arXiv subjects

Sundeep Rangan

Publications and source records attributed to Sundeep Rangan.

At least 109 records · Page 6Linked to original sources

arXiv2017

Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks

The millimeter wave (mmWave) bands offer the possibility of orders of magnitude greater throughput for fifth generation (5G) cellular systems. However, since mmWave signals are highly susceptible to blockage, channel quality on any one mmWave link can be extremely intermittent. This paper implements a novel dual connectivity protocol that enables mobile user equipment (UE) devices to maintain physical layer connections to 4G and 5G cells simultaneously. A novel uplink control signaling system combined with a local coordinator enables rapid path switching in the event of failures on any one link. This paper provides the first comprehensive end-to-end evaluation of handover mechanisms in mmWave cellular systems. The simulation framework includes detailed measurement-based channel models to realistically capture spatial dynamics of blocking events, as well as the full details of MAC, RLC and transport protocols. Compared to conventional handover mechanisms, the study reveals significant benefits of the proposed method under several metrics.

cs.NI↗

arXiv2017

Inference in Deep Networks in High Dimensions

Deep generative networks provide a powerful tool for modeling complex data in a wide range of applications. In inverse problems that use these networks as generative priors on data, one must often perform inference of the inputs of the networks from the outputs. Inference is also required for sampling during stochastic training on these generative models. This paper considers inference in a deep stochastic neural network where the parameters (e.g., weights, biases and activation functions) are known and the problem is to estimate the values of the input and hidden units from the output. While several approximate algorithms have been proposed for this task, there are few analytic tools that can provide rigorous guarantees in the reconstruction error. This work presents a novel and computationally tractable output-to-input inference method called Multi-Layer Vector Approximate Message Passing (ML-VAMP). The proposed algorithm, derived from expectation propagation, extends earlier AMP methods that are known to achieve the replica predictions for optimality in simple linear inverse problems. Our main contribution shows that the mean-squared error (MSE) of ML-VAMP can be exactly predicted in a certain large system limit (LSL) where the numbers of layers is fixed and weight matrices are random and orthogonally-invariant with dimensions that grow to infinity. ML-VAMP is thus a principled method for output-to-input inference in deep networks with a rigorous and precise performance achievability result in high dimensions.

cs.LG↗

arXiv2017

Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems

The problem of estimating a random vector x from noisy linear measurements y = A x + w with unknown parameters on the distributions of x and w, which must also be learned, arises in a wide range of statistical learning and linear inverse problems. We show that a computationally simple iterative message-passing algorithm can provably obtain asymptotically consistent estimates in a certain high-dimensional large-system limit (LSL) under very general parameterizations. Previous message passing techniques have required i.i.d. sub-Gaussian A matrices and often fail when the matrix is ill-conditioned. The proposed algorithm, called adaptive vector approximate message passing (Adaptive VAMP) with auto-tuning, applies to all right-rotationally random A. Importantly, this class includes matrices with arbitrarily poor conditioning. We show that the parameter estimates and mean squared error (MSE) of x in each iteration converge to deterministic limits that can be precisely predicted by a simple set of state evolution (SE) equations. In addition, a simple testable condition is provided in which the MSE matches the Bayes-optimal value predicted by the replica method. The paper thus provides a computationally simple method with provable guarantees of optimality and consistency over a large class of linear inverse problems.

cs.IT↗

arXiv2017

AMP-Inspired Deep Networks for Sparse Linear Inverse Problems

Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction. First, we propose a "learned AMP" network that significantly improves upon Gregor and LeCun's "learned ISTA." Second, inspired by the recently proposed "vector AMP" (VAMP) algorithm, we propose a "learned VAMP" network that offers increased robustness to deviations in the measurement matrix from i.i.d. Gaussian. In both cases, we jointly learn the linear transforms and scalar nonlinearities of the network. Interestingly, with i.i.d. signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP. Finally, we apply our methods to two problems from 5G wireless communications: compressive random access and massive-MIMO channel estimation.

