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Sriram Vishwanath

Publications and source records attributed to Sriram Vishwanath.

158 records · Page 9Linked to original sources

Communication Through Jamming over a Slotted ALOHA Channel

This work derives bounds on the jamming capacity of a slotted ALOHA system. A system with n legitimate users, each with a Bernoulli arrival process is considered. Packets are temporarily stored at the corresponding user queues, and a slotted ALOHA strategy is used for packet transmissions over the shared channel. The scenario considered is that of a pair of illegitimate users that jam legitimate transmissions in order to communicate over the slotted ALOHA channel. Jamming leads to binary signaling between the illegitimate users, with packet collisions due to legitimate users treated as (multiplicative) noise in this channel. Further, the queueing dynamics at the legitimate users stochastically couples the jamming strategy used by the illegitimate users and the channel evolution. By considering various i.i.d. jamming strategies, achievable jamming rates over the slotted ALOHA channel are derived. Further, an upper bound on the jamming capacity over the class of all ergodic jamming policies is derived. These bounds are shown to be tight in the limit where the offered system load approaches unity.

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Relay Subset Selection in Wireless Networks Using Partial Decode-and-Forward Transmission

This paper considers the problem of selecting a subset of nodes in a two-hop wireless network to act as relays in aiding the communication between the source-destination pair. Optimal relay subset selection with the objective of maximizing the overall throughput is a difficult problem that depends on multiple factors including node locations, queue lengths and power consumption. A partial decode-and-forward strategy is applied in this paper to improve the tractability of the relay selection problem and performance of the overall network. Note that the number of relays selected ultimately determines the performance of the network. This paper benchmarks this performance by determining the net diversity achieved using the relays selected and the partial decode-and-forward strategy. This framework is subsequently used to further transform relay selection into a simpler relay placement problem, and two proximity-based approximation algorithms are developed to determine the appropriate set of relays to be selected in the network. Other selection strategies such as random relay selection and a greedy algorithm that relies on channel state information are also presented. This paper concludes by showing that the proposed proximity-based relay selection strategies yield near-optimal expected rates for a small number of selected relays.

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Scheduling and Pre-Conditioning in Multi-User MIMO TDD Systems

The downlink transmission in multi-user multiple-input multiple-output (MIMO) systems has been extensively studied from both communication-theoretic and information-theoretic perspectives. Most of these papers assume perfect/imperfect channel knowledge. In general, the problem of channel training and estimation is studied separately. However, in interference-limited communication systems with high mobility, this problem is tightly coupled with the problem of maximizing throughput of the system. In this paper, scheduling and pre-conditioning based schemes in the presence of reciprocal channel are considered to address this. In the case of homogeneous users, a scheduling scheme is proposed and an improved lower bound on the sum capacity is derived. The problem of choosing training sequence length to maximize net throughput of the system is studied. In the case of heterogeneous users, a modified pre-conditioning method is proposed and an optimized pre-conditioning matrix is derived. This method is combined with a scheduling scheme to further improve net achievable weighted-sum rate.

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Rate Bounds for MIMO Relay Channels

This paper considers the multi-input multi-output (MIMO) relay channel where multiple antennas are employed by each terminal. Compared to single-input single-output (SISO) relay channels, MIMO relay channels introduce additional degrees of freedom, making the design and analysis of optimal cooperative strategies more complex. In this paper, a partial cooperation strategy that combines transmit-side message splitting and block-Markov encoding is presented. Lower bounds on capacity that improve on a previously proposed non-cooperative lower bound are derived for Gaussian MIMO relay channels.

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Generalized Degrees of Freedom of the Symmetric Gaussian $K$ User Interference Channel

We characterize the generalized degrees of freedom of the $K$ user symmetric Gaussian interference channel where all desired links have the same signal-to-noise ratio (SNR) and all undesired links carrying interference have the same interference-to-noise ratio, ${INR}={SNR}^α$. We find that the number of generalized degrees of freedom per user, $d(α)$, does not depend on the number of users, so that the characterization is identical to the 2 user interference channel with the exception of a singularity at $α=1$ where $d(1)=\frac{1}{K}$. The achievable schemes use multilevel coding with a nested lattice structure that opens the possibility that the sum of interfering signals can be decoded at a receiver even though the messages carried by the interfering signals are not decodable.

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The Impact of Channel Feedback on Opportunistic Relay Selection for Hybrid-ARQ in Wireless Networks

This paper presents a decentralized relay selection protocol for a dense wireless network and describes channel feedback strategies that improve its performance. The proposed selection protocol supports hybrid automatic-repeat-request transmission where relays forward parity information to the destination in the event of a decoding error. Channel feedback is employed for refining the relay selection process and for selecting an appropriate transmission mode in a proposed adaptive modulation transmission framework. An approximation of the throughput of the proposed adaptive modulation strategy is presented, and the dependence of the throughput on system parameters such as the relay contention probability and the adaptive modulation switching point is illustrated via maximization of this approximation. Simulations show that the throughput of the proposed selection strategy is comparable to that yielded by a centralized selection approach that relies on geographic information.

