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Cross-Layer Resource Allocation in Multi-access Wireless Network: the Problems and One Solution

Z. Han Carleton University 07/10/03. Outline. Cross Layer Resource Allocation for Multi-usersWhat causes the problemsWhy cross layer approachesHow to formulate the problemsFour possible categories of solutionsFurther performance improvement techniquesPower Minimization under Throughput Managem

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Cross-Layer Resource Allocation in Multi-access Wireless Network: the Problems and One Solution

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    1. Cross-Layer Resource Allocation in Multi-access Wireless Network: the Problems and One Solution Zhu Han Department of Electrical and Computer Engineering, University of Maryland, College Park. At Carleton University 07/10/2003

    2. Z. Han Carleton University 07/10/03 Outline

    3. Z. Han Carleton University 07/10/03 What causes the problems

    4. Z. Han Carleton University 07/10/03 Why cross layer approaches Traditional Resource Optimization is Designed in Layers. Application layer: multimedia source coder Network layer: base station assignment, handoff, routing MAC layer: queuing, adaptive rate and coding, QoS Physical layer: power control, antenna array processing Shortcomings Layers don’t have knowledge with each other: local optima Overhead associated between layers becomes precious. Advantages and Disadvantages of Cross Layer Approach Optimal from resource management point of view Suboptimal from implementation point of view: complex, nonlinear, non-convex, hard to model, and hard to implement.

    5. Z. Han Carleton University 07/10/03 Examples for cross layer problems Joint Source Channel Coding Distortion Based Resource Allocation for Multi-access Networks Joint Power Control, Beamforming and Base Station Assignment Dynamic Cell Management Routing for Ad-hoc Wireless Network with Power Constraint Joint Power Control and Handoff Technique Adaptive Resource Allocation with Buffer or Delay Constraint Resource Management in Packet Access Systems Joint Power Control and Rate Adaptation OFDMA Channel Allocation with Throughput Constraint

    6. Z. Han Carleton University 07/10/03 Resources, constraints, and problems Resources (Parameters): ? Transmitted power, rate (source rate, channel rate, symbol rate), base station, channel, antenna weight vector.... Constraints: ? Maximal transmitted power Maximal delay Optimization Goals: ? Overall throughput, overall transmitted power, average distortion, maximum outage rate, overall QoS, or multiple objectives. General Problem Formulation:

    7. Z. Han Carleton University 07/10/03 Possible solution: analysis Approximation and Simplification Make the problems to be linear or convex. Methods Lagrange multiplier Convex optimization Results Clean analytical results Fast converged algorithms. Advantages and Disadvantages Beautiful mathematics, claimed “optima”, reviewers’ favorite Performance is highly related to how good the approximation and simplification to the reality. (How far away from truth?)

    8. Z. Han Carleton University 07/10/03 Possible solution: optimal control Constrained Optimization Problem Nonlinear programming Integer programming Advantages and Disadvantages Close to reality Fast, no iteration between BS and users is needed. Centralized control Multiple optima: multiple initializations or annealing Full knowledge of channel conditions High complexity with large number of parameters and users Performance bound. Only fit the centralized system with a small number of users, such as a CDMA micro cell. Distributed Implementation: Pricing, Simple Cases

    9. Z. Han Carleton University 07/10/03 Possible solution: game theory Non-cooperative Game Individual mobile users don’t have knowledge of other users’ conditions and cannot cooperate with others, they act selfishly to maximize their own performances in a distributed manner. Non-cooperative game deals largely with how rational and intelligent individuals interact with each other in an effort to achieve their own goals. Utility functions, Games, and Nash Equilibriums. Nash Equilibriums May Not Be Efficient for System Goal: design meaningful utility functions and game rules, so that the system is balanced in the desired social optimal equilibrium. Pricing: develop a mediator between the users’ interests and the system efficiency. “demand and supply” rule. Repeated Game: Act by the rules to avoid future punishments. Cooperative Game: Bargain. Like a company

    10. Z. Han Carleton University 07/10/03 Possible solution: dynamic programming Optimization over Different Time. Dynamic programming: make the optimal decisions over time, based on the distributions of the channels or the sources. Scheduling: A Special Case of Dynamic Programming Tradeoff for the fairness (delay) and the system performance. Advantages and Disadvantages Users might not be optimized at a specific time. But their sacrifices of performances will increase the overall system performance and will be compensated back in the future. Distributions for decision in a multi-user case are hard to obtain, High computation complexity Only fit single user, simple channel condition case.

