Statistical Mechanics Analysis of LDPC Coding in MIMO Gaussian Channels

Using analytical methods of statistical mechanics, we analyse the typical behaviour of a multiple-input multiple-output (MIMO) Gaussian channel with binary inputs under LDPC network coding and joint decoding. The saddle point equations for the replica symmetric solution are found in particular realizations of this channel, including a small and large number of transmitters and receivers. In particular, we examine the cases of a single transmitter, a single receiver and the symmetric and asymmetric interference channels. Both dynamical and thermodynamical transitions from the ferromagnetic solution of perfect decoding to a non-ferromagnetic solution are identified for the cases considered, marking the practical and theoretical limits of the system under the current coding scheme. Numerical results are provided, showing the typical level of improvement/deterioration achieved with respect to the single transmitter/receiver result, for the various cases.Comment: 25 pages, 7 figure

Computing sum of sources over an arbitrary multiple access channel

The problem of computing sum of sources over a multiple access channel (MAC) is considered. Building on the technique of linear computation coding (LCC) proposed by Nazer and Gastpar [2007], we employ the ensemble of nested coset codes to derive a new set of sufficient conditions for computing the sum of sources over an \textit{arbitrary} MAC. The optimality of nested coset codes [Padakandla, Pradhan 2011] enables this technique outperform LCC even for linear MAC with a structural match. Examples of nonadditive MAC for which the technique proposed herein outperforms separation and systematic based computation are also presented. Finally, this technique is enhanced by incorporating separation based strategy, leading to a new set of sufficient conditions for computing the sum over a MAC.Comment: Contains proof of the main theorem and a few minor corrections. Contents of this article have been accepted for presentation at ISIT201

Multi-Source Cooperative Communication with Opportunistic Interference Cancelling Relays

In this paper we present a multi-user cooperative protocol for wireless networks. Two sources transmit simultaneously their information blocks and relays employ opportunistically successive interference cancellation (SIC) in an effort to decode them. An adaptive decode/amplify-and-forward scheme is applied at the relays to the decoded blocks or their sufficient statistic if decoding fails. The main feature of the protocol is that SIC is exploited in a network since more opportunities arise for each block to be decoded as the number of used relays NRU is increased. This feature leads to benefits in terms of diversity and multiplexing gains that are proven with the help of an analytical outage model and a diversity-multiplexing tradeoff (DMT) analysis. The performance improvements are achieved without any network synchronization and coordination. In the final part of this work the closed-form outage probability model is used by a novel approach for offline pre-selection of the NRU relays, that have the best SIC performance, from a larger number of NR nodes. The analytical results are corroborated with extensive simulations, while the protocol is compared with orthogonal and multi-user protocols reported in the literature.Comment: in IEEE Transactions on Communications, 201

Capacity-Achieving Iterative LMMSE Detection for MIMO-NOMA Systems

This paper considers a iterative Linear Minimum Mean Square Error (LMMSE) detection for the uplink Multiuser Multiple-Input and Multiple-Output (MU-MIMO) systems with Non-Orthogonal Multiple Access (NOMA). The iterative LMMSE detection greatly reduces the system computational complexity by departing the overall processing into many low-complexity distributed calculations. However, it is generally considered to be sub-optimal and achieves relatively poor performance. In this paper, we firstly present the matching conditions and area theorems for the iterative detection of the MIMO-NOMA systems. Based on the proposed matching conditions and area theorems, the achievable rate region of the iterative LMMSE detection is analysed. We prove that by properly design the iterative LMMSE detection, it can achieve (i) the optimal sum capacity of MU-MIMO systems, (ii) all the maximal extreme points in the capacity region of MU-MIMO system, and (iii) the whole capacity region of two-user MIMO systems.Comment: 6pages, 5 figures, accepted by IEEE ICC 2016, 23-27 May 2016, Kuala Lumpur, Malaysi

Computation in Multicast Networks: Function Alignment and Converse Theorems

The classical problem in network coding theory considers communication over multicast networks. Multiple transmitters send independent messages to multiple receivers which decode the same set of messages. In this work, computation over multicast networks is considered: each receiver decodes an identical function of the original messages. For a countably infinite class of two-transmitter two-receiver single-hop linear deterministic networks, the computing capacity is characterized for a linear function (modulo-2 sum) of Bernoulli sources. Inspired by the geometric concept of interference alignment in networks, a new achievable coding scheme called function alignment is introduced. A new converse theorem is established that is tighter than cut-set based and genie-aided bounds. Computation (vs. communication) over multicast networks requires additional analysis to account for multiple receivers sharing a network's computational resources. We also develop a network decomposition theorem which identifies elementary parallel subnetworks that can constitute an original network without loss of optimality. The decomposition theorem provides a conceptually-simpler algebraic proof of achievability that generalizes to $L$-transmitter $L$-receiver networks.Comment: to appear in the IEEE Transactions on Information Theor

Compute-and-Forward: Harnessing Interference through Structured Codes

Interference is usually viewed as an obstacle to communication in wireless networks. This paper proposes a new strategy, compute-and-forward, that exploits interference to obtain significantly higher rates between users in a network. The key idea is that relays should decode linear functions of transmitted messages according to their observed channel coefficients rather than ignoring the interference as noise. After decoding these linear equations, the relays simply send them towards the destinations, which given enough equations, can recover their desired messages. The underlying codes are based on nested lattices whose algebraic structure ensures that integer combinations of codewords can be decoded reliably. Encoders map messages from a finite field to a lattice and decoders recover equations of lattice points which are then mapped back to equations over the finite field. This scheme is applicable even if the transmitters lack channel state information.Comment: IEEE Trans. Info Theory, to appear. 23 pages, 13 figure