New Upper Bounds on the Capacity of Primitive Diamond Relay Channels

Abstract

Consider a primitive diamond relay channel, where a source X wants to send information to a destination with the help of two relays Y1 and Y2, and the two relays can communicate to the destination via error-free digital links of capacities C1 and C2 respectively, while Y1 and Y2 are conditionally independent given X. In this paper, we develop new upper bounds on the capacity of such primitive diamond relay channels that are tighter than the cut-set bound. Our results include both the Gaussian and the discrete memoryless case and build on the information inequalities recently developed in [6]-[8] that characterize the tension between information measures in a certain Markov chain. C (n,R) = x n (m) ∈ X n : m ∈ [1 : 2 nR ] ;

The Filtered Gaussian Primitive Diamond Channel

“…In this paper we compare our scheme results to the cut-set upper bound at Eq. ( 2), and to a tighter upper bound shown in [6].…”

“…In this work the band limited symmetric primitive diamond relay channel is considered, where a single user is connected to two non-cooperating relay nodes via symmetric bandlimited and filtered Gaussian channels, while the relay nodes are connected to the final-end receiver via ideal fronthaul links of given capacity. We consider and optimize achievable schemes that account for decode and forward (DF), and distributed compress and forward (CF), and compare the achievable rates to the cut-set bound and the upper bound from [8]. In early stages of this work we attempted to combine CF and DF by superposition in the same time and frequency resource similar to the classical scheme used for broadcast channels, this approach yielded no improvement over time-sharing.…”

The Broadcast Approach in Communication Networks

“…In the oblivious non-fading case, the optimal transmission and relay compression, together with joint decompression at the receiver, are known and characterized in [ 255 ]. For the non-oblivious diamond channel, only upper bounds [ 256 ], and achievable rates of the type discussed in [ 257 ] are available. It may also be interesting to consider the setting of recent work [ 258 ] and extend it to the case that no CSIT is available and consider a broadcast approach strategy for each user.…”

Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles

“…As a main advantage, the proposed scheme can adapt to PA mismatch between training and inference, and can be incorporated with channel coding to combat signal deterioration. In our future work, we will consider the system designs for high-order constellations, transmission rate adaptation [43], and grant-free access [44], and to include more cooperative users [45], [46].…”