cs.IT↗

arXiv2017

Provisioning Low Latency, Resilient Mobile Edge Clouds for 5G

Network virtualization and SDN-based routing allow carriers to flexibly configure their networks in response to demand and unexpected network disruptions. However, cellular networks, by nature, pose some unique challenges because of user mobility and control/data plane partitioning, which calls for new architectures and provisioning paradigms. In this paper, we address the latter part by devising algorithms that can provision the data plane to create a distributed Mobile Edge Cloud (MEC), which provides opportunities for lower latencies and increased resilience (through placement of network functions at more distributed datacenter locations) and accounts for service disruption that could be incurred because of user mobility between the service areas of different datacenters. Through evaluations with topology and traffic data from a major carriers's network, we show that, compared to static, centralized networks, careful virtualized provisioning can yield significant savings in network costs while still minimizing service disruption due to mobility. We demonstrate that up to a 75% reduction in redundant datacenter capacity over the operator's current topology (while achieving the same level of resilience) is possible by distributing load over many mobile cloud datacenters.

cs.NI↗

arXiv2017

Hybrid Approximate Message Passing

Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with the weak edges representing interactions through aggregates of small, linearizable couplings of variables. AMP approximations based on the Central Limit Theorem can be readily applied to aggregates of many weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (HyGAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition of strong and weak edges, a performance--complexity trade-off can be achieved. Group sparsity and multinomial logistic regression problems are studied as examples of the proposed methodology.

cs.IT↗

arXiv2017

Resource Sharing Among mmWave Cellular Service Providers in a Vertically Differentiated Duopoly

With the increasing interest in the use of millimeter wave bands for 5G cellular systems comes renewed interest in resource sharing. Properties of millimeter wave bands such as massive bandwidth, highly directional antennas, high penetration loss, and susceptibility to shadowing, suggest technical advantages to spectrum and infrastructure sharing in millimeter wave cellular networks. However, technical advantages do not necessarily translate to increased profit for service providers, or increased consumer surplus. In this paper, detailed network simulations are used to better understand the economic implications of resource sharing in a vertically differentiated duopoly market for cellular service. The results suggest that resource sharing is less often profitable for millimeter wave service providers compared to microwave cellular service providers, and does not necessarily increase consumer surplus.

cs.NI↗

arXiv2017

ns-3 Implementation of the 3GPP MIMO Channel Model for Frequency Spectrum above 6 GHz

Communications at mmWave frequencies will be a key enabler of the next generation of cellular networks, due to the multi-Gbps rate that can be achieved. However, there are still several problems that must be solved before this technology can be widely adopted, primarily associated with the interplay between the variability of mmWave links and the complexity of mobile networks. An end-to-end network simulator represents a great tool to assess the performance of any proposed solution to meet the stringent 5G requirements. Given the criticality of channel propagation characteristics at higher frequencies, we present our implementation of the 3GPP channel model for the 6-100 GHz band for the ns-3 end-to-end 5G mmWave module, and detail its associated MIMO beamforming architecture.

cs.IT↗

arXiv2017

Expectation Consistent Approximate Inference: Generalizations and Convergence

Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems. This paper proposes and analyzes a generalization of Opper and Winther's expectation consistent (EC) approximate inference method. The proposed method, called Generalized Expectation Consistency (GEC), can be applied to both maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimation. Here we characterize its fixed points, convergence, and performance relative to the replica prediction of optimality.

cs.IT↗

arXiv2016

Vector Approximate Message Passing for the Generalized Linear Model

The generalized linear model (GLM), where a random vector $\boldsymbol{x}$ is observed through a noisy, possibly nonlinear, function of a linear transform output $\boldsymbol{z}=\boldsymbol{Ax}$, arises in a range of applications such as robust regression, binary classification, quantized compressed sensing, phase retrieval, photon-limited imaging, and inference from neural spike trains. When $\boldsymbol{A}$ is large and i.i.d. Gaussian, the generalized approximate message passing (GAMP) algorithm is an efficient means of MAP or marginal inference, and its performance can be rigorously characterized by a scalar state evolution. For general $\boldsymbol{A}$, though, GAMP can misbehave. Damping and sequential-updating help to robustify GAMP, but their effects are limited. Recently, a "vector AMP" (VAMP) algorithm was proposed for additive white Gaussian noise channels. VAMP extends AMP's guarantees from i.i.d. Gaussian $\boldsymbol{A}$ to the larger class of rotationally invariant $\boldsymbol{A}$. In this paper, we show how VAMP can be extended to the GLM. Numerical experiments show that the proposed GLM-VAMP is much more robust to ill-conditioning in $\boldsymbol{A}$ than damped GAMP.

