Praveen Kumar Gopala

On the Secrecy Capacity of Fading Channels

We consider the secure transmission of information over an ergodic fading channel in the presence of an eavesdropper. Our eavesdropper can be viewed as the wireless counterpart of Wyners wiretapper. The secrecy capacity of such a system is characterized under the assumption of asymptotically long coherence intervals. We analyze the full channel state information (CSI) case, where the transmitter has access to the channel gains of the legitimate receiver and eavesdropper, and the main channel CSI scenario, where only the legitimate receiver channel gain is known at the transmitter. In each scenario, the secrecy capacity is obtained along with the optimal power and rate allocation strategies. We then propose a low-complexity on/off power allocation strategy that achieves near-optimal performance with only the main channel CSI. More specifically, this scheme is shown to be asymptotically optimal as the average SNR goes to infinity, and interestingly, is shown to attain the secrecy capacity under the full CSI assumption. Remarkably, our results reveal the positive impact of fading on the secrecy capacity and establish the critical role of rate adaptation, based on the main channel CSI, in facilitating secure communications over slow fading channels.

On the throughput-delay tradeoff in cellular multicast

In this paper, we adopt a cross layer design approach for analyzing the throughput-delay tradeoff of the multicast channel in a single cell system. To illustrate the main ideas, we start with the single group case, i.e., pure multicast, where a common information stream is requested by all the users. We consider three classes of scheduling algorithms with progressively increasing complexity. The first class strives for minimum complexity by resorting to a static scheduling strategy along with memoryless decoding. Our analysis for this class of scheduling algorithms reveals the existence of a static scheduling policy that achieves the optimal scaling law of the throughput at the expense of a delay that increases exponentially with the number of users. The second scheduling policy resorts to a higher complexity incremental redundancy encoding/decoding strategy to achieve a superior throughput-delay tradeoff. The third, and most complex, scheduling strategy benefits from the cooperation between the different users to minimize the delay while achieving the optimal scaling law of the throughput. In particular, the proposed cooperative multicast strategy is shown to simultaneously achieve the optimal scaling laws of both throughput and delay. Then, we generalize our scheduling algorithms to exploit the multi-group diversity available when different information streams are requested by different subsets of the user population. Finally, we discuss the potential gains of equipping the base station with multiple transmit antennas and present simulation results that validate our theoretical claims.

Correlated sources over wireless channels: cooperative source-channel coding

We consider wireless sensor networks deployed to observe arbitrary random fields. The requirement is to reconstruct an estimate of the random field at a certain collector node. This creates a many-to-one data gathering wireless channel. One of the main challenges in this scenario is that the source/channel separation theorem, proved by Shannon for point-to-point links, does not hold any more. In this paper, we construct novel cooperative source-channel coding schemes that exploit the wireless channel and the correlation between the sources. In particular, we differentiate between two distinct cases. The first case assumes that the sensor nodes are equipped with receivers and, hence, every node can exploit the wireless link to distribute its information to its neighbors. We then devise an efficient deterministic cooperation strategy where the neighboring nodes act as virtual antennas in a beamforming configuration. The second, and more challenging, scenario restricts the capability of sensor nodes to transmit only. In this case, we argue that statistical cooperative source-channel coding techniques still yield significant performance gains in certain relevant scenarios. Specifically, we propose a low complexity cooperative source-channel coding scheme based on the proper use of low-density generator matrix codes. This scheme is shown to outperform the recently proposed joint source-channel coding scheme (Garcia-Frias et al., 2002) in the case of highly correlated sources. In both the deterministic and statistical cooperation scenarios, we develop analytical results that guide the optimization of the proposed schemes and validate the performance gains observed in simulations.

On the Error Exponents of ARQ Channels With Deadlines

In this correspondence, we consider communication over automatic repeat request (ARQ) memoryless channels with deadlines. In particular, an upper bound L is imposed on the maximum number of ARQ transmission rounds. In this setup, it is shown that incremental redundancy ARQ outperforms Forneys memoryless decoding in terms of the achievable error exponents.

On the scaling laws of multimodal wireless sensor networks

In this paper, we consider dense wireless sensor networks deployed to observe multiple random processes. The requirement is to reconstruct an estimate of each random process at the corresponding collector node. This leads to multiple many-to-one data gathering wireless channels that interfere with one another. We derive the transport capacity that the network can provide to each process and characterize an achievable rate region for the dense multimodal network. We further investigate the number of processes that can be observed simultaneously by the network. Specifically, we show that it is possible to observe O (N/sup /spl beta//) processes simultaneously such that the transport capacity scales as /spl Theta/ (log (N)) for each of the observed processes, with a large number of sensors A; and a fixed total average power. We show this result using a simple scheme based on antenna sharing. We then proceed to show that it is possible to simultaneously observe O (N/sup /spl beta//) continuous, spatially bandlimited Gaussian processes using a fixed total average power, through a scheme composed of single dimensional quantization, distributed Slepian-Wolf source coding, and the proposed antenna sharing strategy.

Resolving Collisions Via Incremental Redundancy: ARQ Diversity

A cross-layer approach is adopted for the design of finite-user symmetric random access wireless systems. Instead of the traditional collision model, a more realistic physical layer model is adopted. An incremental redundancy automatic repeat request (IR-ARQ) scheme, tailored to jointly combat the effects of user collisions, multi-path fading, and channel noise, is proposed. The diversity-multiplexing-delay tradeoff of the proposed scheme is analyzed for fully-loaded queues, and compared with that of the Gallager tree algorithm for collision resolution and the network-assisted diversity multiple access (NDMA) protocol of Tsatsanis et at.. The fully-loaded queue model is then replaced by one with random arrivals, where the three protocols are compared in terms of the stability region and average delay. Overall, our analytical and numerical results establish the superiority of the proposed IR-ARQ scheme and reveal some important insights. For example, it turns out that the performance is optimized, for a given total throughput, by maximizing the probability that a certain user will send a new packet and minimizing the transmission rate employed by each user.

Poster abstract: on the scaling laws of dense wireless sensor networks

We consider dense wireless sensor networks deployed to observe arbitrary random fields. The requirement is to reconstruct an estimate of the random field at a certain collector node. This creates a many-to-one data gathering wireless channel. In this note, we first characterize the transport capacity of many-to-one dense wireless networks subject to a constraint on the total average power. In particular, we show that the transport capacity scales as Theta(log(N)) when the number of sensors N grows to infinity and the total average power remains fixed. We then use this result along with some information-theoretic tools to derive sufficient and necessary conditions that characterize the set of observable random fields by dense sensor networks. In particular, for random fields that can be modeled as discrete random sequences, we derive a certain form of source/channel coding separation theorem. We further show that one can achieve any desired nonzero mean-square estimation error for continuous, Gaussian, and spatially bandlimited fields through a scheme composed of single-dimensional quantization, distributed Slepian-Wolf source coding, and the proposed antenna sharing strategy. Based on our results, we revisit earlier conclusions about the feasibility of data gathering applications using dense sensor networks.We characterize the scaling laws of many-to-one dense wireless sensor networks and derive conditions governing the observability of random fields by such networks. We further extend our results to the multimodal case where the sensors observe multiple random processes simultaneously. Quite interestingly, our results show that an unbounded number of spatially bandlimited Gaussian processes can be observed simultaneously by a dense multimodal wireless sensor network.