Balasubramaniam Natarajan

High-performance MC-CDMA via carrier interferometry codes

This paper introduces the principles of interferometry to multicarrier code division multiple access (MC-CDMA). Specifically, we propose the use of MC-CDMA with novel carrier interferometry (CI) complex spreading codes. The CI/MC-CDMA method, applied to mobile wireless communication systems, offers enhanced performance and flexibility relative to MC-CDMA with conventional spreading codes. Specifically, assuming a frequency selective Rayleigh-fading channel, CI/MC-CDMAs performance matches that of orthogonal MC-CDMA using Hadamard-Walsh codes up to the MC-CDMA N user limit; and, CI/MC-CDMA provides the added flexibility of going beyond N users, adding up to N-1 additional users with pseudo orthogonal positioning. When compared to MC-CDMA schemes capable of supporting greater than N users, CI/MC-CDMAs performance exceeds that of MC-CDMA. Additionally, this new system is analyzed in the presence of phase jitters and frequency offsets and is shown to be robust to both cases.

Generation of correlated Rayleigh fading envelopes for spread spectrum applications

A procedure for generating N Rayleigh fading envelopes with any desired covariance matrix is given. This method, numerical in nature, enables researchers to simulate correlated fading envelopes, for use in: (1) the study of the impact of correlation on diversity system performance and (2) the study of multicarrier CDMA (MC-CDMA), where the number of carriers notably exceeds the degree of system diversity.

Voltage/VAR Control in Distribution Networks via Reactive Power Injection Through Distributed Generators

This paper demonstrates how reactive power injection from distributed generators can be used to mitigate the voltage/VAR control problem of a distribution network. Firstly, power flow equations are formulated with arbitrarily located distributed generators in the network. Since reactive power injection is limited by economic viability and power electronics interface, we formulate voltage/VAR control as a constrained optimization problem. The formulation aims to minimize the combined reactive power injection by distributed generators, with constraints on: 1) power flow equations; 2) voltage regulation; 3) phase imbalance correction; and 4) maximum and minimum reactive power injection. The formulation is a nonconvex problem thereby making the search for an optimal solution extremely complex. So, a suboptimal approach is proposed based on methods of sequential convex programming (SCP). Comparing our suboptimal approach with the optimal solution obtained from branch and bound method, we show the trade-off in quality of our solution with runtime. We also validate our approach on the IEEE 123 node test feeder and illustrate the efficacy of using distributed generators as distributed reactive power resource.

Large set of CI spreading codes for high-capacity MC-CDMA

In this letter, a large set of spreading codes that doubles capacity in multicarrier code-division multiple-access (MC-CDMA) systems without any cost in bandwidth and with negligible cost in performance is introduced. This large set is comprised of: 1) complex spreading codes instead of conventional real-valued spreading codes and 2) two sets, each made up of orthogonal complex spreading codes, with minimum cross correlation between sets. Simulations performed over Rayleigh fading channels demonstrate 100% gains in terms of MC-CDMA capacity with negligible loss in performance.

State Estimation and Voltage/VAR Control in Distribution Network With Intermittent Measurements

Reactive power injection in smart grid distribution networks via distributed generators is envisioned to play a vital role in voltage/VAR support. In this paper, we integrate the three aspects of voltage/VAR support: modeling, state estimation and network control in a single framework. Firstly, we develop an input to state nonlinear dynamic model that incorporates power flow equations along with load and distributed generation (DG) forecasts. Then, considering an extended Kalman filter (EKF) approach for nonlinear state estimation, we analyze the impact of dropped packets on stability of estimation process. Finally, we apply separation principle locally around some known state estimates, to design a nonlinear model predictive control (NMPC) based voltage/VAR support strategy. The control problem aims to minimize the aggregate reactive power injected by DG with the following constraints: 1) voltage regulation; 2) phase imbalance correction; and 3) maximum and minimum reactive power injection by individual generators. Considering computational complexity incurred in search for the optimal solution for large scale nonlinear control problems, we propose a successive time varying linear (STVL) approximation to our voltage/VAR control problem. The control framework approach and the analytical results presented in this paper are validated by simulating a radial distribution network as an example.

