1. Fairness-Aware Cooperative Resource Allocation for Self-Healing in SON-based Indoor System Kisong Lee, Student Member, IEEE, Howon Lee, Associate Member, IEEE, and Dong-Ho Cho, Senior Member, IEEE

2. agenda I. INTRODUCTION II. SYSTEM MODEL III. FA-CRA: FAIRNESS-AWARE COOPERATIVE RESOURCE ALLOCATION IV. SIMULATION RESULTS AND DISCUSSIONS V. CONCLUSIONS

3. I. INTRODUCTION

4. THE main objectives of a self- organizing network (SON) autonomous network deployment, Network performance optimization, real-time adaptation to environmental changes

5. three functionalities self-configuration, self-optimization, self-healing

6. Fig. 1

7. FA-CRA In this paper, to deal with abrupt faults with guaranteeing user fairness in SON-based indoor system, we propose a fairness-aware cooperative resource allocation (FA-CRA) algorithm

8. FA-CRA In the FA-CRA algorithm, i) the concept of healing channels (HCs) for serving users in faulty indoor cells is proposed and the set of HCs is determined adaptively, ii) indoor cells cooperate on the HCs to overcome the degradation of network throughput, iii) subchannels and power are allocated suboptimally for guaranteeing user fairness through an optimization technique.

9. II. SYSTEM MODEL

10. Environmental conditions We consider orthogonal frequency division multiplexing (OFDM) based centralized SON where multiple indoor cells are connected to a central unit (CU) installed for operation and management as shown in Fig. 1. Thus, indoor base stations (IBS) can share information for cooperation through the CU.

11. Fig. 1

12. Assumptions i) There are N subchannels where each subchannel is independent and used by only one user. ii) Perfect instantaneous channel quality information is available at IBSs and channel quality does not vary during the transmission time of one packet. iii) Interference from a macro base station (MBS) is regarded as additive white Gaussian noise (AWGN) because it is dominated by path loss, so the fast-fading effect on subchannels is negligible. iv) Entire subchannels are classified into normal channels (NCs) and HCs. v) Received signals from the cooperation of multiple normal IBSs on HCs are added up coherently

13. parameters

15. When a user s in an indoor cell m selects a subchannel n for use, the data rate of the subchannel n is written as

16. For NC σ2 contains the effect of interference from the MBS as well as AWGN

17. For HC σ2 contains the effect of interference from the MBS as well as AWGN

18. formulate optimization problem the objective function is to maximize the sum of logarithmic user rates to guarantee user fairness as well as to enlarge user rate. Also, ws is a weight factor for deciding user priority.

19. III. FA-CRA: FAIRNESS-AWARE COOPERATIVE RESOURCE ALLOCATION

20. FA-CRA: FAIRNESS-AWARE COOPERATIVE RESOURCE ALLOCATION finding an optimal solution to the problem is NP hard Thus, we divide the original problem into three subproblems and propose a fairness- aware cooperative resource allocation algorithm.

21. FA-CRA: FAIRNESS-AWARE COOPERATIVE RESOURCE ALLOCATION The sum of logarithmic user rates can be maximized when subchannels are allocated to users according to proportional fair (PF) scheduling [8]. Thus, each IBS uses the PF scheduling for multiple carrier system [9], which is defined as follows. Rm,s is the average rate of user s in indoor cell m.

22. The average user rate and instantaneous transmission rate are considered at the same time in the PF scheduling, so both user fairness and rate can be guaranteed. Therefore, the sum of logarithmic user rates can be maximized by adapting the PF scheduling. Then, the CU can find the subchannel that maximizes the throughput improvement of faulty indoor cells while minimizing the throughput degradation of normal indoor cells from (4). FA-CRA: FAIRNESS-AWARE COOPERATIVE RESOURCE ALLOCATION

30. , is the sum of the interference that an indoor cell m causes to other indoor cells for a subchannel n.

31. For example, when the rate of user s(m, n) at previous slot, Rm,s(m,n), is larger than that of user s(j, n), Rj,s(j,n), indoor cell m reduces the power on subchannel n by increasing to reduce the interference that the indoor cell m causes to the user s(j, n) in indoor cell j. Therefore, in NCs, more power is allocated to a subchannel that has a good SINR value and causes small interference to users in other IBSs.