    11. Z. Han Carleton University 07/10/03 Pro and Con for different solutions

    12. Z. Han Carleton University 07/10/03 Further performance improvements Antenna Array Processing Multi-User Detection Space Time Processing MIMO System Adaptive BICM, LDPC Coding OFDM Channel Assignment Memo for the resource allocation over the crowded wireless networks

    13. Z. Han Carleton University 07/10/03 Introduction Power Minimization under Throughput Management over Wireless Networks with Antenna Diversity Time Varying Nature and Co-Channel Interferences Power control and Adaptive modulation Minimize the Overall Transmitted Power of Networks. No reduction for the overall network throughput Fairness: user’s time average throughput is assured Heuristically Divide the Problem into Two Sub-problems at the User Level and at the System Level, Respectively. “Water Filling” Each User’s Throughput in Time Domain and Allocating Network Throughput to Different Users Each Time.

    14. Z. Han Carleton University 07/10/03 Motivation

    15. Z. Han Carleton University 07/10/03 System model Multi-cell, One User per Cell, TDMA/FDMA, Uplink Antenna Diversity Maximal Ratio Combine and Selective Combine Received SINR Adaptive Modulation: MQAM

    16. Z. Han Carleton University 07/10/03 Traditional power control Problem Formulation Problems A fixed and predefined targeted SINR threshold . Works perfectly in low SINR areas. Powers increase quickly when the threshold is higher. No feasible solution if the threshold is too high. A user with a bad channel causes too much CCI. Adapt the thresholds, according to channel conditions.

    17. Z. Han Carleton University 07/10/03 Proposed problem formulation Problem Formulation Difficulties Bilinear Matrix Inequality Involve complicated dynamic programming

    18. Z. Han Carleton University 07/10/03 Problem partition Problem Partition into the User Level and the System Level Users adapt and report acceptable throughput ranges, according to their transmission histories and current channel conditions. If less throughput now, more aggressive to transmit, higher range in the future, so that fairness is maintained. Channel conditions are also considered System determines the optimal throughput allocation to min P, within these throughput ranges. Two-user Example

    19. Z. Han Carleton University 07/10/03 User Throughput range algorithm Idea: Credit System. Moving Throughput Window If assigned lower throughput, the user accumulates credit, so that he can aggressively transmit by providing higher throughput windows in the future when the channel becomes good. High throughput, credit is used up, so less aggressive and smaller throughput windows. Fairness is maintain. Change the Throughput Windows according to Channel Trends. Throughput smaller than that of the adjacent cells, still in bad channel condition and report lower throughput window, vice versa. Extreme Case Analysis Trapped in bad channels, users can provide lowest throughput range to wait for channels to become better. Proof to Guarantee Fairness

    20. Z. Han Carleton University 07/10/03 User Throughput range algorithm Initialization: Iteration: If all adjacent CCI cells, Else Feedback the acceptable throughput range back to BS: If report Else report

    21. Z. Han Carleton University 07/10/03 System Throughput allocation algorithm 1 Full Search Algorithm Adaptive Modulation Search all possible throughput combinations subject to constraints. Find the combination that minimizes the overall power. Iteration Powers are initialized by any feasible values. Antenna diversity Power update Throughput Range Update Update Too Complex, Only for Comparing Performances.

    22. Z. Han Carleton University 07/10/03 System Throughput allocation algorithm 2 Fast Search Algorithm Find the gradient of Psum with respect to user’s target SINR. For the user with the largest gradient, Find the throughput that generates the lowest overall transmitted power subject to the constraints. When the throughput of the user with the largest gradient is changed, the throughput of the other users is modified in the order from the lower gradient to higher gradient to compensate the network throughput constraint T=R. More throughput is allocated to the users with small gradients, and less throughput is assigned to the users with large gradients. Simple but may be sub-optimal.

    23. Z. Han Carleton University 07/10/03 System Throughput allocation algorithm 3 Projected Gradient Method Nonlinear programming by assuming throughput is continuous Constrained projected gradient method Projected the continuous throughput to the discrete values. More complex than fast search, can find optimal solutions

    24. Z. Han Carleton University 07/10/03 Simulation results

    25. Z. Han Carleton University 07/10/03 Simulation results

    26. Z. Han Carleton University 07/10/03 Simulation results

    27. Z. Han Carleton University 07/10/03 Conclusions Cross Layer Resource Allocation for Multi-access Wireless Networks For each user, cross layer approach can increase the performance. For whole system, clearly managing different users’ resources can increase the system performance and reduce unnecessary CCI. The desired scheme should have easy, (sub-)optimal, and distributed implementation, without requiring too much information. Joint Power and Throughput Optimization Fairness for the services that the users have paid for. “Water filling” each user’s throughput in time domain and allocating the network throughput to different users each time. 7 dB gain for powers, 1.2 bit/s/Hz gain for spectrum efficiencies.