cs.IT↗

arXiv2016

The Bufferbloat Problem over Intermittent Multi-Gbps mmWave Links

Due to massive available spectrum in the millimeter wave (mmWave) bands, cellular systems in these frequencies may provides orders of magnitude greater capacity than networks in conventional lower frequency bands. However, due to high susceptibility to blocking, mmWave links can be extremely intermittent in quality. This combination of high peak throughputs and intermittency can cause significant challenges in end-to-end transport-layer mechanisms such as TCP. This paper studies the particularly challenging problem of bufferbloat. Specifically, with current buffering and congestion control mechanisms, high throughput-high variable links can lead to excessive buffers incurring long latency. In this paper, we capture the performance trends obtained while adopting two potential solutions that have been proposed in the literature: Active queue management (AQM) and dynamic receive window. We show that, over mmWave links, AQM mitigates the latency but cannot deliver high throughput. The main reason relies on the fact that the current congestion control was not designed to cope with high data rates with sudden change. Conversely, the dynamic receive window approach is more responsive and therefore supports higher channel utilization while mitigating the delay, thus representing a viable solution.

cs.NI↗

arXiv2016

Denoising based Vector Approximate Message Passing

The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery. But AMP diverges with small deviations from the i.i.d.-Gaussian assumption on the measurement matrix. Recently, the vector AMP (VAMP) algorithm has been proposed to fix this problem. In this work, we show that the benefits of VAMP extend to D-VAMP.

cs.IT↗

arXiv2016

Frame Structure Design and Analysis for Millimeter Wave Cellular Systems

The millimeter-wave (mmWave) frequencies have attracted considerable attention for fifth generation (5G) cellular communication as they offer orders of magnitude greater bandwidth than current cellular systems. However, the medium access control (MAC) layer may need to be significantly redesigned to support the highly directional transmissions, ultra-low latencies and high peak rates expected in mmWave communication. To address these challenges, we present a novel mmWave MAC layer frame structure with a number of enhancements including flexible, highly granular transmission times, dynamic control signal locations, extended messaging and ability to efficiently multiplex directional control signals. Analytic formulae are derived for the utilization and control overhead as a function of control periodicity, number of users, traffic statistics, signal-to-noise ratio and antenna gains. Importantly, the analysis can incorporate various front-end MIMO capability assumptions -- a critical feature of mmWave. Under realistic system and traffic assumptions, the analysis reveals that the proposed flexible frame structure design offers significant benefits over designs with fixed frame structures similar to current 4G long-term evolution (LTE). It is also shown that fully digital beamforming architectures offer significantly lower overhead compared to analog and hybrid beamforming under equivalent power budgets.

cs.NI↗

arXiv2016

Directional Initial Access for Millimeter Wave Cellular Systems

The millimeter wave (mmWave) bands have recently attracted considerable interest for next-generation cellular systems due to the massive available bandwidths at these frequencies. However, a key challenge in designing mmWave cellular systems is initial access -- the procedure by which a mobile establishes an initial link-layer connection to a base station cell. MmWave communication relies on highly directional transmissions and the initial access procedure must thus provide a mechanism by which initial transmission directions can be searched in a potentially large angular space. Design options are compared considering different scanning and signaling procedures to evaluate access delay and system overhead. The channel structure and multiple access issues are also considered. The analysis demonstrates significant benefits of low-resolution fully digital architectures in comparison to single stream analog beamforming.

