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Order optimal delay for opportunistic scheduling in multi-user wireless uplinks and downlinks

by Michael J. Neely - Proc. of Allerton Conf. on Communication, Control, and Computing (invited paper , 2006
"... Abstract — We consider a one-hop wireless network with independent time varying channels and N users, such as a multiuser uplink or downlink. We first show that general classes of scheduling algorithms that do not consider queue backlog necessarily incur average delay that grows at least linearly wi ..."
Abstract - Cited by 45 (6 self) - Add to MetaCart
Abstract — We consider a one-hop wireless network with independent time varying channels and N users, such as a multiuser uplink or downlink. We first show that general classes of scheduling algorithms that do not consider queue backlog necessarily incur average delay that grows at least linearly with N. We then construct a dynamic queue-length aware algorithm that stabilizes the system and achieves an average delay that is independent of N. This is the first analytical demonstration that O(1) delay is achievable in such a multi-user wireless setting. The delay bounds are achieved via a technique of queue grouping together with basic Lyapunov stability and statistical multiplexing concepts.

Cross-Layer Latency Minimization in Wireless Networks with SINR Constraints

by Deepti Chafekar, V.S. Anil Kumar , Madhav V. Marathe , Srinivasan Parthasarathy, Aravind Srinivasan - MOBIHOC’07, SEPTEMBER 9–14, 2007, MONTREAL, QUEBEC, CANADA , 2007
"... Recently, there has been substantial interest in the design of cross- layer protocols for wireless networks. These protocols optimize certain performance metric(s) of interest (e.g. latency, energy, rate) by jointly optimizing the performance of multiple layers of the protocol stack. Algorithm desig ..."
Abstract - Cited by 40 (2 self) - Add to MetaCart
Recently, there has been substantial interest in the design of cross- layer protocols for wireless networks. These protocols optimize certain performance metric(s) of interest (e.g. latency, energy, rate) by jointly optimizing the performance of multiple layers of the protocol stack. Algorithm designers often use geometric-graph- theoretic models for radio interference to design such cross-layer protocols. In this paper we study the problem of designing cross- layer protocols for multi-hop wireless networks using a more real- istic Signal to Interference plus Noise Ratio (SINR) model for radio interference. The following cross-layer latency minimization prob- lem is studied: Given a set V of transceivers, and a set of source- destination pairs, (i) choose power levels for all the transceivers, (ii) choose routes for all connections, and (iii) construct an end-to-end schedule such that the SINR constraints are satisfied at each time step so as to minimize the make-span of the schedule (the time by which all packets have reached their respective destinations). We present a polynomial-time algorithm with provable worst-case performance guarantee for this cross-layer latency minimization problem. As corollaries of the algorithmic technique we show that a number of variants of the cross-layer latency minimization prob- lem can also be approximated efficiently in polynomial time. Our work extends the results of Kumar et al. (Proc. SODA, 2004) and Moscibroda et al. (Proc. MOBIHOC, 2006). Although our algo- rithm considers multiple layers of the protocol stack, it can natu- rally be viewed as compositions of tasks specific to each layer — this allows us to improve the overall performance while preserving the modularity of the layered structure.

Super-Fast Delay Tradeoffs for Utility Optimal Fair Scheduling in Wireless Networks

by Michael J. Neely , 2006
"... We consider the fundamental delay tradeoffs for utility optimal scheduling in a general network with time varying channels. A network controller acts on randomly arriving data and makes flow control, routing, and resource allocation decisions to maximize a fairness metric based on a concave utilit ..."
Abstract - Cited by 34 (17 self) - Add to MetaCart
We consider the fundamental delay tradeoffs for utility optimal scheduling in a general network with time varying channels. A network controller acts on randomly arriving data and makes flow control, routing, and resource allocation decisions to maximize a fairness metric based on a concave utility function of network throughput. A simple set of algorithms are constructed that yield total utility within O(1/V) of the utilityoptimal operating point, for any control parameter V> 0, with a corresponding end-to-end network delay that grows only logarithmically in V. This is the first algorithm to achieve such “super-fast” performance. Furthermore, we show that this is the best utility-delay tradeoff possible. This work demonstrates that the problem of maximizing throughput utility in a data network is fundamentally different than related problems of minimizing average power expenditure, as these latter problems cannot achieve such performance tradeoffs.
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Delay Reduction via Lagrange Multipliers in Stochastic Network Optimization

