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In this pilot project, we teamed up with artists to develop new workflows for 2D animation while producing a short educational cartoon. We identified several workflows to streamline the animation process, bringing the artists' vision to the screen more effectively., Comment: Project page: https://genai-2d-character-animation.github.io/

We propose a novel scheme for estimating the large-scale gains of the channels between user terminals (UTs) and base stations (BSs) in a cellular system. The scheme leverages TDD operation, uplink (UL) training by means of properly designed non-orthogonal pilot codes, and massive antenna arrays at the BSs. Subject to Q resource elements allocated for UL training and using the new scheme, a BS is able to estimate the large-scale channel gains of K users transmitting UL pilots in its cell and in nearby cells, provided K<=Q^2. Such knowledge of the large-scale channel gains of nearby out-of-cells users can be exploited at the BS to mitigate interference to the out-of-cell users that experience the highest levels of interference from the BS. We investigate the large-scale gain estimation performance provided by a variety of non-orthogonal pilot codebook designs. Our simulations suggest that among all the code designs considered, Grassmannian line-packing type codes yield the best large-scale channel gain estimation performance., Comment: 6 pages, 3 figures, and published in IEEE ICC 2018

The decoupling of data and control planes, as proposed for 5G networks, will enable the efficient implementation of multitier networks where user equipment (UE) nodes obtain coverage and connectivity through the top-tier macro-cells, and, at the same time, achieve high-throughput low-latency communication through lower tiers in the hierarchy. This paper considers a new architecture for such lower tiers, dubbed fog massive MIMO, where the UEs are able to establish high-throughput low-latency data links in a seamless and opportunistic manner, as they travel through a dense fog of high-capacity wireless infrastructure nodes, referred to as remote radio heads (RRHs). Traditional handover mechanisms in dense multicell networks inherently give rise to frequent handovers and pilot sequence re-assignments, incurring, as a result, excessive protocol overhead and significant latency. In the proposed fog massive MIMO architecture, UEs seamlessly and implicitly associate themselves to the most convenient RRHs in a completely autonomous manner. Each UE makes use of a unique uplink pilot sequence, and pilot contamination is mitigated by a novel coded "on-the-fly" pilot contamination control mechanism. We analyze the spectral efficiency and the outage probability of the proposed architecture via stochastic geometry, using some recent results on unique coverage in Boolean models, and provide a detailed comparison with respect to an idealized baseline massive MIMO cellular system, that neglects protocol overhead and latency due to explicit user-cell association. Our analysis, supported by extensive system simulation, reveals that there exists a "sweet spot" of the per-pilot user load (number of users per pilot), such that the proposed system achieves spectral efficiency close to that of an ideal cellular system with the minimum distance user-base station association and no pilot/handover overhead., Comment: 32 pages, 7 figures and 1 Table

We investigate the viability of using machine-learning techniques for estimating user-channel features at a large-array base station (BS). In the scenario we consider, user-pilot broadcasts are observed and processed by the BS to extract angle-of-arrival (AoA) specific information about propagation-channel features, such as received signal strength and relative path delay. The problem of interest involves using this information to predict the angle-of-departure (AoD) of the dominant propagation paths in the user channels, i.e., channel features not directly observable at the BS. To accomplish this task, the data collected in the same propagation environment are used to train neural networks. Our studies rely on ray-tracing channel data that have been calibrated against measurements from Shinjuku Square, a famous hotspot in Tokyo, Japan. We demonstrate that the observed features at the BS side are correlated with the angular features at the user side. We train neural networks that exploit different combinations of measured features at the BS to infer the unknown parameters at the users. The evaluation based on standard statistical performance metrics suggests that such data-driven methods have the potential to predict unobserved channel features from observed ones., Comment: 6 pages, 6 figures, to appear in 2018 IEEE International Conference on Communications (ICC)

We focus on C-RAN random access protocols for IoT devices that yield low-latency high-rate active-device detection in dense networks of large-array remote radio heads. In this context, we study the problem of learning the strengths of links between detected devices and network sites. In particular, we develop recommendation-system inspired algorithms, which exploit random-access observations collected across the network to classify links between active devices and network sites across the network. Our simulations and analysis reveal the potential merit of data-driven schemes for such on-the-fly link classification and subsequent resource allocation across a wide-area network., Comment: This manuscript has been submitted to 2018 IEEE International Conference on Communications Workshops (ICC Workshops): Promises and Challenges of Machine Learning in Communication Networks

