arXiv subjects

Osvaldo Simeone

Publications and source records attributed to Osvaldo Simeone.

At least 343 records · Page 19Linked to original sources

arXiv2016

Optimal Fronthaul Quantization for Cloud Radio Positioning

Wireless positioning systems that are implemented by means of a Cloud Radio Access Networks (C-RANs) may provide cost-effective solutions, particularly for indoor localization. In a C-RAN, the baseband processing, including localization, is carried out at a centralized control unit (CU) based on quantized baseband signals received from the RUs over finite-capacity fronthaul links. In this paper, the problem of maximizing the localization accuracy over fronthaul quantization/compression is formulated by adopting the Cramér-Rao bound (CRB) on the localization accuracy as the performance metric of interest and information-theoretic bounds on the compression rate. The analysis explicitly accounts for the uncertainty of parameters at the CU via a robust, or worst-case, optimization formulation. The proposed algorithm leverages the Charnes-Cooper transformation and Difference-of-Convex (DC) programming, and is validated via numerical results.

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arXiv2015

Cloud Radio Access Network: Virtualizing Wireless Access for Dense Heterogeneous Systems

Cloud Radio Access Network (C-RAN) refers to the virtualization of base station functionalities by means of cloud computing. This results in a novel cellular architecture in which low-cost wireless access points, known as radio units (RUs) or remote radio heads (RRHs), are centrally managed by a reconfigurable centralized "cloud", or central, unit (CU). C-RAN allows operators to reduce the capital and operating expenses needed to deploy and maintain dense heterogeneous networks. This critical advantage, along with spectral efficiency, statistical multiplexing and load balancing gains, make C-RAN well positioned to be one of the key technologies in the development of 5G systems. In this paper, a succinct overview is presented regarding the state of the art on the research on C-RAN with emphasis on fronthaul compression, baseband processing, medium access control, resource allocation, system-level considerations and standardization efforts.

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arXiv2015

Cache Aided Wireless Networks: Tradeoffs between Storage and Latency

We investigate the fundamental information theoretic limits of cache-aided wireless networks, in which edge nodes (or transmitters) are endowed with caches that can store popular content, such as multimedia files. This architecture aims to localize popular multimedia content by proactively pushing it closer to the edge of the wireless network, thereby alleviating backhaul load. An information theoretic model of such networks is presented, that includes the introduction of a new metric, namely normalized delivery time (NDT), which captures the worst case time to deliver any requested content to the users. We present new results on the trade-off between latency, measured via the NDT, and the cache storage capacity of the edge nodes. In particular, a novel information theoretic lower bound on NDT is presented for cache aided networks. The optimality of this bound is shown for several system parameters.

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arXiv2015

Layered Downlink Precoding for C-RAN Systems with Full Dimensional MIMO

The implementation of a Cloud Radio Access Network (C-RAN) with Full Dimensional (FD)-MIMO is faced with the challenge of controlling the fronthaul overhead for the transmission of baseband signals as the number of horizontal and vertical antennas grows larger. This work proposes to leverage the special low-rank structure of FD-MIMO channel, which is characterized by a time-invariant elevation component and a time-varying azimuth component, by means of a layered precoding approach, so as to reduce the fronthaul overhead. According to this scheme, separate precoding matrices are applied for the azimuth and elevation channel components, with different rates of adaptation to the channel variations and correspondingly different impacts on the fronthaul capacity. Moreover, we consider two different Central Unit (CU) - Radio Unit (RU) functional splits at the physical layer, namely the conventional C-RAN implementation and an alternative one in which coding and precoding are performed at the RUs. Via numerical results, it is shown that the layered schemes significantly outperform conventional non-layered schemes, especially in the regime of low fronthaul capacity and large number of vertical antennas.