by Longbo Huang, Michael J. Neely , 2009
"... In this paper, we consider the problem of reducing network delay in stochastic network utility optimization problems. We start by studying the recently proposed quadratic Lyapunov function based algorithms (QLA). We show that for every stochastic problem, there is a corresponding deterministic prob ..."
Abstract - Cited by 33 (15 self) - Add to MetaCart
In this paper, we consider the problem of reducing network delay in stochastic network utility optimization problems. We start by studying the recently proposed quadratic Lyapunov function based algorithms (QLA). We show that for every stochastic problem, there is a corresponding deterministic problem, whose dual optimal solution “exponentially attracts” the network backlog process under QLA. In particular, the probability that the backlog vector under QLA deviates from the attractor is exponentially decreasing in their Euclidean distance. This not only helps to explain how QLA achieves the desired performance but also suggests that one can roughly “subtract out ” a Lagrange multiplier from the system induced by QLA. We thus develop a family of Fast Quadratic Lyapunov based Algorithms (FQLA) that achieve an [O(1/V), O(log 2 (V))] performance-delay tradeoff for problems with a discrete set of action options, and achieve a square-root tradeoff for continuous problems. This is similar to the optimal performance-delay tradeoffs achieved in prior work by Neely (2007) via drift-steering methods, and shows that QLA algorithms can also be used to approach such performance. These results highlight the “network gravity ” role of Lagrange Multipliers in network scheduling. This role can be viewed as the counterpart of the “shadow price” role of Lagrange Multipliers in flow regulation for classic flow-based network problems.
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Utility optimal scheduling in energy harvesting networks

by Longbo Huang, Michael J. Neely - Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC , 2011
"... ar ..."
Abstract - Cited by 25 (3 self) - Add to MetaCart
Abstract not found
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Analysis of energy efficiency in fading channel under QoS constrains

by Deli Qiao, Mustafa Cenk Gursoy, Senem Velipasalar, Deli Qiao, Mustafa Cenk Gursoy, Senem Velipasalar - IEEE Global Communications Conference (GLOBECOM , 2008
"... Abstract — 1 Energy efficiency in fading channels in the pres-ence of QoS constraints is studied. Effective capacity, which provides the maximum constant arrival rate that a given process can support while satisfying statistical delay constraints, is considered. Spectral efficiency–bit energy tradeo ..."
Abstract - Cited by 18 (11 self) - Add to MetaCart
Abstract — 1 Energy efficiency in fading channels in the pres-ence of QoS constraints is studied. Effective capacity, which provides the maximum constant arrival rate that a given process can support while satisfying statistical delay constraints, is considered. Spectral efficiency–bit energy tradeoff is analyzed in the low-power and wideband regimes by employing the effective capacity formulation, rather than the Shannon capacity, and energy requirements under QoS constraints are identified. The analysis is conducted for the case in which perfect channel side information (CSI) is available at the receiver and also for the case in which perfect CSI is available at both the receiver and transmitter. In particular, it is shown in the low-power regime that the minimum bit energy required in the presence of QoS constraints is the same as that attained when there are no such limitations. However, this performance is achieved as
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Energy-efficient scheduling with individual delay constraints over a fading channel

