not necessarily have to provide an optimal FA.
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CR FA has to re-initiate and re-converge in cases of the quality degradation due to a dynamic changes in the radio channel or SU and PU activation.
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CR FA has to allocate the channels and also the spectrum fragments of a different bandwidth.
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CR FA has to work with the limited information in an environment with either cooperative or selfish SUs.
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CR FA has to be flexible, applicable in centralized and distributed manner.
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CR FA has to assign frequencies continuously and sequentially when a part of the PUs and SUs are already operating.
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CR FA has to be applicable in the heterogeneous wireless environment with the different classes of the dynamic spectrum access, different user requirements, and different protection requirements.
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The extension of the CR network conflict graph with:
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Introduction of the continuous value edge weights quantifying the potential level of co- and adjacent channel interference between SUs
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Incorporation of the influence of the adjacent channel interference by introducing an additional layer in the conflict graph
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Introduction of the SU interference categorization for interference susceptibility of the FA algorithms, while reducing the communication overhead
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The proposal of the spectrum fragments assignment with the identification of the central frequency and optimal bandwidth for the CR transmissions
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Introduction of a new saturation metric for a dynamic sorting of the vertices in the process of the sequential FA, taking into consideration the channel limitations due to the PU transmissions as hard constraints and a level of the interference from the adjacent assigned CRs as soft constraints
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The proposal and evaluation of the interference sensitive FA algorithms with the objective of:
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Minimal total CR network interference
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Maximal CR network throughput with consideration of the network interference as a comprehensive metric
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2 Related work
Ref. | Objective | Approach | Method |
---|---|---|---|
[11] | Throughput/ | Centralized single ch | Linear integer |
variance | optimization | ||
[12] | Power/total | Distributed / centralized | Graph theory |
throughput | |||
[13] | Fairness/throughput | Centralized multi ch | Graph theory |
[14] | Node connectivity/ | Local | Heuristic |
interference | algorithm | ||
[15] | Throughput surplus | Distributed / centralized | Nash bargaining / |
game theory | |||
[16] | Fairness/QoS | Distributed list coloring | Graph theory |
[17] | Fairness | Local multi ch | Graph theory / heuristic |
[18] | Throughput | Macro BS femtocell | Graph coloring |
[19] | Interference | Wireless mesh | Graph coloring |
[20] | Interference/power | Distributed | Game theory |
3 The proposed FA framework
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Spectrum sensing. A CR user can only utilize temporary unused parts of the spectrum. Therefore, CR should monitor the available spectrum bands, collect the information on the spectrum use, and identify possible spectrum holes and their characteristics.×
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Spectrum decision. Based on the spectrum availability information acquired through the spectrum sensing, policy guidelines, user requirements, and registry of the spectrum use, CR characterizes radio frequency spectrum possible for various models of the dynamic spectrum access. In the spectrum decision, CR or the centralized entity determines the carrier frequency, channel bandwidth, transmission power, modulation, coding, communication technology, together with other operational, and technical parameters used for the CR operation.
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Dynamic spectrum access (DSA). The CR reconfigures its technical parameters in line with the selected operational technical parameters and operates in order to satisfy its primary goal of successful communication with a required QoS.
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Learning. Since CR is operating in heterogeneous radio environment with different user characteristics, different requirements and many parameters determining its environment and performance, CR has to adapt to the constantly changing environment, observe performance of its operation, and has to adapt its spectrum decision function using reinforcement learning.