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arXiv2015

On the Effect of Fronthaul Latency on ARQ in C-RAN Systems

In the Cloud Radio Access Network (C-RAN) architecture, a Control Unit (CU) implements the baseband processing functionalities of a cluster of Base Stations (BSs), which are connected to it through a fronthaul network. This architecture enables centralized processing at the CU, and hence the implementation of enhanced interference mitigation strategies, but it also entails an increased decoding latency due to the transport on the fronthaul network. The fronthaul latency may offset the benefits of centralized processing when considering the performance of protocols at layer 2 and above. This letter studies the impact of fronthaul latency on the performance of standard Automatic Retransmission reQuest (ARQ) protocols, namely Stop and Wait, Go-Back-N and Selective Repeat. The performance of the C-RAN architecture in terms of throughput and efficiency is compared to the that of a conventional cellular system with local processing, as well as with that of a proposed hybrid C-RAN system in which BSs can perform decoding. The dynamics of the system are modeled as a multi-dimensional Markov process that includes sub-chains to capture the temporal correlation of interference and channel gains. Numerical results yield insights into the impact of system parameters such as fronthaul latency and signal-to-interference ratio on different ARQ protocols.

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arXiv2015

Hypergraph-Based Analysis of Clustered Cooperative Beamforming with Application to Edge Caching

The evaluation of the performance of clustered cooperative beamforming in cellular networks generally requires the solution of complex non-convex optimization problems. In this letter, a framework based on a hypergraph formalism is proposed that enables the derivation of a performance characterization of clustered cooperative beamforming in terms of per-user degrees of freedom (DoF) via the efficient solution of a coloring problem. An emerging scenario in which clusters of cooperative base stations (BSs) arise is given by cellular networks with edge caching. In fact, clusters of BSs that share the same requested files can jointly beamform the corresponding encoded signals. Based on this observation, the proposed framework is applied to obtain quantitative insights into the optimal use of cache and backhaul resources in cellular systems with edge caching. Numerical examples are provided to illustrate the merits of the proposed framework.

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arXiv2015

Optimal Fronthaul Compression for Synchronization in the Uplink of Cloud Radio Access Networks

A key problem in the design of cloud radio access networks (CRANs) is that of devising effective baseband compression strategies for transmission on the fronthaul links connecting a remote radio head (RRH) to the managing central unit (CU). Most theoretical works on the subject implicitly assume that the RRHs, and hence the CU, are able to perfectly recover time synchronization from the baseband signals received in the uplink, and focus on the compression of the data fields. This paper instead dose not assume a priori synchronization of RRHs and CU, and considers the problem of fronthaul compression design at the RRHs with the aim of enhancing the performance of time and phase synchronization at the CU. The problem is tackled by analyzing the impact of the synchronization error on the performance of the link and by adopting information and estimationtheoretic performance metrics such as the rate-distortion function and the Cramer-Rao bound (CRB). The proposed algorithm is based on the Charnes-Cooper transformation and on the Difference of Convex (DC) approach, and is shown via numerical results to outperform conventional solutions.

cs.NI↗

arXiv2015

Inter-Layer Per-Mobile Optimization of Cloud Mobile Computing: A Message-Passing Approach

Cloud mobile computing enables the offloading of computation-intensive applications from a mobile device to a cloud processor via a wireless interface. In light of the strong interplay between offloading decisions at the application layer and physical-layer parameters, which determine the energy and latency associated with the mobile-cloud communication, this paper investigates the inter-layer optimization of fine-grained task offloading across both layers. In prior art, this problem was formulated, under a serial implementation of processing and communication, as a mixed integer program, entailing a complexity that is exponential in the number of tasks. In this work, instead, algorithmic solutions are proposed that leverage the structure of the call graphs of typical applications by means of message passing on the call graph, under both serial and parallel implementations of processing and communication. For call trees, the proposed solutions have a linear complexity in the number of tasks, and efficient extensions are presented for more general call graphs that include "map" and "reduce"-type tasks. Moreover, the proposed schemes are optimal for the serial implementation, and provide principled heuristics for the parallel implementation. Extensive numerical results yield insights into the impact of inter-layer optimization and on the comparison of the two implementations.

