The compute-and-forward framework permits each receiver in a Gaussian network to directly decode a linear combination of the transmitted messages. The resulting linear combinations can then be employed as an end-to-end communication strategy for relaying, interference alignment, and other applications. Recent efforts have demonstrated the advantages of employing unequal powers at the transmitters and decoding more than one linear combination at each receiver. However, neither of these techniques fit naturally within the original formulation of compute-and-forward. This paper proposes an expanded compute-and-forward framework that incorporates both of these possibilities and permits an intuitive interpretation in terms of signal levels. Within this framework, recent achievability and optimality results are unified and generalized.

In this work, we consider an interference channel model in which K receivers cooperatively attempt to decode their intended messages locally by processing and sharing information through limited capacity backhaul links. In contrast to distributed antenna architectures that have been proposed in the literature, where data processing is utterly performed in a centralized fashion, the model considered in this paper aims to capture the essence of decentralized (over the cloud) processing, allowing for a more general class of interference management strategies. Focusing on the three-user case, we characterize the fundamental tradeoff between the achievable communication rates and the corresponding backhaul cooperation rate, in terms of degrees of freedom (DoF). Surprisingly, we show that the optimum communication-cooperation tradeoff remains the same when we move from two-user to three-user interference channels. In the absence of cooperation, this is due to interference alignment, which keeps the fraction of communication dimensions wasted for interference unchanged. When backhaul cooperation is available, we develop a new idea that we call cooperation alignment, which guarantees that the average (per user) backhaul load remains the same as we increase the number of users.

Interference alignment promises that, in Gaussian interference channels, each link can support half of a degree of freedom (DoF) per pair of transmit-receive antennas. However, in general, this result requires to precode the data bearing signals over a signal space of asymptotically large diversity, e.g., over an infinite number of dimensions for time-frequency varying fading channels, or over an infinite number of rationally independent signal levels, in the case of time-frequency invariant channels. In this paper, we consider a wireless cellular system scenario where the promised optimal DoFs are achieved with linear precoding in one-shot (i.e., over a single time-frequency slot). We focus on the uplink of a symmetric cellular system, where each cell is split into three sectors with orthogonal intrasector multiple access. In our model, interference is local, i.e., it is due to transmitters in neighboring cells only. We consider a noniterative local cooperation scheme where base stations pass to their neighbors their decoded messages such that interference from already decoded messages can be canceled. Therefore, for a given decoding order, the interference between sectors is described by a directed locally connected graph. The problem consists of maximizing the per-sector DoFs over all possible decoding orders and precoding schemes. In particular, we provide a decoding order and a one-shot interference alignment scheme able to achieve optimal per-sector DoFs, up to an additive gap due to boundary effects, that vanishes as the size of the network becomes large. Then, we extend our treatment by considering the case of intersector interference with joint processing of the three sector at each cell site. In order to avoid signaling schemes relying on the strength of interference, we further introduce the notion of topologically robust schemes, which are able to guarantee a minimum rate (or DoFs) irrespectively of the strength of the interfering links. Toward this end, we design a different decoding order and alignment scheme, which is topologically robust and still achieves the same optimum DoFs. Finally, we provide a new scheme for the downlink, based on local base station cooperation, where base stations pass to their neighbors a quantized version of their dirty-paper coded signals. For the proposed downlink scheme, we can prove a DoFs duality result showing that, for an appropriate choice of the precoding order and of the alignment beamforming vectors, it can achieve the same per-sector DoFs of the corresponding uplink schemes.

Although interference alignment (IA) can theoretically achieve the optimal degrees of freedom (DoFs) in the K-user Gaussian interference channel, its direct application comes at the prohibitive cost of precoding over exponentially many signaling dimensions. On the other hand, it is known that practical one-shot IA precoding (i.e., linear schemes without symbol expansion) provides a vanishing DoFs gain in large fully connected networks with generic channel coefficients. In our previous work, we introduced the concept of cellular IA for a network topology induced by hexagonal cells with sectors and nearest-neighbor interference. Assuming that neighboring sectors can exchange decoded messages (and not received signal samples) in the uplink, we showed that linear one-shot IA precoding over M transmit/ receive antennas can achieve the optimal M/2 DoFs per user. In this paper, we extend this framework to networks with omnidirectional (non-sectorized) cells and consider a limited practical scenario where users have 2 antennas, and base-stations have 2, 3, or 4 antennas. We provide linear one-shot IA schemes for the 2 × 2, 2 × 3, and 2 × 4 cases, and show the achievability of 3/4, 1, and 7/6 DoFs per user, respectively. DoFs converses for one-shot schemes require the solution of a discrete optimization problem over a number of variables that grows with the network size. We develop a new approach to transform such optimization problem into a tractable linear program with significantly fewer variables. This approach is used to show that 3/4 DoFs per user are indeed optimal for one-shot schemes over large (extended) cellular network with 2 × 2 links.

Interference alignment (IA) promises that, in Gaussian interference channels, each link can support half of a degree of freedom (DoF) per pair of transmit-receive antennas. However, in general, this result requires to precode the data bearing signals over a signal space of asymptotically large diversity, e.g., over an infinite number of dimensions in time-frequency for time-frequency varying fading channels. Here, we propose a communication scenario in wireless cellular systems where the promised optimal DoFs are achieved with linear precoding in one-shot (coding over a single time-frequency slot). We focus on uplink cellular systems, where each cell is split into three sectors and assume that interference is generated locally between transmitters and receivers of neighboring cells. We consider a message-passing network architecture, in which nearby sectors can exchange already decoded messages and propose an alignment solution that can achieve the optimal DoFs. To avoid signaling schemes relying on the strength of interference, we further introduce the notion of topologically robust schemes, which are able to guarantee a minimum rate (or degrees of freedom) no matter if the interference link are strong or weak. Towards this end, we design an alignment scheme which is topologically robust and still achieves the same optimum DoFs.

We study the three-user interference channel where each transmitter has local feedback of the signal from its targeted receiver. We show that in the important case where the channel coefficients are static, exact alignment can be achieved over two time slots using linear schemes. This is in contrast with the interference channel where no feedback is utilized, where it seems that either an infinite number of channel extensions or infinite precision is required for exact alignment. We also demonstrate, via simulations, that our scheme outperforms time-sharing even at finite SNR.

In this paper, we propose a novel framework, integer-forcing interference alignment, that can simultaneously exploit both signal-space and signal-scale alignment. We consider receivers that can decode integer-linear combinations of desired and interfering streams and then solve for their desired symbols. This is possible by using appropriate lattice codes at the transmitters and can be applied to the class of wireless communication systems that use linear beamforming. At the core of our architecture lies the compute-and-forward framework, which we extend here to encompass asymmetric power allocations. We evaluate the performance of our scheme in the context of the three-user interference channel through simulation results.

We study the problem of storing a data object in a set of data nodes that fail independently with given probabilities. Our problem is a natural generalization of a homogenous storage allocation problem where all the nodes had the same reliability and is naturally motivated for peer-to-peer and cloud storage systems with different types of nodes. Assuming optimal erasure coding (MDS), the goal is to find a storage allocation (i.e, how much to store in each node) to maximize the probability of successful recovery. This problem turns out to be a challenging combinatorial optimization problem. In this work we introduce an approximation framework based on large deviation inequalities and convex optimization. We propose two approximation algorithms and study the asymptotic performance of the resulting allocations.