by Wanshi Chen - Proc. of the 5th Int. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt , 2007
"... Abstract — This paper focuses on energy-efficient packet transmission with individual packet delay constraints over a fading channel. The problem of optimal offline scheduling (vis-à-vis total transmission energy), assuming information of all packet arrivals and channel states before scheduling, is ..."
Abstract - Cited by 16 (2 self) - Add to MetaCart
Abstract — This paper focuses on energy-efficient packet transmission with individual packet delay constraints over a fading channel. The problem of optimal offline scheduling (vis-à-vis total transmission energy), assuming information of all packet arrivals and channel states before scheduling, is formulated as a convex optimization problem with linear constraints. The optimality conditions are analyzed. From the analysis, a recursive algorithm is developed to search for the optimal offline scheduling. The optimal offline scheduler tries to equalize the energy-rate derivative function as much as possible subject to the causality and delay constraints. The properties of the optimal transmission rates are analyzed, from which upper and lower bounds of the average packet delay are derived. In addition, a heuristic online scheduling algorithm, using causal traffic and channel information, is proposed and shown via simulations to achieve comparable energy and delay performance to the optimal offline scheduler in a wide range of scenarios. I.
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An On-Line Learning Algorithm for Energy Efficient Delay Constrained Scheduling over a Fading Channel

by Nitin Salodkar, Abhijeet Bhorkar, Abhay Karandikar, Vivek S. Borkar
"... In this paper, we consider the problem of energy efficient scheduling under average delay constraint for a single user fading channel. We propose a new approach for on-line implementation of the optimal packet scheduling algorithm. This approach is based on reformulating the value iteration equatio ..."
Abstract - Cited by 16 (5 self) - Add to MetaCart
In this paper, we consider the problem of energy efficient scheduling under average delay constraint for a single user fading channel. We propose a new approach for on-line implementation of the optimal packet scheduling algorithm. This approach is based on reformulating the value iteration equation by introducing a virtual state called post-decision state. The resultant value iteration equation becomes amenable to online implementation based on stochastic approximation. This approach has an advantage that an explicit knowledge of the probability distribution of the channel state as well as the arrivals is not required for the implementation. We prove that the on-line algorithm indeed converges to the optimal policy.

Intelligent Packet Dropping for Optimal Energy-Delay Tradeoffs in Wireless Networks

by Michael J. Neely
"... We explore the advantages of intelligently dropping a small fraction of packets that arrive for transmission over a time varying wireless downlink. Without packet dropping, the optimal energy-delay tradeoff conforms to a square root tradeoff law, as shown by Berry and Gallager (2002). We show that ..."
Abstract - Cited by 14 (5 self) - Add to MetaCart
We explore the advantages of intelligently dropping a small fraction of packets that arrive for transmission over a time varying wireless downlink. Without packet dropping, the optimal energy-delay tradeoff conforms to a square root tradeoff law, as shown by Berry and Gallager (2002). We show that intelligently dropping any non-zero fraction of the input rate dramatically changes this relation from a square root tradeoff law to a logarithmic tradeoff law. Further, we demonstrate an innovative algorithm for achieving this logarithmic tradeoff without requiring a-priori knowledge of arrival rates or channel probabilities. The algorithm can be implemented in real time and easily extends to yield similar performance for multi-user systems.
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Max Weight Learning Algorithms with Application to Scheduling in Unknown Environments

by Michael J. Neely , 2009
"... We consider a discrete time stochastic queueing system where a controller makes a 2-stage decision every slot. The decision at the first stage reveals a hidden source of randomness with a control-dependent (but unknown) probability distribution. The decision at the second stage incurs a penalty vect ..."
Abstract - Cited by 14 (7 self) - Add to MetaCart
We consider a discrete time stochastic queueing system where a controller makes a 2-stage decision every slot. The decision at the first stage reveals a hidden source of randomness with a control-dependent (but unknown) probability distribution. The decision at the second stage incurs a penalty vector that depends on this revealed randomness. The goal is to stabilize all queues and minimize a convex function of the time average penalty vector subject to an additional set of time average penalty constraints. This setting fits a wide class of stochastic optimization problems. This includes problems of opportunistic scheduling in wireless networks, where a 2-stage decision about channel measurement and packet transmission must be made every slot without knowledge of the underlying transmission success probabilities. We develop a simple max-weight algorithm that learns efficient behavior by averaging functionals of previous outcomes. The algorithm yields performance that can be pushed arbitrarily close to optimal, with a tradeoff in convergence time and delay.