cs.DC↗

arXiv2015

Lossy Compression with Privacy Constraints: Optimality of Polar Codes

A lossy source coding problem with privacy constraint is studied in which two correlated discrete sources $X$ and $Y$ are compressed into a reconstruction $\hat{X}$ with some prescribed distortion $D$. In addition, a privacy constraint is specified as the equivocation between the lossy reconstruction $\hat{X}$ and $Y$. This models the situation where a certain amount of source information from one user is provided as utility (given by the fidelity of its reconstruction) to another user or the public, while some other correlated part of the source information $Y$ must be kept private. In this work, we show that polar codes are able, possibly with the aid of time sharing, to achieve any point in the optimal rate-distortion-equivocation region identified by Yamamoto, thus providing a constructive scheme that obtains the optimal tradeoff between utility and privacy in this framework.

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arXiv2015

HARQ Buffer Management: An Information-Theoretic View

A key practical constraint on the design of Hybrid automatic repeat request (HARQ) schemes is the size of the on-chip buffer that is available at the receiver to store previously received packets. In fact, in modern wireless standards such as LTE and LTE-A, the HARQ buffer size is one of the main drivers of the modem area and power consumption. This has recently highlighted the importance of HARQ buffer management, that is, of the use of buffer-aware transmission schemes and of advanced compression policies for the storage of received data. This work investigates HARQ buffer management by leveraging information-theoretic achievability arguments based on random coding. Specifically, standard HARQ schemes, namely Type-I, Chase Combining and Incremental Redundancy, are first studied under the assumption of a finite-capacity HARQ buffer by considering both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds light on the impact of different compression strategies, namely the conventional compression log-likelihood ratios and the direct digitization of baseband signals, on the throughput. Then, coding strategies based on layered modulation and optimized coding blocklength are investigated, highlighting the benefits of HARQ buffer-aware transmission schemes. The optimization of baseband compression for multiple-antenna links is also studied, demonstrating the optimality of a transform coding approach.

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arXiv2015

Interference Spins: Scheduling of Multiple Interfering Two-Way Wireless Links

Two-way is a dominant mode of communication in wireless systems. Departing from the tradition to optimize each transmission direction separately, recent work has demonstrated that, for time-division duplex (TDD) systems, optimizing the schedule of the two transmission directions depending on traffic load and interference condition leads to performance gains. In this letter, a general network of multiple interfering two-way links is studied under the assumption of a balanced load in the two directions for each link. Using the notion of interference spin, we introduce an algebraic framework for the optimization of two-way scheduling, along with an efficient optimization algorithm that is based on the pruning of a properly defined topology graph and dynamic programming. Numerical results demonstrate multi-fold rate gains with respect to baseline solutions, especially for worst-case (5%-ile) rates.

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arXiv2015

Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.

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arXiv2014

Fronthaul Compression and Precoding Design for C-RANs over Ergodic Fading Channel

This work investigates the joint design of fronthaul compression and precoding for the downlink of Cloud Radio Access Networks (C-RANs). In a C-RAN, a central unit (CU) performs the baseband processing for a cluster of radio units (RUs) that receive compressed baseband samples from the CU through low-latency fronthaul links. Most previous works on the design of fronthaul compression and precoding assume constant channels and instantaneous channel state information (CSI) at the CU. This work, in contrast, concentrates on a more practical scenario with block-ergodic channels and considers either instantaneous or stochastic CSI at the CU. Moreover, the analysis encompasses both the Compression-After-Precoding (CAP) and the Compression-Before-Precoding (CBP) schemes. With the CAP approach, which is the standard C-RAN solution, the CU performs channel coding and precoding and then the CU compresses and forwards the resulting baseband signals on the fronthaul links to the RUs. With the CBP scheme, instead, the CU does not perform precoding but rather forwards separately the information messages of a subset of mobile stations (MSs) along with the compressed precoding matrices to the each RU, which then performs precoding. Optimization algorithms over fronthaul compression and precoding for both CAP and CBP are proposed that are based on a stochastic successive upper-bound minimization approach. Via numerical results, the relative merits of the two strategies under either instantaneous or stochastic CSI are evaluated as a function of system parameters such as fronthaul capacity and channel coherence time.