This work studies a variant of hybrid beamforming, namely, hybrid beamforming with selection (HBwS), as an attractive solution to reduce the hardware cost of multi-user Massive Multiple-Input-Multiple-Output systems, while retaining good performance. Unlike conventional hybrid beamforming, in a transceiver with HBwS, the antenna array is fed by an analog beamforming matrix with $\bar{L}$ input ports, where $\bar{L}$ is larger than the number of up/down-conversion chains $\bar{K}$. A bank of switches connects the instantaneously best $\bar{K}$ out of the $\bar{L}$ input ports to the up/down-conversion chains. The analog beamformer is designed based on average channel statistics and therefore needs to be updated only infrequently, while the switches operate based on instantaneous channel knowledge. HBwS allows use of simpler hardware in the beamformer that only need to adjust to the statistics, while also enabling the effective analog beams to adapt to the instantaneous channel variations via switching. This provides better user separability, beamforming gain, and/or simpler hardware than some conventional hybrid schemes. In this work, a novel design for the analog beamformer is derived and approaches to reduce the hardware and computational cost of a multi-user HBwS system are explored. In addition, we study how $\bar{L}$, the switch bank architecture, the number of users and the channel estimation overhead impact system performance., Comment: Accepted to Transactions on Signal Processing

We consider wireless networks of remote radio heads (RRH) with large antenna-arrays, operated in TDD, with uplink (UL) training and channel-reciprocity based downlink (DL) transmission. To achieve large area spectral efficiencies, we advocate the use of methods that rely on rudimentary scheduling, decentralized operation at each RRH and user-centric DL transmission. A slotted system is assumed, whereby users are randomly scheduled (e.g., via shuffled round robin) in slots and across the limited pilot dimensions per slot. As a result, multiple users in the vicinity of an RRH can simultaneously transmit pilots on the same pilot dimension (and thus interfering with one another). Each RRH performs rudimentary processing of the pilot observations in "sectors". In a sector, the RRH is able to resolve a scheduled user's channel when that user is determined to be the only one among the scheduled users (on the same pilot dimension) with significant received power in the sector. Subsequently, only the subset of scheduled users whose channels are resolved in at least one sector can be served by the system. We consider a spatially consistent evaluation of the area multiplexing gains by means of a Poisson Point Process (PPP) problem formulation where RRHs, blockers, scatterers and scheduled user terminals are all PPPs with individual intensities. Also, we study directional training at the user terminals. Our simulations suggest that, by controlling the intensity of the scheduled user PPP and the user-pilot beam-width, many fold improvements can be expected in area multiplexing gains with respect to conventional spatial pilot reuse systems., Comment: In the proceedings of the 50th Asilomar Conference on Signals, Systems, and Computers, Nov. 2016

We consider a single-cell scenario involving a single base station (BS) with a massive array serving multi-antenna terminals in the downlink of a mmWave channel. We present a class of multiuser user MIMO schemes, which rely on uplink training from the user terminals, and on uplink/downlink channel reciprocity. The BS employs virtual sector-based processing according to which, user-channel estimation and data transmission are performed in parallel over non-overlapping angular sectors. The uplink training schemes we consider are non-orthogonal, that is, we allow multiple users to transmit pilots on the same pilot dimension (thereby potentially interfering with one another). Elementary processing allows each sector to determine the subset of user channels that can be resolved on the sector (effectively pilot contamination free) and, thus, the subset of users that can be served by the sector. This allows resolving multiple users on the same pilot dimension at different sectors, thereby increasing the overall multiplexing gains of the system. Our analysis and simulations reveal that, by using appropriately designed directional training beams at the user terminals, the sector-based transmission schemes we present can yield substantial spatial multiplexing and ergodic user-rates improvements with respect to their orthogonal-training counterparts., Comment: 12 pages, 7 figures

