In this paper we study the relay-interference wireless network, in which relay (helper) nodes are to facilitate competing information flows over a wireless network. We examine this in the context of a deterministic wireless interaction model, which eliminates the channel noise and focuses on the signal interactions. Using this model, we show that almost all the known schemes such as interference suppression, interference alignment and interference separation are necessary for relay-interference networks. In addition, we discover a new interference management technique, which we call interference neutralization, which allows for over-the-air interference removal, without the transmitters having complete access the interfering signals. We show that interference separation, suppression, and neutralization arise in a fundamental manner, since we show complete characterizations for special configurations of the relay-interference network.
Deep Dive into Transmission Techniques for Relay-Interference Networks.
In this paper we study the relay-interference wireless network, in which relay (helper) nodes are to facilitate competing information flows over a wireless network. We examine this in the context of a deterministic wireless interaction model, which eliminates the channel noise and focuses on the signal interactions. Using this model, we show that almost all the known schemes such as interference suppression, interference alignment and interference separation are necessary for relay-interference networks. In addition, we discover a new interference management technique, which we call interference neutralization, which allows for over-the-air interference removal, without the transmitters having complete access the interfering signals. We show that interference separation, suppression, and neutralization arise in a fundamental manner, since we show complete characterizations for special configurations of the relay-interference network.
Information transmission in a shared medium is one of the fundamental problems in wireless communication. In such situation a wireless channel is shared between several sources and receivers, and several information flow are competing for resources. Here, a fundamental question is how to manage interference in a wireless network.
In the multiple access channel problem, introduced by Ahlswede and Liao in early 70’s, a single receiver is interested in decoding the messages sent by different transmitters. Several techniques, including multi-user detection, orthogonal source allocation, and taking interference as a part of noise have been devised for this problem.
In a more general setup, not all the source are of interest for all the receivers. The interference channel problem [1] is the very basic example of such situation which has been open for 30 years. The best known achievable region for this problem is due to Han and Kobayashi [1]. Over the past few decades several techniques have been devised for transmission on the interference channels; among them, superposition of information, power allocation, and interference suppression (partly common information) are the most well-known ones. Recently, the capacity region of the interference channel has been characterized for some regimes by building on an approximate characterization (within 1 bit) given for the whole regime in [2]. However, it is not clear whether the known techniques are enough to achieve the capacity when we also have relays in the network facilitating the flow of more than one unicast session.
The deterministic approach, studied by Avestimehr, Diggavi, and Tse [3], [4], simplifies the wireless network interaction model by eliminating the noise. This approach was successfully applied to the relay network in [4], and resulted in insight in terms of transmission techniques. These insights also led to an approximate characterization of the noisy wireless relay network problem [5]. This model is also applied to the interference channel problem in [6], where it is shown that the capacity region of the deterministic interference channel is within constant bit gap of the Gaussian interference channel, and an alternative approximate characterization for the capacity region is provided.
In this paper, we apply the deterministic model to a twostage interference channel, where the goal is to accommodate multiple unicast flows over the network. The simple layered structure of the networks helps us to focus more on the transmission techniques, rather than synchronization issues, raised in a non-layered network. We have complete characterization for two special cases, called the ZS and the ZZ networks. Investigation of these networks, suggest a new insight about the transmission techniques, which can be applied in any network. It is shown that the interference separation and interference suppression are useful to avoid or remove interference in different regimes. We will also show that using interference alignment is essential for some cases, even with two messages transmitted through the network. The other contribution of this paper is to introduce a new transmission technique, interference neutralization, to remove (decrease) the interference in a network.
The paper is organized as follows. Section II states the precise definition of the problem, and introduces the notations. Before stating the main results, we review the known techniques and explain the new techniques that we will use later in Section III. We will present our main results, the exact characterization of the ZS and ZZ networks in Sections IV and V. Finally, we will conclude and discuss about future extensions in Section VI.
Wireless interaction model: In this standard model [7], transmitted signals get attenuated by (complex) gains to which independent (Gaussian) receiver noise is added. More formally, the received signal y i at node i ∈ V at time t is given by,
where h ij is the complex channel gain between node j and i, x j is the signal transmitted by node j, and N i are the set of nodes that have non-zero channel gains to i. We assume that the average transmit power constraints for all nodes is 1 and the additive receiver Gaussian noise is of unit variance. We use the terminology Gaussian wireless network when the signal interaction model is governed by (1). Deterministic interaction model: In [4], a simpler deterministic model which captures the essence of wireless interaction was developed. The advantage of this model is its simplicity, which gives insight to strategies for the noisy wireless network model in (1). We will utilize this model to develop techniques for the relay-interference network. Our main results are developed for this deterministic model. The deterministic model of [4] simplifies the wireless interaction model in (1) by eliminating the noise and discretizing the channel gains through a binary expansion of q bits. Therefore, the received signal Y i which is
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