Small Cell Base Station Sleep Strategies for Energy Efficiency

Small cell networks offer a promising and viable approach to meeting the increasing demand for high-data-rate wireless applications. With the expected increase in the number of small cell deployments, energy efficiency (EE) is a crucial system design parameter that demands consideration from an eco-sustainability perspective. One way to improve EE is to switch off small cell base stations (BSs) or to keep them in energy-saving mode while preserving the quality of service (QoS) experienced by users. With “bits/joule” as the metric, we aim to optimize EE with the introduction of several levels of sleep depths. Using a stochastic geometry-based heterogeneous cellular network (HCN) model, we derive coverage probability, average achievable rate, and EE in heterogeneous K-tier wireless networks with different sleep modes for small cells. Then, we try to maximize EE under 1) a random sleeping policy and 2) a strategic sleeping policy, with constraints on both coverage probability and wake-up times. Due to the nonconvexity of EE, we propose an alternative low-complexity near-optimal solution by maximizing the lower bound of EE. We use an alternating iterative approach to solve the resulting multivariable optimization problem. Simulation results confirm the effectiveness of the scheme. With improvements of approximately 30% in EE with random sleeping policy, simulation indicates that instantaneous EE can be further improved by 15% with a strategic sleeping policy.

Optimal Control-Based Strategy for Sensor Deployment

In sensor network-based detection/surveillance, one of the first challenges to address is the optimal deployment of sensors such that detection requirements are satisfied in a given area. Specifically, we pose the following question: Given a finite number of sensors, what is the best way to deploy these sensors in order to minimize the squared difference between achieved and required detection/miss probabilities? In this paper, we develop a novel optimal control theory based formulation of this sensor deployment problem. Exploiting similarities between the problem at hand and the linear quadratic regulator, an analytical solution is derived and tested. Unlike prior efforts that rely purely on heuristics, the proposed optimal control framework provides a theoretical basis for the resulting solution. As the complexity of the optimal control based solution is high, we develop a low-complexity approximation called Max_Deficiency algorithm. Using simulation results, we show that the proposed algorithms outperform existing methods by using 10% to 30% fewer number of sensors to satisfy detection requirements.

Novel low-complexity DS-CDMA multiuser detector based on ant colony optimization

We present a novel multiuser detection (MUD) technique, based on ant colony optimization (ACO), for synchronous direct sequence code division multiple access (DS-CDMA) systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO based MUD converges to the optimal BER performance in relatively few iterations, providing 9.5% saving in computational complexity.

Distribution Grid State Estimation from Compressed Measurements

Real-time control of a smart distribution grid with renewable energy based generators requires accurate state estimates, that are typically based on measurements aggregated from smart meters. However, the amount of data/measurements increases with the scale of the physical grid, posing a significant stress on both the communication infrastructure as well as data processing control centers. This paper first investigates the effect of geographical footprint of distributed generation (DG) on the voltage states of a smart distribution system. We demonstrate that the strong coupling in the physical power system results in estimated voltage phasors exhibiting a correlation structure that allows for compression of measurements. Specifically, by exploiting principles of 1D and 2D compressed sensing, we develop two approaches, an indirect and direct method for state estimation starting from compressed power measurements. We illustrate the effectiveness of voltage estimation with significantly low number of random spatial, temporal as well as spatio-temporal power measurements using the IEEE 34 node distribution test feeder and a larger 100 node radial distribution system. Results show similar performance for both methods at all levels of compression. It is observed that, even with only 50% compressed power measurements, both methods estimate the states of the test feeder with high level of accuracy.

Throughput enhancement in TDMA through carrier interferometry pulse shaping

This paper introduces a novel TDMA scheme that provides enhanced throughput by employing carrier interferometry pulse shapes (CI pulse shapes). At the transmitter, CI pulse shapes are created from the superposition of N carriers, which generates a short mainlobe (pulse) in time. CI pulse shapes are positioned both orthogonally and pseudo-orthogonally in time, enabling the introduction of additional bits into a TDMA burst. Specifically, up to a 100% increase in throughput can be achieved. At the receiver, a novel TDMA detector is deployed where: the pulse shape is broken down into its frequency components and optimally recombined to create frequency diversity benefits. Simulations performed over hilly terrain (HT) and typical urban (TU) GSM channel models indicate that, with a 100% increase in throughput, the proposed system offers up to 6.5 dB performance gains at probability of error of 10/sup -2/ relative to a standard GSM system employing a decision feedback receiver.