Technologies for mmWave communication are at the forefront of investigations in both industry and academia, as the mmWave band offers the promise of orders of magnitude additional available bandwidths to what has already been allocated to cellular networks. The much larger number of antennas that can be supported in a small footprint at mmWave bands can be leveraged to harvest massive-MIMO type beamforming and spatial multiplexing gains. Similar to LTE systems, two prerequisites for harvesting these benefits are detecting users and acquiring user channel state information (CSI) in the training phase. However, due to the fact that mmWave channels encounter much harsher propagation and decorrelate much faster, the tasks of user detection and CSI acquisition are both imperative and much more challenging than in LTE bands. In this paper, we investigate the problem of fast user detection and CSI acquisition in the downlink of small cell mmWave networks. We assume TDD operation and channel-reciprocity based CSI acquisition. To achieve densification benefits we propose pilot designs and channel estimators that leverage a combination of aggressive pilot reuse with fast user detection at the base station and compressed sensing channel estimation. As our simulations show, the number of users that can be simultaneously served by the entire mmWave-band network with the proposed schemes increases substantially with respect to traditional compressed sensing based approaches with conventional pilot reuse., Comment: 7 pages, 3 figures, submittd to IEEE ICC 2016

Dense large-scale antenna deployments are one of the most promising technologies for delivering very large throughputs per unit area in the downlink (DL) of cellular networks. We consider such a dense deployment involving a distributed system formed by multi-antenna remote radio head (RRH) units connected to the same fronthaul serving a geographical area. Knowledge of the DL channel between each active user and its nearby RRH antennas is most efficiently obtained at the RRHs via reciprocity based training, that is, by estimating a user's channel using uplink (UL) pilots transmitted by the user, and exploiting the UL/DL channel reciprocity. We consider aggressive pilot reuse across an RRH system, whereby a single pilot dimension is simultaneously assigned to multiple active users. We introduce a novel coded pilot approach, which allows each RRH unit to detect pilot collisions, i.e., when more than a single user in its proximity uses the same pilot dimensions. Thanks to the proposed coded pilot approach, pilot contamination can be substantially avoided. As shown, such strategy can yield densification benefits in the form of increased multiplexing gain per UL pilot dimension with respect to conventional reuse schemes and some recent approaches assigning pseudorandom pilot vectors to the active users., Comment: 7 pages, 9 figures, extension of ICC 2016 submission

Two key traits of 5G cellular networks are much higher base station (BS) densities - especially in the case of low-power BSs - and the use of massive MIMO at these BSs. This paper explores how massive MIMO can be used to jointly maximize the offloading gains and minimize the interference challenges arising from adding small cells. We consider two interference management approaches: joint transmission (JT) with local precoding, where users are served simultaneously by multiple BSs without requiring channel state information exchanges among cooperating BSs, and resource blanking, where some macro BS resources are left blank to reduce the interference in the small cell downlink. A key advantage offered by massive MIMO is channel hardening, which enables to predict instantaneous rates a priori. This allows us to develop a unified framework, where resource allocation is cast as a network utility maximization (NUM) problem, and to demonstrate large gains in cell-edge rates based on the NUM solution. We propose an efficient dual subgradient based algorithm, which converges towards the NUM solution. A scheduling scheme is also proposed to approach the NUM solution. Simulations illustrate more than 2x rate gain for 10th percentile users vs. an optimal association without interference management.

Multi-tier networks with large-array base stations (BSs) that are able to operate in the "massive MIMO" regime are envisioned to play a key role in meeting the exploding wireless traffic demands. Operated over small cells with reciprocity-based training, massive MIMO promises large spectral efficiencies per unit area with low overheads. Also, near-optimal user-BS association and resource allocation are possible in cellular massive MIMO HetNets using simple admission control mechanisms and rudimentary BS schedulers, since scheduled user rates can be predicted a priori with massive MIMO. Reciprocity-based training naturally enables coordinated multi-point transmission (CoMP), as each uplink pilot inherently trains antenna arrays at all nearby BSs. In this paper we consider a distributed-MIMO form of CoMP, which improves cell-edge performance without requiring channel state information exchanges among cooperating BSs. We present methods for harmonized operation of distributed and cellular massive MIMO in the downlink that optimize resource allocation at a coarser time scale across the network. We also present scheduling policies at the resource block level which target approaching the optimal allocations. Simulations reveal that the proposed methods can significantly outperform the network-optimized cellular-only massive MIMO operation (i.e., operation without CoMP), especially at the cell edge.