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arXiv2014

Cloud Radio-Multistatic Radar: Joint Optimization of Code Vector and Backhaul Quantization

A multistatic radar set-up is considered in which distributed receive antennas are connected to a Fusion Center (FC) via limited-capacity backhaul links. Similar to cloud radio access networks in communications, the receive antennas quantize the received baseband signal before transmitting it to the FC. The problem of maximizing the detection performance at the FC jointly over the code vector used by the transmitting antenna and over the statistics of the noise introduced by backhaul quantization is investigated. Specifically, adopting the information-theoretic criterion of the Bhattacharyya distance to evaluate the detection performance at the FC and information-theoretic measures of the quantization rate, the problem at hand is addressed via a Block Coordinate Descent (BCD) method coupled with Majorization-Minimization (MM). Numerical results demonstrate the advantages of the proposed joint optimization approach over more conventional solutions that perform separate optimization.

math.ST↗

arXiv2014

Signal and Interference Leakage Minimization in MIMO Uplink-Downlink Cellular Networks

Linear processing in the spatial domain at the base stations (BSs) and at the users of MIMO cellular systems enables the control of both inter-cell and intra-cell interference. A number of iterative algorithms have been proposed that allow the BSs and the users to calculate the transmit-side and the receive-side linear processors in a distributed manner via message exchange based only on local channel state information. In this paper, a novel such strategy is proposed that requires the exchange of unitary matrices between BSs and users. Specifically, focusing on a general both uplink- and downlink-operated cells, the design of the linear processors is obtained as the alternating optimization solution of the problem of minimizing the weighted sum of the downlink and uplink inter-cell interference powers and of the signal power leaked in the space orthogonal to the receive subspaces. Intra-cell interference is handled via minimum mean square error (MMSE) or the zero-forcing (ZF) precoding for downlink-operated cells and via joint decoding for the uplink-operated cells. Numerical results validate the advantages of the proposed technique with respect to existing similar techniques that account only for the interference power in the optimization.

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arXiv2014

Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.

cs.IT↗

arXiv2014

Message and State Cooperation in a Relay Channel When Only the Relay Knows the State

A state-dependent relay channel is studied in which strictly causal channel state information is available at the relay and no state information is available at the source and destination. The source and the relay are connected via two unidirectional out-of-band orthogonal links of finite capacity, and a state-dependent memoryless channel connects the source and the relay, on one side, and the destination, on the other. Via the orthogonal links, the source can convey information about the message to be delivered to the destination to the relay while the relay can forward state information to the source. This exchange enables cooperation between the source and the relay on transmission of message and state information to the destination. First, two achievable schemes are proposed that exploit both message and state cooperation. It is shown that a transmission scheme inspired by noisy network coding performs better than a strategy based on block Markov coding and backward decoding. Next, based on the given achievable schemes and appropriate upper bounds, capacity results are identified for some special cases. Finally, a Gaussian model is studied, along with corresponding numerical results that illuminate the relative merits of state and message cooperation.

cs.IT↗

arXiv2014

Beamforming Design for Joint Localization and Data Transmission in Distributed Antenna System

A distributed antenna system is studied whose goal is to provide data communication and positioning functionalities to Mobile Stations (MSs). Each MS receives data from a number of Base Stations (BSs), and uses the received signal not only to extract the information but also to determine its location. This is done based on Time of Arrival (TOA) or Time Difference of Arrival (TDOA) measurements, depending on the assumed synchronization conditions. The problem of minimizing the overall power expenditure of the BSs under data throughput and localization accuracy requirements is formulated with respect to the beamforming vectors used at the BSs. The analysis covers both frequency-flat and frequency-selective channels, and accounts also for robustness constraints in the presence of parameter uncertainty. The proposed algorithmic solutions are based on rank-relaxation and Difference-of-Convex (DC) programming.

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