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Adaptive Sum Power Iterative Waterfilling for MIMO Cognitive Radio Channels

In this paper, the sum capacity of the Gaussian Multiple Input Multiple Output (MIMO) Cognitive Radio Channel (MCC) is expressed as a convex problem with finite number of linear constraints, allowing for polynomial time interior point techniques to find the solution. In addition, a specialized class of sum power iterative waterfilling algorithms is determined that exploits the inherent structure of the sum capacity problem. These algorithms not only determine the maximizing sum capacity value, but also the transmit policies that achieve this optimum. The paper concludes by providing numerical results which demonstrate that the algorithm takes very few iterations to converge to the optimum.

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On the Capacity of a Class of MIMO Cognitive Radios

Cognitive radios have been studied recently as a means to utilize spectrum in a more efficient manner. This paper focuses on the fundamental limits of operation of a MIMO cognitive radio network with a single licensed user and a single cognitive user. The channel setting is equivalent to an interference channel with degraded message sets (with the cognitive user having access to the licensed user's message). An achievable region and an outer bound is derived for such a network setting. It is shown that under certain conditions, the achievable region is optimal for a portion of the capacity region that includes sum capacity.

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Generation of Efficient Codes for Realizing Boolean Functions in Nanotechnologies

We address the challenge of implementing reliable computation of Boolean functions in future nanocircuit fabrics. Such fabrics are projected to have very high defect rates. We overcome this limitation by using a combination of cheap but unreliable nanodevices and reliable but expensive CMOS devices. In our approach, defect tolerance is achieved through a novel coding of Boolean functions; specifically, we exploit the dont cares of Boolean functions encountered in multi-level Boolean logic networks for constructing better codes. We show that compared to direct application of existing coding techniques, the coding overhead in terms of extra bits can be reduced, on average by 23%, and savings can go up to 34%. We demonstrate that by incorporating efficient coding techniques more than a 40% average yield improvement is possible in case of 1% and 0.1% defect rates. With 0.1% defect density, the savings can be up to 90%.

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Hybrid-ARQ in Multihop Networks with Opportunistic Relay Selection

This paper develops a contention-based opportunistic feedback technique towards relay selection in a dense wireless network. This technique enables the forwarding of additional parity information from the selected relay to the destination. For a given network, the effects of varying key parameters such as the feedback probability are presented and discussed. A primary advantage of the proposed technique is that relay selection can be performed in a distributed way. Simulation results find its performance to closely match that of centralized schemes that utilize GPS information, unlike the proposed method. The proposed relay selection method is also found to achieve throughput gains over a point-to-point transmission strategy.

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Opportunistic Relay Selection with Limited Feedback

It has been shown that a decentralized relay selection protocol based on opportunistic feedback from the relays yields good throughput performance in dense wireless networks. This selection strategy supports a hybrid-ARQ transmission approach where relays forward parity information to the destination in the event of a decoding error. Such an approach, however, suffers a loss compared to centralized strategies that select relays with the best channel gain to the destination. This paper closes the performance gap by adding another level of channel feedback to the decentralized relay selection problem. It is demonstrated that only one additional bit of feedback is necessary for good throughput performance. The performance impact of varying key parameters such as the number of relays and the channel feedback threshold is discussed. An accompanying bit error rate analysis demonstrates the importance of relay selection.

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On the Capacity of Multiple Access Channels with State Information and Feedback

In this paper, the multiple access channel (MAC) with channel state is analyzed in a scenario where a) the channel state is known non-causally to the transmitters and b) there is perfect causal feedback from the receiver to the transmitters. An achievable region and an outer bound are found for a discrete memoryless MAC that extend existing results, bringing together ideas from the two separate domains of MAC with state and MAC with feedback. Although this achievable region does not match the outer bound in general, special cases where they meet are identified. In the case of a Gaussian MAC, a specialized achievable region is found by using a combination of dirty paper coding and a generalization of the Schalkwijk-Kailath, Ozarow and Merhav-Weissman schemes, and this region is found to be capacity achieving. Specifically, it is shown that additive Gaussian interference that is known non-causally to the transmitter causes no loss in capacity for the Gaussian MAC with feedback.

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On the Capacity of Interference Channels with Degraded Message sets

This paper is motivated by a sensor network on a correlated field where nearby sensors share information, and can thus assist rather than interfere with one another. A special class of two-user Gaussian interference channels (IFCs) is considered where one of the two transmitters knows both the messages to be conveyed to the two receivers (called the IFC with degraded message sets). Both achievability and converse arguments are provided for this scenario for a class of discrete memoryless channels with weak interference. For the case of the Gaussian weak interference channel with degraded message sets, optimality of Gaussian inputs is also shown, resulting in the capacity region of this channel.

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On the Capacity of Gaussian Weak Interference Channels with Degraded Message sets

This paper is motivated by a sensor network on a correlated field where nearby sensors share information, and can thus assist rather than interfere with one another. We consider a special class of two-user Gaussian interference channels (IFCs) where one of the two transmitters knows both the messages to be conveyed to the two receivers. Both achievability and converse arguments are provided for a channel with Gaussian inputs and Gaussian noise when the interference is weaker than the direct link (a so called weak IFC). In general, this region serves as an outer bound on the capacity of weak IFCs with no shared knowledge between transmitters.

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