The use of a very large number of antennas at each base station site (referred to as "Massive MIMO") is one of the most promising approaches to cope with the predicted wireless data traffic explosion. In combination with Time Division Duplex and with simple per-cell processing, it achieves large throughput per cell, low latency, and attractive power efficiency performance. Following the current wireless technology trend of moving to higher frequency bands and denser small cell deployments, a large number of antennas can be implemented within a small form factor even in small cell base stations. In a heterogeneous network formed by large (macro) and small cell BSs, a key system optimization problem consists of "load balancing", that is, associating users to BSs in order to avoid congested hot-spots and/or under-utilized infrastructure. In this paper, we consider the user-BS association problem for a massive MIMO heterogeneous network. We formulate the problem as a network utility maximization, and provide a centralized solution in terms of the fraction of transmission resources (time-frequency slots) over which each user is served by a given BS. Furthermore, we show that such a solution is physically realizable, i.e., there exists a sequence of integer scheduling configurations realizing (by time-sharing) the optimal fractions. While this solution is optimal, it requires centralized computation. Then, we also consider decentralized user-centric schemes, formulated as non-cooperative games where each user makes individual selfish association decisions based only on its local information. We identify a class of schemes such that their Nash equilibrium is very close to the global centralized optimum. Hence, these user-centric algorithms are attractive not only for their simplicity and fully decentralized implementation, but also because they operate near the system "social" optimum.

Large-scale distributed Multiuser MIMO (MU-MIMO) is a promising wireless network architecture that combines the advantages of "massive MIMO" and "small cells." It consists of several Access Points (APs) connected to a central server via a wired backhaul network and acting as a large distributed antenna system. We focus on the downlink, which is both more demanding in terms of traffic and more challenging in terms of implementation than the uplink. In order to enable multiuser joint precoding of the downlink signals, channel state information at the transmitter side is required. We consider Time Division Duplex (TDD), where the {\em downlink} channels can be learned from the user uplink pilot signals, thanks to channel reciprocity. Furthermore, coherent multiuser joint precoding is possible only if the APs maintain a sufficiently accurate relative timing and phase synchronization. AP synchronization and TDD reciprocity calibration are two key problems to be solved in order to enable distributed MU-MIMO downlink. In this paper, we propose novel over-the-air synchronization and calibration protocols that scale well with the network size. The proposed schemes can be applied to networks formed by a large number of APs, each of which is driven by an inexpensive 802.11-grade clock and has a standard RF front-end, not explicitly designed to be reciprocal. Our protocols can incorporate, as a building block, any suitable timing and frequency estimator. Here we revisit the problem of joint ML timing and frequency estimation and use the corresponding Cramer-Rao bound to evaluate the performance of the synchronization protocol. Overall, the proposed synchronization and calibration schemes are shown to achieve sufficient accuracy for satisfactory distributed MU-MIMO performance., Comment: Replaced Figure 5 with correct version

A new coding scheme for image transmission over noisy channel is proposed. Similar to standard image compression, the scheme includes a linear transform followed by successive refinement scalar quantization. Unlike conventional schemes, in the proposed system the quantized transform coefficients are linearly mapped into channel symbols using systematic linear encoders. This fixed-to-fixed length "linear index coding" approach avoids the use of an explicit entropy coding stage (e.g., arithmetic or Huffman coding), which is typically fragile to channel post-decoding residual errors. We use linear codes over GF(4), which are particularly suited for this application, since they are matched to the dead-zone quantizer symbol alphabet and to the QPSK modulation used on the deep-space communication channel. We optimize the proposed system where the linear codes are systematic Raptor codes over GF(4). The rateless property of Raptor encoders allows to achieve a "continuum" of coding rates, in order to accurately match the channel coding rate to the transmission channel capacity and to the quantized source entropy rate for each transform subband and refinement level. Comparisons are provided with respect to the concatenation of state-of-the-art image coding and channel coding schemes used by Jet Propulsion Laboratories (JPL) for the Mars Exploration Rover (MER) Mission., Comment: 36 pages, 5 figures, to be submitted to IEEE Trans. on Communications

In this paper the multicasting of independent parallel Gaussian sources over a binary erasure broadcasted channel is considered. Multiresolution embedded quantizer and layered joint source-channel coding schemes are used in order to serve simultaneously several users at different channel capacities. The convex nature of the rate-distortion function, computed by means of reverse water-filling, allows us to solve relevant convex optimization problems corresponding to different performance criteria. Then, layered joint source-channel codes are constructed based on the concatenation of embedded scalar quantizers with binary rateless encoders., Comment: 5 pages, 4 figures, submitted to ISIT-09