Mohammed E. Eltayeb Electrical Engineering Department

1. Opportunistic Scheduling with Limited Feedback in Wireless Communications Systems By Mohammed E. Eltayeb Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) E-mail: melgaily@kfupm.edu.sa Thesis Advisor : Dr. Yahya S. AL-Harthi (1 of 70)

2. Presentation Outline • Opportunistic Communication. • Problem Statement. • Literature Survey. • Thesis Contributions. • OS in Single Carrier Systems. • OS in Multi-Carrier Systems. • Summary. • Future Research.

3. fading channel AWGN channel Tx Rx Opportunistic Communication • Unlike wired channels, wireless channels are random in nature and unpredictable. • The wireless channel is characterized by the variations of the channel coefficients for each user over time and frequency. • These variations occur due to the reflection, diffraction and scattering of EMW as they propagate which results in multiple versions of the transmitted signal (multipath).

4. fading channel AWGN channel Tx Rx Opportunistic Communication (cont’d) • A way to combat this fading is by using any of the classical diversity techniques which provides multiple versions of the transmitted signal at the receiver. • Multiuser diversity is a way to exploit the fading characteristics of the channel by selecting one user from an array of connected users [6]. • Multiuser diversity uses fading to maximize the overall system capacity.

5. Opportunistic Communication (cont’d) • As the number of users increases, multiuser diversity gain also increases. Figure 2.1: Time varying channel of two users undergoing Rayleigh fading.

6. Opportunistic Communication cont’d • Multiuser diversity gain increases with the randomness of the channel. Figure 2.2: Multiuser diversity gain for Rayligh and Ricain fading channels with Rician factor = 5 and average SNR = 0 dB.

7. U1 BS U4 U3 U2 Opportunistic Communication (cont’d) • Schedulers that exploit multiuser diversity are known as opportunistic schedulers as they take the channel conditions into consideration prior any scheduling decision. Requests Requests Grants Grants Requests Requests Grants Grants

8. Opportunistic Communication • Opportunistic schedulers can be classified as fair, semi-fair and greedy. • Opportunistic Round Robin schedulers grant the channel resource to the user with the best channel conditions, but they make sure that all users get an equal share of the resources. • Proportional Fair Schedulers maximizes the system spectral efficiency with a fairness constraint • Greedy algorithms are rate optimal and always schedule the user with the best channel conditions.

9. Opportunistic Communication Figure 2.3: Average spectral efficiency for MCS, PFA, ORR, RR. Figure 2.4: Normalized feedback load for MCS, PFA, ORR, RR.

10. Opportunistic Communication

11. Opportunistic Communication • A good scheduling algorithm should seek these goals • Efficiently utilization resources • Provide fairness • Meet QoS guarantees • Minimize scheduling delay and thus feedback load. • In this thesis, we focus on these goals (except for fairness). • We concentrate on greedy algorithms mainly for two reasons. • Greedy algorithms are rate optimal and thus act as an upper bound in which our algorithms should not deviate from. • Greedy algorithms impose full feedback overhead, as such, any feedback reduction scheme imposed here can be easily implemented on other less greedy schemes.

12. Issues in Scheduling • A good scheduling algorithm should seek these goals • Efficiently utilization resources • Provide fairness • Meet QoS guarantees • Minimize scheduling delay and thus feedback load.

13. Problem Statement • Is it possible to reduce the feedback overhead (load and rate) and guard time requirements imposed by opportunistic schedulers without any significant degradation in spectral efficiency or QoS?

14. Literature Survey • A thorough discussion on the topic of feedback reduction has been conducted in the literature. • Relevant literature can be roughly grouped into the following categories: • Schemes that allow feedback from a group of users only based on an SNR threshold. • Schemes that employ compression of feedback information. • Schemes that feedback quantized values of the SNR instead of the analogue values. • Schemes that use a combination of one or more combination of the above methods.

15. Literature Survey • Gesbert et al., proposed scheduling algorithms that permitted users which had channel qualities above a predetermined threshold to feedback while other users remain silent [9][10][11]. • In [11], Gesbert and Alouni stated that if no user is found with channel quality that exceeded the threshold, a random user is selected. • The work was extended Hassel et al. where full feedback was requested when no user is found. • Hassel et al. further extended the work by using multiple thresholds to reduce the feedback load. • As seen, these schemes reduced the feedback load with the expense of either MUD gain loss or delay.

16. Literature Survey • Floren et. al. focused on feedback quantization to reduce the feedback load. • Floren et. al. showed that only a few quantization levels were required to capture most of the MUD gain. • Sanayei and Nosratina extended showed that only 1 bit of feedback information was able to achieve the optimal capacity growth. • Xue and Kaiser considered imperfect feedback channel and allowed all users which were above a threshold to feedback and then randomly choose a user. • Harthi et. al. considered a probing system with discrete rates. The first user with quantized SNR above the threshold was given the channel resource. This reduced the feedback load in high SNR regime with no loss in spectral efficiency. This work was extended to multicarrier systems in [16].

17. Literature Survey • Other work concentrated on feedback reduction for multi-carrier systems [17]-[20] and [28]-[35]. • Majority of the work focuses on the following • Feedback of either the best or a group of sub-carriers instead of all sub-carriers. • Feedback of information from the best users only. • Feedback of the indices of the strongest clusters only. • Feedback a code that gives an estimate of the current SNR as compared to the SNR of the previous carrier (delta-modulation based). • Feedback compression by exploiting the correlation between neighboring carriers.

