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Self-organized Spectrum Chunk Selection Algorithm for Local Area Deployment

Self-organized Spectrum Chunk Selection Algorithm for Local Area Deployment. Sanjay Kumar 1 Yuanye Wang 2 , Nicola Marchetti 2 1 Birla Institute of Technology, Ranchi, India skumar@bitmesra.ac.in 2 Aalborg University, Denmark ywa @es.aau.dk, nm@es.aau.dk. Contents. Introduction

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Self-organized Spectrum Chunk Selection Algorithm for Local Area Deployment

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  1. Self-organized Spectrum Chunk Selection Algorithm for Local AreaDeployment Sanjay Kumar 1 Yuanye Wang 2, Nicola Marchetti 2 1Birla Institute of Technology, Ranchi, India skumar@bitmesra.ac.in 2Aalborg University, Denmark ywa @es.aau.dk, nm@es.aau.dk

  2. Contents • Introduction • Motivation and Objectives • Algorithm • Scenaion Description • Performance Metrics • Evaluation Parameters • Performance Results • Conclusions

  3. Introduction • The IMT-A : 4G systems with new capabilities • To be operational around the year 2015 • Provide access to wide range of telecommunication services • Target peak data rates 1 Gbpsin DL and 500 Mbps in UL • Focus on Local Area (LA) deployment scenarios • The Wide Area (WA) deployment uses fixed frequency reuse schemes • It needs proper network planning, such as : • Frequency reuse plans • Base station location • Controlling transmit power levels etc. • The LA of IMT-A : expected random and uncoordinated deployment • Hence netwrok planning not feasible • Also, possiblly Home Node Bs (HeNBs) share the available radio spectrum • A mechanism is needed to assign spectrum in self organized manner and improve throughput performance

  4. Motivation and Objectives • Reuse 2 gives the highest throughput performance in LA deployment [11], but needs Network planning • A mechanism is needed to achieve nearly same performance in random deployment  Fig. 1. Possible chunk selection with reuse 2 in random deployment Fig. 2. Allocation for reuse 2 with optimal network planning To minimize the mutual inter-cell interference among HeNBs with random and uncoordinated deployment in order to improve throughput performance .

  5. Algorithm Steps Initialization Update chunk selection Interference consideration Chunk switch over Figure 3: Flow chart of the Algorithm

  6. Algorithm Description Spectral bandwidth is divided in two equal size spectrum chunks (for considering to compare with reuse 2)  Algorithm could be used for any reuse schemes • Random selection of a spectrum chunk •  Initialization with sequence number & switch-over number •  Sequence number : determines position in queue •  Switch-over number : helps to avoid dead lock situation • It indicates the maximum # of times a chunk is retained Step 1: Initialization phase

  7. Algorithm Description (1) Step 2: Condition to update the chunk selection • The sequence number is reduced by one in each update interval • This indicates HeNB turn to update its chunk selection  Step 3: Interference consideration for chunk selection • Comparison of interference levels over the chunks • The UL-RIP is used as interference measurement yard stick • UL- RIP is defined as the total power received over the spectrum chunk •  The chunk selection is based on : Abs (RIP1 - RIP2 ) > P_Threshold •  This ensures a sufficient amount of interference difference before decision •  The threshold indicates a tradeoff between : • tolerable interference vs increased signaling requirement

  8. Algorithm Description (2) Step 4: Switching over to other Chunk • The chunk with minimum RIP is chosen • Main features of the Algorithm • Uses UL- RIP (already standardized for LTE system) •  Works in a random and uncoordinated deployment • Requires very little signaling exchange • Chunks allocation in sequence (avoids complexity in interference assessment) • If # of HeNBs is very high : a long convergence time may be required • Provides a scalable solution. • Performance is upper-bounded by reuses 2 scheme • Could be used for both DL & UL

  9. 50 m 100 m Scenario Description Figure 4: LA office indoor with 4 HeNBs • HeNB Location : center of each cell (also examined with randomized) • HeNB coverage area . 50 x 25 m (consisting of 10 rooms) • Simulation method : snap-shot based • Several thousands snap-shots are simulated • Users : uniformly distributed locations • Throughput :Mapping SINR [8]

  10. Performance Metrics • Average Cell Throughput:Cell throughput averaged among all the simulated cells • Cell Edge User Throughput:The 5% user outage throughput , CDF value • Chunk Selection Interval:Time period for one chunk selection operation. • It contains an integer number of transmission frames. • Some simplifications are also assumed: • No power control • Round Robin for frequency domain scheduling • Fast fading not considered

