1 Cross-Layer Design for Wireless Communication Networks Ness B. Shroff Center for Wireless Systems and Applications (CWSA) School of Electrical and Computer Engineering Purdue University, West Lafayette, IN URL:

Wireless vs. Wireline Networks Wireline systems Reliable channel and very high bandwidth Core router: Gbps - Tbps Requirement: simplicity and scalability Wireless systems Limited natural resource (radio frequency) 3G: up to 2Mbps, WLANs: ~100Mbps Requirement: spectrum efficiency

Cross-Layer Design To satisfy the increasing demand for wireless data capacity, a cross-layer perspective needs to be taken to improve wireless spectrum efficiency Network MAC Physical Transport

Cross-Layer Design To satisfy the increasing demand for wireless data capacity, a cross-layer perspective needs to be taken to improve wireless spectrum efficiency Network MAC Physical Transport Focus of this talk: Opportunistic Scheduling in cellular systems

Characteristics of Wireless Networks Wireless environment is heterogeneous and Interference-prone

Characteristics of Wireless Networks Time-Varying Channel Conditions Reason: Mobility and the Propagation environment Path loss (e.g., signal strength attenuates as D  ) Shadowing or slow-fading (e.g. log-normal shadowing with spatial correlation) Fast-fading or multipath fading (e.g., Rayleigh or Ricean) Both received signal and interference are time varying SINR (Signal to Interference plus Noise Ratio) is a measure of channel quality:

Opportunistic Scheduling Under time-varying channel conditions, which user should be chosen to transmit? Naive Approach: Schedule user with the best channel. Our objective will be to schedule users in an opportunistic way (exploits channel variability) and at the same time satisfy QoS requirements.

Performance Measure Different applications differ in how they can utilize the channel The performance measure is based on a unifying metric, using the notion of the utility value (or reward) to that user Examples of Utility: Throughput; value of throughput; value of throughput – cost of power, etc.

Opportunistic Scheduling U i k = utility value (function of SIR) of user i if it is scheduled at time k Objective: Maximize the sum of all users utility values by opportunistic scheduling while satisfying the QoS requirements of users Scheduling decision depends on: Channel conditions QoS requirements.

QoS Requirements Under our framework, we can consider a variety of fairness/performance requirements Temporal fairness requirement Utilitarian fairness requirement Minimum-performance requirement Combinations of the above requirements

A Case Study: Temporal Fairness N : the number of users in the cell r i = fraction of time assigned to user i with Given U k =( U 1 k, , U N k ), decide who should take time- slot k ? Define a policy Q as a mapping from the utility vector space to the index set { 1,…, N } Given U k, if Q ( U k ) = i, then user i is assigned to the time slot k Objective: Maximize average utility subject to the fairness constraints r i

Scheduling Problem Formulation The optimal scheduling problem with temporal fairness where  : the set of all scheduling policies

An Optimal Scheduling Policy A policy Q ( U ) = argmax i U i will maximize the system utility, but may not meet each user’s fairness requirement. The v i ’s are “off-sets” used to achieve the fairness requirement The coupling needed between layers to balance fairness and efficiency! The optimal policy is given in a very simple form!

“No Loss” Property The average utility of every user in our scheduling scheme will be at least that of any non-opportunistic scheduling scheme. The opportunistic scheduling scheme does not sacrifice some users for overall optimal performance.

Parameter Estimation We can estimate v i * based on measurements of the channel using stochastic approximation. v i k → v i * w.p.1 under appropriate conditions (e.g., a k = 1 / k ).

Scheduling Procedure Basic Idea: Set initial value of v i 0. The initial value can be set to 0 or some estimate based on history information At each time slot, the system performs the following: Estimates U i k Uplink: the base station estimates each user’s channel condition and calculates the values of U i k Downlink: user i measures its channel condition, calculates U i k, and informs the base station

Scheduling Procedure (Cont’d) The base station decides which user should take the time slot based on the scheduling policy: The base station updates the parameter v k  by For downlink, the base station transmits to the chosen user For uplink, the base station broadcasts the ID of the selected user and the selected user transmits in the time slot

Scheduling Procedure (cont’d)

System Performance Our scheduling procedure is efficient, fair and robust against estimation errors

Summary on Opportunistic Scheduling Typical performance improvements with strict fairness are around 50~100% Specific values not critical The users’ performance values are uniformly better Opportunistic gains increase with Level of user elasticity channel variability (over time) number of users negative correlations

Discussion (Cont’d) Further improvement by relaxing the fairness constraint Similar type of myopic index policy is optimal in many cases Simple to implement Easily extended to include short term fairness Appears to be robust to estimation errors Opportunistic scheduling can be combined with other resource allocation strategies (power control, rate control, etc.)

Discussion (Cont’d) Traditional setting: performance of system depends on average channel conditions. Cross-Layer (Opportunistic) setting: performance of system depends on peak channel conditions. Significantly improve efficiency (especially for delay tolerant users)

Discussion (Cont’d) No Free Lunch Signaling costs Each user needs to maintain a signaling channel Signaling costs increase linearly with the number of users Channel Estimation Errors Feedback Delay Time-Scale of fluctuation Scheduling gain vs. short-term fairness Opportunistic Scheduling is important for future wireless systems (Qualcomm, Flarion, etc.)

Cross Layer Design Opportunistic Scheduling: MAC & PHY Many other Cross-Layer Design Issues MAC Interaction with Transport Protocols TCP Congestion Control and MAC layer Security/energy need to be considered across multiple layers Multi-hop wireless Networks Joint Scheduling (link) and Congestion Control (end-to-end) results in significant gains Energy Efficient Routing, synchronization…

Potential: cross-layer gains are multiplicative Key to Success: Cross-layer solutions should be loosely coupled across the layers such that high performance gains are achieved without a complete loss of modularity. Conclusion