18. Thesis Contributions • For Single-Carrier Systems • We introduced an algorithm that reduced the feedback load, rate and guard time. • We derived closed form expressions for the feedback load. • For Multi-Carrier Systems • We presented two algorithms that reduced the feedback load and average guard time requirements. • We derived closed form expressions for the average spectral efficiency for both algorithms. • We derived closed form expressions for the feedback load for both algorithms.

19. A Single Carrier Scenario

20. System Model • In this section we assume the following • A probing based system with a single antenna and K i.i.d. users employing discrete rates. • The channel is assumed to be experiencing flat Rayleigh fading. • Scheduling is performed at the downlink with perfect feedback channel. • The system can detect collisions.

21. Algorithm Description • In this section we detailed description of our proposed 1-bit feedback scheme and compare it with the optimal and DSMUDiv schemes [8]. • The scheduler (base station) sequentially arranges all the users and assigns each user a unique ID. • The base station broadcasts a query message (first stage) to all users with the highest threshold level, allowing all users that are above or equal to the threshold to feedback a 1 in one mini slot with probability p= 1. • If no user feeds back, the threshold is sequentially lowered until at least one user feeds back or the lowest threshold level has been reached. • If one or more users feedback a 1, then the scheduler knows that at least one user has an SNR lying within the broadcasted threshold level and goes into the second stage (search mode).

22. Algorithm Description (cont’d) • The scheduler then randomly probes two users and allows them to contend for a new mini slot with probability p = 1. • Assuming that the probing request is heard by all users, the user with the higher index number will feedback a 1 (lower will feedback -1) if it has an instantaneous SNR above or equal to the threshold. If the SNR is below the threshold, the user will remain silent. • If one of the users feeds back successfully, then the user is identified and given the channel resource. • If a collision occurs, then the channel resource is given randomly to any of the two users. • If none of the two users respond, then another set of two users are allowed to contend for another mini-slot.

23. Algorithm Description (Optimal Scheme) Probing threshold q(2) q(4) q(3) γ(4) User 2 DATA γ(3) 19

24. Algorithm Description (DSMUDiv Scheme [8]) Probing threshold q(2) q(4) γ(4) User 2 DATA

25. 1-Bit Binary Feedback Algorithm Description Stage 1 Stage 2 1 1 User 3 DATA Probing threshold γ(4) γ(4) γ(3) Stage 2

26. Performance Analysis • The average spectral efficiency is the average transmitted data rate per unit bandwidth (bits/sec/Hz). where, is the is the cumulative distribution function (CDF). • The average feedback load (AFL) is defined as the average number of consumed mini-slots until a user is scheduled. The feedback load in this case consists of two terms:

27. Performance Analysis (cont’d) where, and given that and

28. Performance Analysis (cont’d) • Considering feedback traffic degradation, we define the average system capacity • as (bits/channel use): • Considering the guard time effect, we define the average • system throughput as the amount of bits transmitted per unit time (bits/sec/Hz) • The guard time duration is the time duration of the consumed minislots until a • scheduling decision has been made. It is expressed as:

29. System Parameters

30. Numerical Results

31. Numerical Results(cont’d)

32. Numerical Results (cont’d)

33. Numerical Results (cont’d)

34. Conclusion • As the number of users increase, feedback load and guard time requirements increase. • Transmission of data over short time slots to a large number of users degrades the system performance. • We introduced a scheduling scheme that: • Reduced feedback rate due to one bit feedback. • Reduced feedback load with no loss in average spectral efficiency when compared to the optimal scheme. • Improved system throughput due to reduced scheduling delay. • Improved system capacity due to reduced feedback load and rate.

35. A Multi-Carrier Scenario

36. System Model • Multi-carrier systems employing opportunistic scheduling impose heavy feedback overhead. • In this section, we introduce two algorithms that reduce the feedback load and guard time requirements and then we compare our results with the optimal, DSMUDiv and DSMUDiv-EEA schemes.

37. System Model (cont’d)

38. Algorithm 1 Description (Contribution 2)

39. Algorithm Description - (DSMUDiv-EEA Scheme [16]) Probing threshold γ(4) Time slot q(2) q(3) q(1) User 2 DATA γ(3) CH #1 CH #2 CH #3 q(2) q(4) User 3 DATA q(1) User 1 DATA 19

40. MC-ALGO 1 (Contribution 2) Probing threshold γ(1) γ(2) Time slot q(2) γ(3) User 1 DATA γ(3) CH #1 CH #2 CH #3 γ(4) q(2) q(4) User 3 DATA q(1) User 2 DATA 19

41. Algorithm Description (cont’d)

42. Algorithm 2 Description (Contribution 3)

43. Performance Analysis

44. Performance Analysis (cont’d)

45. Performance Analysis (cont’d)

46. Performance Analysis - Sub-state steady-state probability forAlgorithm 1 • In order to derive the average feedback load and average spectral efficiency, we need to derive the steady-state probability of being in substate w(n, k). • The probability of being in substate w(n, k) (steady-state probability) is :

47. Performance Analysis - Sub-state steady-state probability forAlgorithm 1 (cont’d)

48. Performance Analysis - Sub-state steady-state probability forAlgorithm 2 The probability of being in substate w(n, k) (steady-state probability) is :

49. Performance Analysis - Sub-state steady-state probability forAlgorithm 2 (cont’d)

50. Performance Analysis (Feedback Load) • The average feedback load (AFL) is defined as the average number of probes sent until the subchannel is assigned. The average feedback load conditioned on k and n is [8]: