Santosh Nagaraj

Entropy-based spectrum sensing in cognitive radio

In this paper, we present a simple technique for detection of primary users in cognitive radio networks with unknown noise and interference levels. We will show that the likelihood ratio test for detecting the primary user can be approximated to a formulation that compares the estimated entropy of the received signal to a suitable threshold. This formulation is also intuitive since for a given variance, the entropy of a stochastic signal is maximized if it is Gaussian. If the received signal contains the primary users digitally modulated component, the entropy is reduced. Although the proposed approach is applicable under any scenario, we will specifically consider matched-filter-based detection in this paper, with its underlying assumption that the cognitive radio knows the primary user signaling waveform. We will consider the case where the Gaussian noise and interference levels in the region are unknown, which renders traditional matched-filtering and energy-based detection approaches unfeasible. The probabilities of successful detection and false alarm are characterized for both classical and Bayesian scenarios.

Cell Zooming for Power Efficient Base Station Operation

With enormous growth in the telecommunication industry, energy efficiency has become a critical issue. Base stations and the core network account for a large amount of energy consumption today. Traditional energy saving techniques switch some base stations off completely during light loads to save energy. This creates problems for the backhaul network and also for quickly returning to full capacity when demand increases. In this paper, we propose novel cell zooming techniques to reduce energy consumption at base stations. With cell zooming, base stations dynamically adjust their coverage radius and hence their transmit powers based on user locations. The transmit power is set to the minimum required level depending on user locations, signal to interference and noise ratio (SINR) and quality of service (QoS) requirements of the users. Base stations are never completely switched off. Our simulations show that the proposed cell zooming algorithm reduces the energy consumption of base stations by up to 40% compared to traditional static-coverage-area base stations without compromising on the QoS requirements of any user in the cell.

Entropy based spectrum sensing in cognitive radio

Cognitive radio (CR) is defined as an intelligent wireless communication, which has the capability of sensing and utilizing the underutilized spectrum resources. One of the key problems is how to efficiently and successfully detect the spectrum hole. In this paper, an entropy based technique for detection of primary users in cognitive radio networks with unknown noise and interference levels will be discussed. The likelihood ratio test will be used to examine the presence or absence of the primary user by comparing the empirical entropy of the received signal to a suitable threshold. In this paper, we will assume the primary users signaling waveform is known by the cognitive radios and the matched-filter based detection will be specifically considered.

QoS-aware dynamic cell reconfiguration for energy conservation in cellular networks

Given the significant energy consumption in operating base stations (BSs), improving their energy efficiency is an important problem in cellular networks. To this end, this paper proposes a novel framework, called DCR (dynamic cell reconfiguration) that dynamically adjust the set of active BSs and user association according to user traffic demand for energy conservation. In order to overcome prohibitive computational complexity in finding an optimal solution, we take an approach to design simple yet effective algorithms. We demonstrate that the proposed framework is not only computationally efficient but also can achieve the performance close to the optimum solution from an exhaustive search. Through simulations based on a real dataset of BS topology and utilization, we show that DCR can yield about a 30-40% reduction compared to the conventional static scheme where all BSs are always turned on.

Unequal Error Protection for H.264 Video Using RCPC Codes and Hierarchical QAM

Compressed video data is very sensitive to channel-induced errors and network losses. Most conventional unequal error protection techniques involve forward error correction codes of different rates or asymmetric signal constellations for data of different priorities. It is yet unknown if combining the above two techniques can further improve performance. In this paper, we investigate rate-compatible punctured convolutional (RCPC) codes concatenated with hierarchical QAM for H.264 encoded video sequences. We investigate system constraints and propose an optimization formulation to compute the optimal parameters of the proposed system under the given source significance information. An upper bound to the bit error rate of the proposed system is derived as a function of system parameters, including the code rate and geometry of the constellation. The example shown demonstrates system design for H.264 video, where PSNR improvement is observed.