cs.IT↗

arXiv2016

An MDP Model for Optimal Handover Decisions in mmWave Cellular Networks

The new frontier in cellular networks is harnessing the enormous spectrum available at millimeter wave (mmWave) frequencies above 28 GHz. The challenging radio propagation characteristics at these frequencies, and the use of highly directional beamforming, lead to intermittent links between the base station (BS) and the user equipment (UE). In this paper, we revisit the problem of cell selection to maintain an acceptable level of service, despite the underlying intermittent link connectivity typical of mmWave links. We propose a Markov Decision Process (MDP) framework to study the properties and performance of our proposed cell selection strategy, which jointly considers several factors such as dynamic channel load and link quality. We use the Value Iteration Algorithm (VIA) to solve the MDP, and obtain the optimal set of associations. We address the multi user problem through a distributed iterative approach, in which each UE characterizes the evolution of the system based on stationary channel distribution and cell selection statistics of other UEs. Through simulation results, we show that our proposed technique makes judicious handoff choices, thereby providing a significant improvement in the overall network capacity. Further, our technique reduces the total number of handoffs, thus lowering the signaling overhead, while providing a higher quality of service to the UEs.

cs.NI↗

arXiv2016

Spectrum and Infrastructure Sharing in Millimeter Wave Cellular Networks: An Economic Perspective

The licensing model for millimeter wave bands has been the subject of considerable debate, with some industry players advocating for unlicensed use and others for traditional geographic area exclusive use licenses. Meanwhile, the massive bandwidth, highly directional antennas, high penetration loss and susceptibility to shadowing in these bands suggest certain advantages to spectrum and infrastructure sharing. However, even when sharing is technically beneficial (as recent research in this area suggests that it is), it may not be profitable. In this paper, both the technical and economic implications of resource sharing in millimeter wave networks are studied. Millimeter wave service is considered in the economic framework of a network good, where consumers' utility depends on the size of the network, and the strategic decisions of consumers and service providers are connected to detailed network simulations. The results suggest that "open" deployments of neutral small cells that serve subscribers of any service provider encourage market entry by making it easier for networks to reach critical mass, more than "open" (unlicensed) spectrum would. The conditions under which competitive service providers would prefer to share resources or not are also described.

cs.NI↗

arXiv2016

A Framework for End-to-End Evaluation of 5G mmWave Cellular Networks in ns-3

The growing demand for ubiquitous mobile data services along with the scarcity of spectrum in the sub-6 GHz bands has given rise to the recent interest in developing wireless systems that can exploit the large amount of spectrum available in the millimeter wave (mmWave) frequency range. Due to its potential for multi-gigabit and ultra-low latency links, mmWave technology is expected to play a central role in 5th Generation (5G) cellular networks. Overcoming the poor radio propagation and sensitivity to blockages at higher frequencies presents major challenges, which is why much of the current research is focused at the physical layer. However, innovations will be required at all layers of the protocol stack to effectively utilize the large air link capacity and provide the end-to-end performance required by future networks. Discrete-event network simulation will be an invaluable tool for researchers to evaluate novel 5G protocols and systems from an end-to-end perspective. In this work, we present the first-of-its-kind, open-source framework for modeling mmWave cellular networks in the ns-3 simulator. Channel models are provided along with a configurable physical and MAC-layer implementation, which can be interfaced with the higher-layer protocols and core network model from the ns-3 LTE module to simulate end-to-end connectivity. The framework is demonstrated through several example simulations showing the performance of our custom mmWave stack.

cs.NI↗

arXiv2016

Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices

The estimation of a random vector with independent components passed through a linear transform followed by a componentwise (possibly nonlinear) output map arises in a range of applications. Approximate message passing (AMP) methods, based on Gaussian approximations of loopy belief propagation, have recently attracted considerable attention for such problems. For large random transforms, these methods exhibit fast convergence and admit precise analytic characterizations with testable conditions for optimality, even for certain non-convex problem instances. However, the behavior of AMP under general transforms is not fully understood. In this paper, we consider the generalized AMP (GAMP) algorithm and relate the method to more common optimization techniques. This analysis enables a precise characterization of the GAMP algorithm fixed-points that applies to arbitrary transforms. In particular, we show that the fixed points of the so-called max-sum GAMP algorithm for MAP estimation are critical points of a constrained maximization of the posterior density. The fixed-points of the sum-product GAMP algorithm for estimation of the posterior marginals can be interpreted as critical points of a certain free energy.

cs.IT↗