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Adaptive Resource Allocation Algorithm for Multiuser Mimo-Ofdm Systems

Adaptive Resource Allocation Algorithm for Multiuser Mimo-Ofdm Systems. Presented By: Mohammed Akber Ali, ID: G200806120. Overview. Introduction-Basic Definitions Scheduling Optimization problem Conventional method of solving problem Optimal Algorithm proposed

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Adaptive Resource Allocation Algorithm for Multiuser Mimo-Ofdm Systems

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  1. Adaptive Resource Allocation Algorithm for Multiuser Mimo-Ofdm Systems Presented By: Mohammed Akber Ali, ID: G200806120.

  2. Overview • Introduction-Basic Definitions • Scheduling • Optimization problem • Conventional method of solving problem • Optimal Algorithm proposed • Analyzing results for OFDMA system • Extending the algorithm to Multiuser MIMO-OFDM • Results

  3. INTRODUCTION: • OFDMA: (i). OFDMA also employs multiple sub-carriers as OFDM, where sub-carriers are divided into groups of sub-carriers known as sub-channels. (ii). In a downlink scenario, a sub-channel may be intended for different receivers.

  4. User 1 User 2 User K INTRODUCTION • MIMO: -Spatial multiplexing: Several data are simultaneously transmitted over multiple antennas, increasing the capacity drastically. -Space-time coding: Same data is transmitted over multiple antennas, to combat signal fading by increasing diversity. • Multiuser MIMO-OFDM : - Further increases the systems capacity, by scheduling multiple users to share the same spatial channel simultaneously.

  5. SCHEDULING: Periodically selecting the best user to serve in order to improve the system performance is Scheduling

  6. Resource Allocation Problem: • Task is to Distribute frequency subchannels and total power among various users. Such that they maximize the overall network throughput specified by:

  7. Resource Allocation Problem: Constraints to be satisfied by an resource allocation algorithm: • The total power constraint –that should assure, Where pk,n is power allocation for (k,n) subchannel. • The sub channel allocations Ωk’s for different users must be mutually exclusive, disjoint : • The proportional rate constraints are to be satisfied for a promised QoS,  introducing a level fairness among users: R1/ γ1=R2/ γ2... =RK / γK. are proportional rate constraint constants (for QOS).

  8. Conventional Method for solving optimization problem: , , • In general to solve such problem, a cost function is constructed from a set of Lagrange multipliers as done by shen et al.[3]: • Total power assigned to the kth user is given by, The constants Vk and Wk are given by: • Using the derived cost function the total power allocation (Pk,total) for a particular user can be found, gives us the power allocations for the individual subchannels. • These power allocations are made in such a way that we can achieve maximum capacity, satisfying all constraints.

  9. Conventional Method for solving optimization problem: Therefore, • Use of equally weighted capacity sum as the optimizing function, • Introducing the scheme of proportional fairness among users, Gives us a benefit of explicitly controlling the capacity ratios among various users, while ensuring each user his maximum data rate. Considering the complexity of the cost function, many researchers (Rhee et al.[2] & Shen et al.[3]) , tried solving a simplified version of it, - Assuming channel power gains to be large and similar, - Thus, the proportional rates constraint is not satisfied in strict sense.

  10. Optimal Algorithm Proposed • In 2008, Dr. Ashraf et al.[1] proposed an algorithm that - Solves the utility function without making any assumption about the channel power gains & - Satisfies the proportional rates constraint in strict sense. • The proposed algorithm in [1]: Modifys the set of subchannel allocations Ωk's for a given user k, by dropping weak channels untill a valid solution for optimization problem is obtained. • The obtained solution -Maximizes the system throughput, - Guarantees that the provided users rates Rk’s satisfy the proportional rates constraint in strict sense, such that R1: R2: ...: RK = γ1: γ2: ...: γK.

  11. Analyzing Results in [1]: The results were reproduced under conditions similar to that in paper [1], • Considering a frequency selctive multipath channel modeled as 6 independent Rayleigh multipaths, • Total power available is assumed to be1 Watt, • Noise PSD of 65 dBW , • The Overall Bandwidth of 1 MHz, divided among 64 sub-channels.

  12. Analyzing Results in [1]: The optimal scheduling algorithm proposed in [1] provides significantly higher capacity than that proposed in [3].

  13. Analyzing Results in [1]: Jain’s Fairness Index [1]: If the proportionality rate constraints are satisfied in strict sense then all Гk’s =1 => fairness index nearly equals 1 Therefore, The algorithm proposed in [1] satisfies the proportional rates constraints in strict sense.

  14. Extending- To Multiuser MIMO-OFDM system: • The problem of resource allocation in a multiuser MIMO-OFDM system is formulated similar to that of OFDMA system, But is more challenging & complicated due to multiple antennas. • Channel Model:For Mt transmit & Mr receive antennas. Where, is power gain relative to noise power from Mtthtransmit antenna & Mrth receive antenna for kth user over nth subcarrier.

  15. Extending- To Multiuser MIMO-OFDM system: • In a MU-MIMO-OFDM system the power & subcarrier allocations should maximize the overall network throughput, given by: • While meeting all the constraints specified for an OFDMA system, The algorithm proposed in [1], is now used to solve the optimization problem of a multiuser MIMO-OFDM system. • Results are obtained under similar conditions, Ptotal=1Watt, Total bandwidth=1MHz, divided among 64 sub channels, for a noise PSD of 65dBW. • For Mr=2&4,Mt=2&4i.e. a 2 x2 & 4 x 4 MIMO casesare considered.

  16. RESULTS: As expected the capacity increases drastically for a MU-MIMO-OFDM system when compared to OFDMA system.

  17. RESULTS: As expected a MU-MIMO-OFDM system has higher capacity when compared to OFDMA system.

  18. RESULTS: Proportional rates constraints are also satisfied in strict sense, as in case of an OFDMA system.

  19. Conclusion: • The numerical analysis of the optimal algorithm proposed in [1] (for OFDMA) shows that it outer performs and satisfies fairness constraint in strict sense. • The results in that, capacity increases drastically when the channel is replaced with that of a multiuser MIMO OFDM channel. The capacity can be improved further by increasing number of receiving and transmitting antennas (as demonstrated by a 4 x4 MIMO-OFDMA system).

  20. References: • Ashraf S. Mahmoud, Ali Y. Al-Rayyah, and Tarek R. Sheltami - “Adaptive Power Allocation Algorithm to Support Absolute Proportional Rates Constraint for Scalable OFDM Systems”. IEEE 71st VTC-Spring May 2010. • W. Rhee and M. Cioffi, “Increase in capacity of multiuser OFDM system using dynamic subchannel allocation,” in Proc. IEEE Vehicular. Technology Conference (VTC 2000), May 2000, pp. 1085-1089. • Z. Shen, J. G. Andrews, and B. L. Evans, ”Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints,” IEEETrans. Wireless Commun., vol. 4, no. 6, Nov. 2005, pp. 2726-2737. • S. Sadr, A. Anpalagan, and K. Raahemifar, ”Suboptimal Rate Adaptive Resource Allocation for Downlink OFDMA Systems,” Int. J. of Veh.Technol., vol 2009, Art 891367, 10 pages doi:10.1155/2009/891367. • Yang Hu, Changchuan Yin and GuangxinYue -“Multiuser MIMO-OFDM with Adaptive Antenna and Subcarrier Allocation”. VTC Spring 2006: 2873-2877.

  21. Thank you !

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