Resource Allocation Optimization for Time and Wavelength Division Multiplexing Passive Optical Network Enabled Mobile Fronthaul With Bitrate-Variable Compressed Common Public Radio Interface

Gang Wang; Rentao Gu; Yuefeng Ji

doi:10.1364/JOCN.8.000417

Resource Allocation Optimization for Time and Wavelength Division Multiplexing Passive Optical Network Enabled Mobile Fronthaul With Bitrate-Variable Compressed Common Public Radio Interface

Gang Wang, Rentao Gu, and Yuefeng Ji

Journal of Optical Communications and Networking
Vol. 8,
Issue 6,
pp. 417-426
(2016)
•https://doi.org/10.1364/JOCN.8.000417

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Gang Wang, Rentao Gu, and Yuefeng Ji, "Resource Allocation Optimization for Time and Wavelength Division Multiplexing Passive Optical Network Enabled Mobile Fronthaul With Bitrate-Variable Compressed Common Public Radio Interface," J. Opt. Commun. Netw. 8, 417-426 (2016)

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Previously assigned OCIS codes
- Optical communications (060.4510)
- Networks, network optimization (060.4256)

About this Article
History
- Original Manuscript: January 22, 2016
- Revised Manuscript: April 11, 2016
- Manuscript Accepted: April 19, 2016
- Published: May 20, 2016

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Abstract

Recently, a cloud radio access network (C-RAN) has been proposed as a candidate architecture for fifth-generation mobile communication. Fronthaul is a new segment in C-RAN. In this paper, we investigate the algorithms for resource allocation in time and wavelength division multiplexing passive optical network enabled fronthaul. We formulate an integer nonlinear programming (INLP) model, considering the operation mode of the small cell. A heuristic method is also proposed based on an adaptive parallel genetic algorithm (GA). Three optimization objectives are considered when we implement the resource allocation schemes in the fronthaul: 1) minimize the total number of used wavelengths, 2) minimize the load imbalance of the fronthaul, and 3) minimize the amount of traffic influenced by fronthaul virtual topology change. The results show that INLP and adaptive parallel GA have good performance for resource allocation in the fronthaul. Wavelength resources can be significantly saved in different load scenarios, so that the load difference can be minimized with minimal topology adjustment. Furthermore, the adaptive parallel GA obtains more efficient resource allocation than the traditional GA with much less running time, which makes it applicable for the large-scale fronthaul network optimization with fast convergence.

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1:	$P = Φ$ ;
	{Phase I: Construct Initial Population $P$ }
2:	while $\| P \|$ is less than preset population size do
3:	$I = Φ$ ;
	{Construct a Chromosome $I$ }
4:	for all small cells $c_{i} \in C$ do
5:	select $w_{j}$ from $W$ randomly;
6:	construct a gene $g_{i, j} = {w_{j}}$ ;
7:	insert $g_{i, j}$ into $I$ ;
8:	end for
9:	verify $I$ ;
10:	if $I$ is a feasible Chromosome then
11:	insert $I$ into $P$ ;
12:	end while
	{Phase II: Evolution}
13:	$I_{best} = Φ$ ;
14:	while GA is not converged do
15:	divide $P$ into three subpopulations uniformly;
16:	for all $I$ in $P_{sub 1}$ do
17:	find preset parameters for all genes $g_{i, j}$ in $I$ ;
18:	calculate fitness $I_{traffic}$ for $I$ ;
19:	end for
20:	for all $I$ in $P_{sub 2}$ do
21:	find preset parameters for all genes $g_{i, j}$ in $I$ ;
22:	calculate fitness $I_{traffic}$ for $I$ ;
23:	end for
24:	for all $I$ in $P_{sub 3}$ do
25:	find preset parameters for all genes $g_{i, j}$ in $I$ ;
26:	calculate fitness $B_{w}$ for $I$ ;
27:	end for
28:	for all $I$ in $P$ do
29:	find preset parameters for all genes $g_{i, j}$ in $I$ ;
30:	calculate fitness $N_{w} + B_{w} + I_{traffic}$ for $I$ ;
31:	end for
32:	$I_{best}$ ← the fittest one in $P$ based on fitness
	$N_{w} + B_{w} + I_{traffic}$ ;
33:	update the crossover rate $p_{c}$ and mutation rate $p_{m}$ ;
34:	evolve $P$ with adaptive crossover and mutation operation;
35:	calculate the degree of the diversity $D_{p}$ for $P$ ;
36:	end while.

$C_{w}$ , capacity of a wavelength resource	10 Gbits/s
$L_{c}$ , load demand range of a small cell	0–100 Mbits/s
$R$ , CPRI constant link rate (option 3)	2.5 Gbits/s
$β_{c}$ , CPRI compression ratio of the small cell	0.5–1
The number of the small cells ( $\| W \| = 4$ )	16
The number of the small cells ( $\| W \| = 8$ )	32
The number of the small cells ( $\| W \| = 16$ )	64
$\| P \|$ , population size of the adaptive parallel GA	120
$D_{p}$ , threshold for GA convergence	0.15
Number of generations below the threshold to confirm GA convergence	5

1:	$P = Φ$ ;
	{Phase I: Construct Initial Population $P$ }
2:	while $\| P \|$ is less than preset population size do
3:	$I = Φ$ ;
	{Construct a Chromosome $I$ }
4:	for all small cells $c_{i} \in C$ do
5:	select $w_{j}$ from $W$ randomly;
6:	construct a gene $g_{i, j} = {w_{j}}$ ;
7:	insert $g_{i, j}$ into $I$ ;
8:	end for
9:	verify $I$ ;
10:	if $I$ is a feasible Chromosome then
11:	insert $I$ into $P$ ;
12:	end while
	{Phase II: Evolution}
13:	$I_{best} = Φ$ ;
14:	while GA is not converged do
15:	divide $P$ into three subpopulations uniformly;
16:	for all $I$ in $P_{sub 1}$ do
17:	find preset parameters for all genes $g_{i, j}$ in $I$ ;
18:	calculate fitness $I_{traffic}$ for $I$ ;
19:	end for
20:	for all $I$ in $P_{sub 2}$ do
21:	find preset parameters for all genes $g_{i, j}$ in $I$ ;
22:	calculate fitness $I_{traffic}$ for $I$ ;
23:	end for
24:	for all $I$ in $P_{sub 3}$ do
25:	find preset parameters for all genes $g_{i, j}$ in $I$ ;
26:	calculate fitness $B_{w}$ for $I$ ;
27:	end for
28:	for all $I$ in $P$ do
29:	find preset parameters for all genes $g_{i, j}$ in $I$ ;
30:	calculate fitness $N_{w} + B_{w} + I_{traffic}$ for $I$ ;
31:	end for
32:	$I_{best}$ ← the fittest one in $P$ based on fitness
	$N_{w} + B_{w} + I_{traffic}$ ;
33:	update the crossover rate $p_{c}$ and mutation rate $p_{m}$ ;
34:	evolve $P$ with adaptive crossover and mutation operation;
35:	calculate the degree of the diversity $D_{p}$ for $P$ ;
36:	end while.

$C_{w}$ , capacity of a wavelength resource	10 Gbits/s
$L_{c}$ , load demand range of a small cell	0–100 Mbits/s
$R$ , CPRI constant link rate (option 3)	2.5 Gbits/s
$β_{c}$ , CPRI compression ratio of the small cell	0.5–1
The number of the small cells ( $\| W \| = 4$ )	16
The number of the small cells ( $\| W \| = 8$ )	32
The number of the small cells ( $\| W \| = 16$ )	64
$\| P \|$ , population size of the adaptive parallel GA	120
$D_{p}$ , threshold for GA convergence	0.15
Number of generations below the threshold to confirm GA convergence	5

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Equations (14)

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