  11. Simulation Parameters PARAMETERS SETTINGS Spectrum allocation 100 MHz at 3.5 GHz Access schemes DL: OFDMA, UL: SCFDMA Duplexing Schemes TDD # UE’s per Cell 5~10 Total Transmit Power 24dBm Receiver Noise Figure 7-9 dB Room Size 10x10 m Corridoor width 5 m Internal walls light attenuation (5dB) Shadow fading LOS: 3dB, NLOS: 6dB (SD) Path Loss models LOS18.7 log10(d) + 46.8 + 20log10(f/5.0) NLOS 20 log10 (d) + 46.4 +nw · Lw + 20log10 (f /5.0) where, d = direct-line distance (m) f = carrier frequency (GHz)nw = # of walls between Tx & Rx Lw = wall attenuation (dB)

  12. Performance Results (1) DL Average Cell Throughput • Improved compared to reuse • 1 and 4 schemes • Slightly lower compared to • reuse 2 scheme • Reuse 2 needs planning for • optimal performance. • Proposed algorithm performs in • self- organized manner with • random and un-coordinated • deployment Figure 5 : Comparison of DL Average Cell Throughput

  13. Performance Results Performance Results (2) DL Cell Edge User Throughput • significantly higher • compared to reuse 1 • scheme (200 ~ 300 %) • Nearly same as reuse • 4 scheme • However lower than • reuse 2 scheme Figure 6: Comparison of DL Cell Edge User Throughput

  14. Performance Results Performance Results (3) Convergence Time The performance is stabilized after 10 selection intervals, with Significant Improvement Figure 7 : Convergence Time of the Algorithm

  15. Performance Results Performance Results (4) Randomized HeNB Locations • It may be difficult to control HeNB locations • Especially in home scenarios • Nearly the similar performance is realized With the same chunk allocation as DL, The similar performance has been realized in UL also Figure 8: DL Average Cell Throughput with Randomized HeNB Locations

  16. Conclusions • An Algorithm for self organized spectrum chunk selection has been proposed • It minimizes mutual inter-cell interference and improves the system throughput performance • Performance approaches fixed frequency reuse 2 scheme • Gives much better performance than fixed frequency 1 and 4 schemes • Suitable for LA with large scale random and uncoordinated deployment • Works with very limited signaling exchange • Needs no additional measurements than what is already established for the LTE • The main features of the algorithm are scalability and operation simplicity

  17. References • [1] H. Murai, M. Edvardsson and E. Dahlman, “LTE-Advanced – The Solution for IMT-Advanced”, ERICSSON, 2008. • [2] IEEE 802.11 B. P. Crow, I. Widjaja, J.G. Kim, P. T. Sakai, “Wireless Local Area Networks”, IEEE Communications Magazine, September 1997, pp. 116-126. • [3] ETSI MCC, “Report of 3GPP TSG RAN IMT-Advanced Workshop”, April 7-8, 2008. • [4] Arne Simonsson, “Frequency Reuse and Intercell Interference Co-ordination in E-UTRA”, IEEE VTC2007-Spring, pp. 3091-3095 • [5] R. Giuliano, C. Monti and P. Loreti, “Wireless Technologies Advances for Emergency and Rural Communications - WiMAX Fractional Frequency Reuse for Rural Environments”, IEEE Wireless Communications, June 2008, pp. 60-65 • [6] T. S. Rappaport and R. A. Brickhouse, “A Simulation Study of Urban In-building Cellular Frequency Reuse”, IEEE Personal Communications, February 1997, pp. 19-23 • [7] IST-4-027756 WINNER II, D1.1.2 “WINNER II Channel Models part I- Channel Models”, Sept 2007. • [8] P. Mogensen, W. Na, I. Kovács, F. Frederiksen, A. Pokhariyal, K. Pedersen, T. Kolding, K. Hugl and M. Kuusela, “LTE Capacity compared to the Shannon Bound”, IEEE VTC2007-Spring, pp. 1234-1238. • [9] TR 101 112 V3.2.0 (1998-04), Technical Report, Universal Mobile Telecommunications System (UMTS); Selection Procedures for the Choice of Radio Transmission Technologies of the UMTS (UMTS 30.03 version 3.2.0) • [10] 3GPP TR 25.814 Technical Specification Group Radio Access Network; Physical Layer Aspects for Evolved UTRA, V7.0.0 (2006-6). • [11] Sanjay Kumar, “Techniques for Efficient Spectrum Usage for Next Generation Mobile Communication Networks: An LTE and LTE-A case Study” a PhD thesis at Aalborg University, Denmark, June, 2009 (ISBN 978-87-92328-29-8).

  18. ThankYou

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