Power control and transmission scheduling for network utility maximization in wireless networks

We consider a joint power control and transmission scheduling problem in wireless networks with average power constraints. While the capacity region of a wireless network is convex, a characterization of this region is a hard problem. We formulate a network utility optimization problem involving time-sharing across different "transmission modes," where each mode corresponds to the set of power levels used in the network. The structure of the optimal solution is a time-sharing across a small set of such modes. We use this structure to develop an efficient heuristic approach to finding a suboptimal solution through column generation iterations. This heuristic approach converges quite fast in simulations, and provides a tool for wireless network planning.

Process Variation Aware Estimation of Static Leakage Power in Nano-CMOS

We present a statistical methodology for leakage power estimation, due to subthreshold and gate tunneling leakage, in the presence of process variations, for 65 nm CMOS. The circuit leakage power variations is analyzed by Monte Carlo (MC) simulations, by characterizing NAND gate library. A statistical “hybrid model” is proposed, to extend this methodology to a generic library. We demonstrate that hybrid model based statistical design results in up to 95% improvement in the prediction of worst to best corner leakage spread, with an error of less than 0.5%, with respect to worst case design.

Jointly Optimal Power Control and Hop Length for a Single Cell, Dense, Ad hoc Wireless Network

We consider a dense, ad hoc wireless network confined to a small region, such that direct communication is possible between any pair of nodes. The physical communication model is that a receiver decodes the signal from a single transmitter, while treating all other signals as interference. Data packets are sent between source-destination pairs by multihop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc network (described above) as a single cell, we study the optimal hop length and power control that maximizes the transport capacity for a given network power constraint. More specifically, for a fading channel and for a fixed transmission time strategy (akin to the IEEE 802.11 TXOP), we find that there exists an intrinsic aggregate bit rate (Thetaopt bits per second, depending on the contention mechanism and the channel fading characteristics) carried by the network, when operating at the optimal hop length and power control. The optimal transport capacity is of the form dopt(Pmacrt) x Thetaopt with dopt scaling as Pmacrt 1 /eta, where Pmacrt is the available time average transmit power and eta is the path loss exponent. Under certain conditions on the fading distribution, we then pro- - vide a simple characterisation of the optimal operating point.

A Low Power Frequency Multiplication Technique for Zigbee Transceiver

A low-power frequency multiplication technique, developed for ZigBee (IEEE 802.15.4) like applications is presented. We have provided an estimate for the power consumption for a given output voltage swing using our technique. The advantages and disadvantages which determine the application areas of the technique are discussed. The issues related to design, layout and process variation are also addressed. Finally, a design is presented for operation in 2.405-2.485-GHz band of ZigBee receiver. SpectreRF simulations show 30% improvement in efficiency for our circuit with regard to conversion of DC bias current to output amplitude, against a LC-VCO. To establish the low-power credentials, we have compared our circuit with an existing technique; our circuit performs better with just 1/3 of total current from supply, and uses one inductor as against three in the latter case. A test chip was implemented in UMC 0.13-mum RF process with spiral on-chip inductors and MIM (metal-insulator-metal) capacitor option.

A Scalable Low Power Issue Queue for Large Instruction Window Processors

Large instruction windows and issue queues are key to exploiting greater instruction level parallelism in out-of-order superscalar processors. However, the cycle time and energy consumption of conventional large monolithic issue queues are high. Previous efforts to reduce cycle time segment the issue queue and pipeline wakeup. Unfortunately, this results in significant IPC loss. Other proposals which address energy efficiency issues by avoiding only the unnecessary tag-comparisons do not reduce broadcasts. These schemes also increase the issue latency.To address both these issues comprehensively, we propose the Scalable Lowpower Issue Queue (SLIQ). SLIQ augments a pipelined issue queue with direct indexing to mitigate the problem of delayed wakeups while reducing the cycle time. Also, the SLIQ design naturally leads to significant energy savings by reducing both the number of tag broadcasts and comparisons required.A 2 segment SLIQ incurs an average IPC loss of 0.2% over the entire SPEC CPU2000 suite, while achieving a 25.2% reduction in issue latency when compared to a monolithic 128-entry issue queue for an 8-wide superscalar processor. An 8 segment SLIQ improves scalability by reducing the issue latency by 38.3% while incurring an IPC loss of only 2.3%. Further, the 8 segment SLIQ significantly reduces the energy consumption and energy-delay product by 48.3% and 67.4% respectively on average.