arXiv subjects

Seok-Hwan Park

Publications and source records attributed to Seok-Hwan Park.

41 records · Page 3Linked to original sources

arXiv2013

Performance Evaluation of Multiterminal Backhaul Compression for Cloud Radio Access Networks

In cloud radio access networks (C-RANs), the baseband processing of the available macro- or pico/femto-base stations (BSs) is migrated to control units, each of which manages a subset of BS antennas. The centralized information processing at the control units enables effective interference management. The main roadblock to the implementation of C-RANs hinges on the effective integration of the radio units, i.e., the BSs, with the backhaul network. This work first reviews in a unified way recent results on the application of advanced multiterminal, as opposed to standard point-to-point, backhaul compression techniques. The gains provided by multiterminal backhaul compression are then confirmed via extensive simulations based on standard cellular models. As an example, it is observed that multiterminal compression strategies provide performance gains of more than 60% for both the uplink and the downlink in terms of the cell-edge throughput.

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arXiv2013

Multi-Layer Transmission and Hybrid Relaying for Relay Channels with Multiple Out-of-Band Relays

In this work, a relay channel is studied in which a source encoder communicates with a destination decoder through a number of out-of-band relays that are connected to the decoder through capacity-constrained digital backhaul links. This model is motivated by the uplink of cloud radio access networks. In this scenario, a novel transmission and relaying strategies are proposed in which multi-layer transmission is used, on the one hand, to adaptively leverage the different decoding capabilities of the relays and, on the other hand, to enable hybrid decode-and-forward (DF) and compress-and-forward (CF) relaying. The hybrid relaying strategy allows each relay to forward part of the decoded messages and a compressed version of the received signal to the decoder. The problem of optimizing the power allocation across the layers and the compression test channels is formulated. Albeit non-convex, the derived problem is found to belong to the class of so called complementary geometric programs (CGPs). Using this observation, an iterative algorithm based on the homotopy method is proposed that achieves a stationary point of the original problem by solving a sequence of geometric programming (GP), and thus convex, problems. Numerical results are provided that show the effectiveness of the proposed multi-layer hybrid scheme in achieving performance close to a theoretical (cutset) upper bound.

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arXiv2013

Joint Precoding and Multivariate Backhaul Compression for the Downlink of Cloud Radio Access Networks

This work studies the joint design of precoding and backhaul compression strategies for the downlink of cloud radio access networks. In these systems, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art approaches, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals of different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate with respect to both the precoding matrix and the joint correlation matrix of the quantization noises is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. Moreover, in order to enable the practical implementation of multivariate compression across BSs, a novel architecture is proposed based on successive steps of minimum mean-squared error (MMSE) estimation and per-BS compression. Robust design with respect to imperfect channel state information is also discussed. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on the separate design of precoding and compression or independent compression across the BSs.

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arXiv2013

Multi-Layer Hybrid-ARQ for an Out-of-Band Relay Channel

This paper addresses robust communication on a fading relay channel in which the relay is connected to the decoder via an out-of-band digital link of limited capacity. Both the source-to-relay and the source-to-destination links are subject to fading gains, which are generally unknown to the encoder prior to transmission. To overcome this impairment, a hybrid automatic retransmission request (HARQ) protocol is combined with multi-layer broadcast transmission, thus allowing for variable-rate decoding. Moreover, motivated by cloud radio access network applications, the relay operation is limited to compress-and-forward. The aim is maximizing the throughput performance as measured by the average number of successfully received bits per channel use, under either long-term static channel (LTSC) or short-term static channel (STSC) models. In order to opportunistically leverage better channel states based on the HARQ feedback from the decoder, an adaptive compression strategy at the relay is also proposed. Numerical results confirm the effectiveness of the proposed strategies.

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arXiv2012

Robust and Efficient Distributed Compression for Cloud Radio Access Networks

This work studies distributed compression for the uplink of a cloud radio access network where multiple multi-antenna base stations (BSs) are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed source coding strategies are potentially beneficial, and can be implemented via sequential source coding with side information. For the problem of compression with side information, available compression strategies based on the criteria of maximizing the achievable rate or minimizing the mean square error are reviewed first. It is observed that, in either case, each BS requires information about a specific covariance matrix in order to realize the advantage of distributed source coding. Since this covariance matrix depends on the channel realizations corresponding to other BSs, a robust compression method is proposed for a practical scenario in which the information about the covariance available at each BS is imperfect. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.

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