32. Consequently, the sum of logarithmic user rates can be maximized by the proposed power allocation.

35. IV. SIMULATION RESULTS AND DISCUSSIONS

36. Assumption For the simulations, we assumed that there are four indoor cells whose radius is 10m. In addition, we set Mf =1, Mn=3, N=32, Sm=10, and f=2.3GHz. The bandwidth size was 10MHz and the thermal noise density was -174dBm/Hz. The maximum transmission powers of an IBS and an MBS were 15dBm and 43dBm, respectively.

37. six algorithms FA-CRA : Proposed algorithm. CRA : Normal IBSs allocate subchannels and power in order to maximize sum rate [10]. NRA(non-cooperative resource allocation)-FHC : The set of HCs is fixed and one normal IBS supports users in a faulty IBS. Normal IBSs allocate resources according to (2) and (7). NRA-NH(non-healing) : There are no healing schemes, so users in a faulty IBS cannot be supported. Normal IBSs allocate resources according to (2) and (7). EPA-FHC : The set of HCs is fixed and one normal IBS supports users in a faulty IBS. Normal IBSs use the EPA algorithm [4]. EPA-NH : There are no healing schemes, so users in a faulty IBS cannot be supported. Normal IBSs use the EPA algorithm.

39. Fig. 2 shows the convergence of FA-CRA algorithm according to the variation of the number of users. The FA-CRA algorithm is converged to a meaningful performance within a few number of iterations in indoor system where user mobility is stationary or slow [5] even though the number of users increases. In addition, the converged point of average cell capacity is large as the number of users increases due to the multi-user diversity.

41. Fig. 3 shows the average cell capacity versus the number of HCs. The average cell capacity of healing algorithms, which include FA-CRA, CRA, NRA-FHC, and EPA-FHC, decreases according to the increment of the number of HCs, because the number of available subchannels of normal indoor cells is reduced.

42. Fig. 3 However, in the FA-CRA and the CRA, power control gain on NCs and diversity gain on HCs by the cooperation of normal IBSs compensate for the subchannel loss of normal indoor cells, so the FA-CRA and the CRA outperform the NRA-FHC and the EPA-FHC with respect to the average cell capacity. Since normal IBSs consider user fairness in subchannel and power allocations in the FA- CRA, the average cell capacity of the FA-CRA is degraded within 10% compared with the CRA.

44. Fig. 4 shows the user fairness versus the number of HCs. The fairness increases as the number of HCs increases up to the peak point because users in a faulty indoor cell can be served. However, when the number of HCs increases beyond the peak point, fairness decreases because users in normal indoor cells cannot be supported reliably.

45. Fig. 4 In the FA-CRA, normal IBSs serve users in a faulty indoor cell as well as their own users very well, thereby the fairness of the FA-CRA is larger than that of the NRA-FHC and the EPA-FHC. in the CRA, only a specific user who has the best channel gain can be served mainly because normal IBSs perform resource allocation to maximize sum rate. As a result, the fairness of the CRA is degraded seriously compared with the FA- CRA.

46. Fig. 4 Here, the range of available number of HCs found by the FA-CRA is obtained as RHC=[1,18], and the suboptimal number of HCs is found as nth=9. In this range, the FA- CRA can improve the fairness remarkably with little degradation of system capacity, compared with the NRA-NH and the EPA-NH.

47. V. CONCLUSIONS

48. CONCLUSIONS In this paper, we focused on self-healing to deal with abrupt failure with guaranteeing user fairness in SON based indoor system. To serve all users fairly, we formulated the optimization problem of which objective is to maximize the sum of logarithmic user rate. Based on optimization techniques, we proposed a fairness- aware cooperative resource allocation algorithm. In the proposed algorithm, subchannels and power are allocated in order to guarantee user fairness and rate at the same time.

49. CONCLUSIONS Through simulation results, we showed that the average cell capacity of the FA-CRA is degraded within 10% but its user fairness is improved more than twice compared with the CRA. Consequently, we can conclude that the FACRA can simultaneously enhance network throughput as well as resolve network failure problem effectively.

50. Thanks