Vaneet Aggarwal

Design and Characterization of a Full-Duplex Multiantenna System for WiFi Networks

In this paper, we present an experiment- and simulation-based study to evaluate the use of full duplex (FD) as a potential mode in practical IEEE 802.11 networks. To enable the study, we designed a 20-MHz multiantenna orthogonal frequency-division-multiplexing (OFDM) FD physical layer and an FD media access control (MAC) protocol, which is backward compatible with current 802.11. Our extensive over-the-air experiments, simulations, and analysis demonstrate the following two results. First, the use of multiple antennas at the physical layer leads to a higher ergodic throughput than its hardware-equivalent multiantenna half-duplex (HD) counterparts for SNRs above the median SNR encountered in practical WiFi deployments. Second, the proposed MAC translates the physical layer rate gain into near doubling of throughput for multinode single-AP networks. The two results allow us to conclude that there are potentially significant benefits gained from including an FD mode in future WiFi standards.

Iterative Dynamic Water-Filling for Fading Multiple-Access Channels With Energy Harvesting

In this paper, we develop optimal energy scheduling algorithms for N-user fading multiple-access channels with energy harvesting to maximize the channel sum-rate, assuming that the side information of both the channel states and energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with O (NK) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver becomes impractically high when the number of constraints is large. This paper gives an efficient energy scheduling algorithm, called the iterative dynamic water-filling algorithm, that has a computational complexity of O(NK2) per iteration. For the single-user case, a dynamic water-filling method is shown to be optimal. Unlike the traditional water-filling algorithm, in dynamic water-filling, the water level is not constant but changes when the battery overflows or depletes. An iterative version of the dynamic water-filling algorithm is shown to be optimal for the case of multiple users. Even though in principle the optimality is achieved under large number of iterations, in practice convergence is reached in only a few iterations. Moreover, a single iteration of the dynamic water-filling algorithm achieves a sum-rate that is within (N-1)K nats of the optimal sum-rate.

Secrecy capacity of a class of orthogonal relay eavesdropper channels

The secrecy capacity of relay channels with orthogonal components is studied in the presence of an additional passive eavesdropper node. The relay and destination receive signals from the source on two orthogonal channels such that the destination also receives transmissions from the relay on its channel. The eavesdropper can overhear either one or both of the orthogonal channels. Inner and outer bounds on the secrecy capacity are developed for both the discrete memoryless and the Gaussian channel models. For the discrete memoryless case, the secrecy capacity is shown to be achieved by a partial decode-and-forward (PDF) scheme when the eavesdropper can overhear only one of the two orthogonal channels. Two new outer bounds are presented for the Gaussian model using recent capacity results for a Gaussian multiantenna point-to-point channel with a multiantenna eavesdropper. The outer bounds are shown to be tight for two subclasses of channels. The first subclass is one in which the source and relay are clustered, and the eavesdropper receives signals only on the channel from the source and the relay to the destination, for which the PDF strategy is optimal. The second is a subclass in which the source does not transmit to the relay, for which a noise-forwarding strategy is optimal.

Modeling location uncertainty for eavesdroppers: A secrecy graph approach

In this paper, we consider end-to-end secure communication in a large wireless network, where the locations of eavesdroppers are uncertain. Our framework attempts to bridge the gap between physical layer security under uncertain channel state information of the eavesdropper and network level connectivity under security constraints, by modeling location uncertainty directly at the network level as correlated node and link failures in a secrecy graph. Bounds on the percolation threshold are obtained for square and triangular lattices, and bounds on mean degree are obtained for Poisson secrecy graphs. Both analytic and simulation results show the dramatic effect of uncertainty in location of eavesdroppers on connectivity in a secrecy graph.

On Achieving Local View Capacity Via Maximal Independent Graph Scheduling

“If we know more, we can achieve more.” This adage also applies to communication networks, where more information about the network state translates into higher sum-rates. In this paper, we formalize this increase of sum-rate with increased knowledge of the network state. The knowledge of network state is measured in terms of the number of hops, h, of information available to each transmitter and is labeled as h-local view. To understand how much capacity is lost due to limited information, we propose to use the metric of normalized sum-capacity, which is the h -local view sum-capacity divided by global-view sum capacity. For the cases of one and two-local view, we characterize the normalized sum-capacity for many classes of deterministic and Gaussian interference networks. In many cases, a scheduling scheme called maximal independent graph scheduling is shown to achieve normalized sum-capacity. We also show that its generalization for 1-local view, labeled coded set scheduling, achieves normalized sum-capacity in some cases where its uncoded counterpart fails to do so.