3.1 Problem formulation and system model
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CR self-goal: successfully transmit as much information as possible with required QoS
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CR network goal:
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Minimizing the total network interference
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Maximizing the network throughput
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Requirements towards other users: not to cause excessive interference with the operating PUs, to keep the CR mutual interference under the reasonable limit and to efficiently share the radio frequency spectrum
Notation | Description |
G
c
=(V
c,E
co,E
adj) | Conflict graph with vertices V
c and edges E
co,E
adj
|
F={1,..,M} | Set of M available frequencies |
B
v
| List of blocked frequencies at CRv due to PU |
hard constraints | |
W
co={wvi vjco}∈[0,1] | Set of co-channel continuous interference |
weight coefficients | |
W
adj={wvi vjadj}∈[0,1] | Set of adjacent channel continuous |
interference weight coefficients | |
N
PU,N
SU
| Number of primary and secondary users |
hPUiPUi
′
hPUiSUk
′
| Channel coefficients between PU Tx i
′, SU Tx k
′, |
and PU Rx i
| |
hSUlSUk
′
hSUlPUj
′
| Channel coefficients between SU Tx k
′, PU Tx j
′, |
and SU Rx l
| |
P
Rx_SUl_D,P
Rx_SUl_I
| Desired received signal strength and interfering |
received signal strength at the l t
h SU | |
\(P_{\text {Tx}\_\text {PU}m^{\prime }}, P_{\text {Tx}\_\text {SU}l^{\prime }}\phantom {\dot {i}\!}\)
| Transmitting power at m
′th PU and l
′th SU |
S
vi
| Saturation metrics label |
Colvi
| Vertex coloring argument |
Acronyms | Description |
CR | Cognitive radio |
FA | Frequency assignment |
PU, SU | Primary user, secondary user |
Tx, Rx | Transmitter, receiver |
QoS | Quality of service |
CminSumInt | Centralized minimum cumulative network |
interference FA algorithm | |
CMaxSumCap | Centralized interference sensitive maximum |
throughput FA algorithm | |
DminInt | Distributed minimum interference FA algorithm |
DMaxCap | Distributed interference sensitive maximum |
throughput FA algorithm |
3.2 Framework overview
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The local list of the blocked frequencies B v associated with each conflict graph vertex representing frequencies which cannot be used by CR
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Two layers of edges in conflict graph taking into consideration co-channel interference E co and adjacent channel interference E adj
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Continuous interference weight coefficients w co and w adj associated to the conflict graph co-channel and adjacent channel edges incorporating quantification of possible interference between adjacent vertices corresponding to the interference “strength”
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The categorization of the interference weights reducing communication overhead
4 Interference modeling and characterization
4.1 Interference model
4.2 Weighting coefficients characterization
5 Interference sensitive FA algorithms
5.1 Algorithm description
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Phase 1. FA preparation:
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Establish the list of the blocked channels at all SUs.
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Determine the SU transmitting power for each available channel at all SUs.
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Calculate the SU throughput for each available channel at all SUs.
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Establish a conflict graph edges and determine the interference weights for co-channel and adjacent channel transmissions.
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Interference categorization.
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Phase 2. Selecting the next SU to assign frequency:
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Label the non-assigned SUs using specific CR FA saturation metric.
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Dynamically order the SUs with decreasing saturation score.
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Select the SU with the highest saturation metric to assign frequency as the next SU to process.
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Phase 3. Assigning frequency to the selected SU:
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Calculate objective function for each available frequency at the selected SU.
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Select and assign frequency maximizing objective function at the selected SU.
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Phase 4. CR FA performance evaluation and conflict graph update:
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Calculate interference, throughput, and throughput variance for all SUs.
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Determine the overall network performance and update conflict graph.
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Objective | ||
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Implementation | Minimal interference | Maximal throughput |
Centralized | CminSumInt | CMaxSumCap |
Distributed | DminInt | DMaxCap |
5.2 Saturation metric labeling and coloring
5.2.1 Minimum cumulative network interference FA
5.2.2 Interference sensitive maximum throughput FA
5.3 Transmit power control
6 Centralized and distributed algorithm
6.1 Centralized FA algorithm
6.2 Distributed FA algorithm
7 Generalizations and implementation
7.1 Central frequency and bandwidth CR spectrum assignment
7.2 Implementation considerations
8 Numerical results and discussion
8.1 Simulation setup
Parameter | Value |
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Area under test | 30 km × 30 km |
Number of PUs (N
PU) | 15–40 |
Number of SUs (N
SU) | 10–50 |
Frequency band | 2000 MHz |
Channel bandwidth | 3.5 MHz |
Number of frequencies (M) | 15–25 |
PU transmission range (dPU) | 10 km |
SU transmission range (dSU) | 1–4 km |
PU interference range | 20 km |
SU interference range | 2–8 km |
Modulation M-QAM | M = [4, 16, 32, 64] |
Maximal SU throughput (64 QAM) | 16 Mbit/s |
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Average throughput per SU (Mbit/s): the ratio of the cumulative CR network throughput over the number of active SUs
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Average interference per SU: the ratio of the cumulative interference of all interference sources over the number of active SUs
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SU throughput fairness: Jain’s fairness index as a measure of the throughput distribution of all of the active SUs in the CR network