    28. Z. Han Carleton University 07/10/03 My Other Works Game Theory and Economy Approach Distortion Based CDMA Joint Source-Channel Coding Estimation Blind Estimation Bio Image Processing Time of Arrival Beamforming Tutorial Paper Channel Allocation for OFDMA

    29. Z. Han Carleton University 07/10/03 Self-advertisement Journal Papers Zhu Han and K.J.Ray Liu, "Joint Adaptive Link Quality and Power Management with Fairness Constraint over Wireless Networks", Submitted to IEEE Transactions on Vehicular Technology. Zhu Han and K.J.Ray Liu, "Power Minimization under Throughput Management over Wireless Networks with Antenna Diversity", Revision, IEEE Transactions on Wireless Communications. Zhu Han and K.J.Ray Liu, "Joint Power Control and Blind Beamforming over Wireless Networks: A Cross Layer Approach", Submitted to Eurasip. Zhu Han and K.J.Ray Liu, "Non-Cooperative Power Control Game and Throughput Game over Wireless Networks", Submitted to IEEE Transactions on Communications. Zhu Han, Jane Wang and K.J.Ray Liu, "A Resource Allocation Framework with Credit System and User Autonomy Over Heterogeneous Wireless Networks ", in Preparation. Zhu Han, Farrokh Rashid-Farrokhi and K.J.Ray Liu, "A Tutorial for Cross Layer Resource Allocation in Wireless Networks: Problems, Techniques and Solutions", in Preparation. Zhu Han, Andres Kwasinski, Mehdi Alasti, K.J.Ray Liu, and Nariman Farvardin, "Downlink Resource Allocation for Multi-cell CDMA Networks Based on Real-Time Dynamic Joint Source Channel Coding", in Preparation. Jane Wang, Zhu Han and K.J.Ray Liu, "MIMO-OFDM Channel Estimation via Probabilistic Data Association Based TOA Estimation", in Preparation. Jane Wang, Zhu Han and K.J.Ray Liu, "DCE-MRI Based Tumor Heterogeneity Characterization: Simultaneous Estimation of Kinetic Parameters and Input Function", in Preparation.

    30. Z. Han Carleton University 07/10/03 Self-advertisement Conference Papers Zhu Han and K.J.Ray Liu, "Non-Cooperative Power Control Game and Throughput Game over Wireless Networks”, submitted to Infocom 2004. Zhu Han, Andres Kwasinski, and K.J.Ray Liu, “Pizza Party Algorithm for Distortion Management in Downlink Single-Cell CDMA Systems”, submitted to Allerton Conference, 2004. Zhu Han, Jane Wang and K.J.Ray Liu, "A Resource Allocation Framework with Credit System and User Autonomy Over Heterogeneous Wireless Networks",  accepted for Globecom 2003. Jane Wang, Zhu Han and K.J.Ray Liu, "MIMO-OFDM Channel Estimation via Probabilistic Data Association Based TOA Estimation",  accepted for Globecom 2003. Zhu Han and K.J.Ray Liu, "Joint Power Control and Blind Beamforming in Wireless Networks", ICC 2003. Zhu Han and K.J.Ray Liu, "Throughput Maximization Using Adaptive Modulation in Wireless Networks with Fairness Constraint ", WCNC 2003. Andres Kwasinski,, Zhu Han and K.J.Ray Liu, "Power Minimization under Real-Time Source Distortion Constraints in Wireless Networks", WCNC 2003. Zhu Han and K.J.Ray Liu, "Power Minimization Under Constant Throughput Constraint in Wireless Networks with Beamforming ", VTC fall, 2002. Zhu Han and K.J.Ray Liu, "Joint Adaptive Power and Modulation Management in Wireless Networks with Antenna Diversity", SAM 2002. Zhu Han and K.J.Ray Liu, " Adaptive Coding for Joint Power Control and Beamforming Over Wireless Networks", SPIE 2002. Zhu Han and K.J.Ray Liu, "Adaptive SINR Threshold Allocation for Joint Power Control and Beamforming over Wireless Networks", VTC fall, 2001.

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