Uri Erez

Bio: Uri Erez is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Decoding methods & Communication channel. The author has an hindex of 5, co-authored 10 publications receiving 431 citations.

A close-to-capacity dirty paper coding scheme

Abstract: The "writing on dirty paper"-channel model offers an information-theoretic framework for precoding techniques for canceling arbitrary interference known at the transmitter. It indicates that lossless precoding is theoretically possible at any signal-to-noise ratio (SNR), and thus dirty-paper coding may serve as a basic building block in both single-user and multiuser communication systems. We design an end-to-end coding realization of a system materializing a significant portion of the promised gains. We employ multidimensional quantization based on trellis shaping at the transmitter. Coset decoding is implemented at the receiver using "virtual bits." Combined with iterative decoding of capacity-approaching codes we achieve an improvement of 2dB over the best scalar quantization scheme. Code design is done using the EXIT chart technique.

The ML decoding performance of LDPC ensembles over Z/sub q/

Abstract: We derive the asymptotic spectra of low-density parity-check (LDPC) ensembles over Z/sub q/. We consider two ensembles of LDPC matrices, one is binary and the other q-ary. We also show that for modulo-additive noise channels, both ensembles achieve the random coding error exponent, for graphs with sufficiently large connectivity.

Rateless space-time coding

Abstract: Rateless codes are good codes of infinite length that have the property that prefixes of such codes are themselves good codes. This makes them attractive for applications in which the channel quality is uncertain, where systems transmit as much of a codeword as necessary for decoding to be possible. In particular, rateless codes are potentially attractive for wireless communication. In a recent work, a rateless coding scheme was proposed for the AWGN channel, based on layering, repetition and random dithering. We extend this scheme to multiple-input single-output (MISO) Gaussian channels. We show that the rate loss associated with orthogonal design space-time codes may be alleviated by layering and dithering, very similar to the rateless approach for the AWGN channel. We then combine the two schemes and arrive at a close-to-capacity rateless code for MISO channels. The required complexity depends on the fraction of capacity that is targeted, is linear in the capacity of the channel and does not depend on the number of transmit antennas. Furthermore, the coding scheme uses only one base AWGN code

Writing on many pieces of dirty paper at once: the binary case

Abstract: We study the problem of sending a common message to several users on a channel with side information. Specifically, each user experiences an additive interference which is known only to the sender. The sender has to simultaneously adapt its transmitted signal to all the interferences. We derive upper and lower bounds for the special case of binary channels and derive some optimality conditions.

Enhanced communication over networks using joint matrix decompositions

Abstract: The disclosure describes examples of systems and methods for communication networks including multiple-input multiple output MIMO channels. In these examples, based on a novel decomposition of two or more channel matrices or functions thereof, a MIMO channel may be treated as a plurality of parallel scalar additive white Gaussian noise (AWGN) channels.

MIMO Broadcast Channels With Finite-Rate Feedback

Abstract: Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well-known zero-forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite-rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signal-to-noise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to point-to-point multiple-input multiple-output (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR

Transmitter Optimization for the Multi-Antenna Downlink With Per-Antenna Power Constraints

Abstract: This paper considers the transmitter optimization problem for a multiuser downlink channel with multiple transmit antennas at the base-station. In contrast to the conventional sum-power constraint on the transmit antennas, this paper adopts a more realistic per-antenna power constraint, because in practical implementations each antenna is equipped with its own power amplifier and is limited individually by the linearity of the amplifier. Assuming perfect channel knowledge at the transmitter, this paper investigates two different transmission schemes under the per-antenna power constraint: a minimum-power beamforming design for downlink channels with a single antenna at each remote user and a capacity-achieving transmitter design for downlink channels with multiple antennas at each remote user. It is shown that in both cases, the per-antenna downlink transmitter optimization problem may be transformed into a dual uplink problem with an uncertain noise. This generalizes previous uplink-downlink duality results and transforms the per-antenna transmitter optimization problem into an equivalent minimax optimization problem. Further, it is shown that various notions of uplink-downlink duality may be unified under a Lagrangian duality framework. This new interpretation of duality gives rise to efficient numerical optimization techniques for solving the downlink per-antenna transmitter optimization problem

Achieving 1/2 log (1+SNR) on the AWGN channel with lattice encoding and decoding

Abstract: We address an open question, regarding whether a lattice code with lattice decoding (as opposed to maximum-likelihood (ML) decoding) can achieve the additive white Gaussian noise (AWGN) channel capacity We first demonstrate how minimum mean-square error (MMSE) scaling along with dithering (lattice randomization) techniques can transform the power-constrained AWGN channel into a modulo-lattice additive noise channel, whose effective noise is reduced by a factor of /spl radic/(1+SNR/SNR) For the resulting channel, a uniform input maximizes mutual information, which in the limit of large lattice dimension becomes 1/2 log (1+SNR), ie, the full capacity of the original power constrained AWGN channel We then show that capacity may also be achieved using nested lattice codes, the coarse lattice serving for shaping via the modulo-lattice transformation, the fine lattice for channel coding We show that such pairs exist for any desired nesting ratio, ie, for any signal-to-noise ratio (SNR) Furthermore, for the modulo-lattice additive noise channel lattice decoding is optimal Finally, we show that the error exponent of the proposed scheme is lower bounded by the Poltyrev exponent

Multiuser MIMO Achievable Rates With Downlink Training and Channel State Feedback

Abstract: In this paper, we consider a multiple-input-multiple-output (MIMO) fading broadcast channel and compute achievable ergodic rates when channel state information (CSI) is acquired at the receivers via downlink training and it is provided to the transmitter by channel state feedback. Unquantized (analog) and quantized (digital) channel state feedback schemes are analyzed and compared under various assumptions. Digital feedback is shown to be potentially superior when the feedback channel uses per channel state coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a minor effect even if simple uncoded modulation is used on the feedback channel. We discuss first the case of an unfaded additive white Gaussian noise (AWGN) feedback channel with orthogonal access and then the case of fading MIMO multiple access (MIMO-MAC). We show that by exploiting the MIMO-MAC nature of the uplink channel, a much better scaling of the feedback channel resource with the number of base station (BS) antennas can be achieved. Finally, for the case of delayed feedback, we show that in the realistic case where the fading process has (normalized) maximum Doppler frequency shift 0 ? F < 1/2, a fraction 1 - 2F of the optimal multiplexing gain is achievable. The general conclusion of this work is that very significant downlink throughput is achievable with simple and efficient channel state feedback, provided that the feedback link is properly designed.

Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey

Abstract: Several enabling technologies are being explored for the fifth-generation (5G) mobile system era. The aim is to evolve a cellular network that remarkably pushes forward the limits of legacy mobile systems across all dimensions of performance metrics. One dominant technology that consistently features in the list of the 5G enablers is the millimeter-wave (mmWave) massive multiple-input-multiple-output (massive MIMO) system. It shows potentials to significantly raise user throughput, enhance spectral and energy efficiencies and increase the capacity of mobile networks using the joint capabilities of the huge available bandwidth in the mmWave frequency bands and high multiplexing gains achievable with massive antenna arrays. In this survey, we present the preliminary outcomes of extensive research on mmWave massive MIMO (as research on this subject is still in the exploratory phase) and highlight emerging trends together with their respective benefits, challenges, and proposed solutions. The survey spans broad areas in the field of wireless communications, and the objective is to point out current trends, evolving research issues and future directions on mmWave massive MIMO as a technology that will open up new frontiers of services and applications for next-generation cellular networks.