Adaptive modulation for limited diversity fading channels

In this study, the authors propose a novel technique for adaptive modulation over limited diversity fading channels with channel state information at the transmitter. Limited diversity channels such as those encountered in indoor orthogonal frequency division multiplexing systems are characterised by the fact that achievable diversity orders are limited by the channel and not by code-free distances. The authors first propose a novel analysis technique for the performance of coded modulation on limited diversity block fading channels with different modulation sets on each block. The authors then propose adaptive modulation techniques for maximising the throughput at a fixed bit error probability and also for minimising the bit error probability at a fixed rate. Lastly, the authors show simulation results that support the arguments presented in the paper.

A Cross-Layer Unequal Error Protection Scheme for Prioritized H.264 Video using RCPC Codes and Hierarchical QAM

We investigate the rate-compatible punctured convolutional (RCPC) codes concatenated with hierarchical QAM for designing a cross-layer unequal error protection scheme for H.264 coded sequences. We first divide the H.264 encoded video slices into three priority classes based on their relative importance. We investigate the system constraints and propose an optimization formulation to compute the optimal parameters of the proposed system for the given source significance information. An upper bound to the significance-weighted bit error rate in the proposed system is derived as a function of system parameters, including the code rate and geometry of the constellation. An example is given with design rules for H.264 video communications and 3.5-4 dB PSNR improvement over existing RCPC based techniques for AWGN wireless channels is shown through simulations.

An extension to the ordered subcarrier selection algorithm (OSSA)

In this letter, we propose an extension to the ordered subcarrier selection algorithm (OSSA) for orthogonal frequency division multiplexing (OFDM) systems. The result is a simple algorithm for minimizing the bit error rate of the OFDM system at a fixed throughput. The proposed algorithm employs multiple modulations (non-uniform bit loading) within an OFDM symbol. However, unlike existing bit loading algorithms that have a very high computational complexity, the proposed algorithm is based only on the ordered statistics of the subcarrier gains and is consequently very simple. After ordering the subcarriers based on their gains, progressively higher order modulations are used with increasing gains. The key aspect here that greatly simplifies the algorithm is that the modulation used on a subcarrier depends only on the position of its gain in the ordered set and not on the actual values of the gains. We show an analytical approach for determining the parameters of the algorithm.

Symbol-level adaptive modulation for coded OFDM on block fading channels

It has recently been recognized by many researchers that adaptive modulation is most effective when the channel diversity order is small. In this letter, we propose a simple adaptive modulation scheme for orthogonal frequency division multiplexing (OFDM) systems on channels that provide a small order of diversity. The proposed adaptation algorithm is based on a novel and very simple analytical formula we derive for the performance of BICM on block fading channels. Simulation results show that the derived analytical formula is very tight when the targeted bit error probability is small. OFDM systems on indoor channels provide small orders of diversity and form an ideal scenario for adaptation. In order to keep system complexity and feedback requirements at a minimum, we will consider symbol-level adaptive modulation wherein all the subcarriers in an OFDM symbol use the same modulation.

Concatenated Trellis Coded Modulation for Quasi-Static Fading Channels

In this paper, we present a novel concatenated trellis coded modulation (CTCM) scheme for limited diversity order fading channels. Examples for such channels include those encountered in indoor wireless networks like IEEE 802.11. It is first shown that when the diversity order afforded by the channel is fixed, bit interleaved coded modulation (BICM) is no longer the best way to encode. We then develop CTCM, which is superior to both BICM and conventional TCM of similar complexities. Unlike conventional TCM where convolutional codes are designed over modulated signal sets, CTCM has TCM concatenated to short length inner codes. Each trellis branch in the TCM now corresponds to a short block-code. We discuss design of good inner codes that allow for simple decoders. CTCM design incorporates useful features of both BICM and conventional TCM. Code design is explained with examples. Simulation results and information theoretic supporting the arguments are shown.