Full- or half-duplex? A capacity analysis with bounded radio resources

Full duplex communication requires nodes to cancel their own signal which appears as an interference at their receive antennas. Recent work has experimentally demonstrated the feasibility of full duplex communications using software radios. In this paper, we address capacity comparisons when the total amount of analog radio hardware is bounded. Under this constraint, it is not immediately clear if one should use these radios to perform full-duplex self-interference cancellation or use the radios to give additional MIMO multiplexing advantage. We find that repurposing radios for cancellation, instead of using all of them for half-duplex over-the-air transmission, can be beneficial since the resulting full-duplex system performs better in some practical SNR regimes and almost always outperforms half duplex in symmetric degrees-of-freedom (large SNR regime).

Layered Exact-Repair Regenerating Codes via Embedded Error Correction and Block Designs

A new class of exact-repair regenerating codes is constructed by stitching together shorter erasure correction codes, where the stitching pattern can be viewed as block designs. The proposed codes have the help-by-transfer property where the helper nodes simply transfer part of the stored data directly, without performing any computation. This embedded error correction structure makes the decoding process straightforward, and in some cases the complexity is very low. We show that this construction is able to achieve performance better than space-sharing between the minimum storage regenerating codes and the minimum repair-bandwidth regenerating codes, and it is the first class of codes to achieve this performance. In fact, it is shown that the proposed construction can achieve a nontrivial point on the optimal functional-repair tradeoff, and it is asymptotically optimal at high rate, i.e., it asymptotically approaches the minimum storage and the minimum repair-bandwidth simultaneously.

Exploiting virtualization for delivering cloud-based IPTV services

Cloud computing is a new infrastructure environment that delivers on the promise of supporting on-demand services in a flexible manner by scheduling bandwidth, storage and compute resources on the fly. IPTV services like Video On Demand (VoD) and Live broadcast TV requires substantial bandwidth and compute resources to meet the real time requirements and to handle the very bursty resource requirements for each of these services. To meet the needs of the bursts of requests, each with a deadline constraint for both VoD and LiveTV channel changes, we propose a resource provisioning framework that allows these services to co-exist on a common infrastructure by taking advantage of virtualization. We propose an optimal algorithm that provides the minimum number of servers needed to fulfill all requests for these services. We prove this optimality in a general setting for any number of services with general deadline constraints. By using real world data from an operational IPTV environment, our results show that anticipating and thereby enabling the delaying of VoD requests by up to 30 seconds gives significant resource savings even under conservative environmental assumptions. We also experiment with different scenarios (by varying the deadline constraints, changing the peak to average ratios of the constituent services) to compute the overall savings.

Wiretap channel type II with an active eavesdropper

The wiretap channel type II with an active eavesdropper is considered in this paper. Compared with the eavesdropper model considered in much of the literature, the eavesdropper considered here can not only overhear but also modify the signal transmitted over the channel. Two modification models are considered. In the first model, the eavesdropper erases the bits it observes. In the second model, the eavesdropper modifies the bits it observes. For this channel with memory (introduced by the activity of the eavesdropper), one should conduct the worst case scenario analysis. Novel concatenated coding schemes that provide perfect security for the communications are developed for both models to give bounds on the achievable secrecy rate. The technique to modify the inner code to maintain the secrecy properties of the outer code may be of independent interest.

Power Allocation for Energy Harvesting Transmitter With Causal Information

We consider power allocation for an access-controlled transmitter with energy harvesting capability based on causal observations of the channel fading state. We assume that the system operates in a time-slotted fashion and the channel gain in each slot is a random variable which is independent across slots. Further, we assume that the transmitter is solely powered by a renewable energy source and the energy harvesting process can practically be predicted. With the additional access control for the transmitter and the maximum power constraint, we formulate the stochastic optimization problem of maximizing the achievable rate as a Markov decision process (MDP) with continuous state. To efficiently solve the problem, we define an approximate value function based on a piecewise linear fit in terms of the battery state. We show that with the approximate value function, the update in each iteration consists of a group of convex problems with a continuous parameter. Moreover, we derive the optimal solution to these convex problems in closed-form. Further, we propose power allocation algorithms for both the finite- and infinite-horizon cases, whose computational complexity is significantly lower than that of the standard discrete MDP method but with improved performance. Extension to the case of a general payoff function and imperfect energy prediction is also considered. Finally, simulation results demonstrate that the proposed algorithms closely approach the optimal performance.