The unified 3D communication networks, integrating standard terrestrial mobile communication networks and non-terrestrial networks (NTNs), are seen as the key enabler for global connectivity in the next generation (6G) wireless communications. To achieve this goal, new technologies and components are needed in order to meet the requirements for the 6G networks in terms of higher data rates, and enhanced reliability, security and network reconfigurability. This work introduces the German project 6G-TakeOff, aimed at the design of solutions for unified 3D networks for 6G wireless communication systems. The project consortium brings together academic and industrial partners from Germany and Europe, covering the entire value chain from design of electro-nics to applications. This work presents the key hardware components required for 3D networks and the concept for demonstration of their functionality.

This paper proposes a conceptually novel, adaptive and future-proof 5G mobile network architecture. The proposed architecture enables unprecedented levels of network customisability, ensuring stringent performance, security, cost and energy requirements to be met; as well as providing an API-driven architectural openness, fuelling economic growth through over-the-top innovation. Not following the 'one system fits all services' paradigm of current architectures, the architecture allows for adapting the mechanisms executed for a given service to the specific service requirements, resulting in a novel service- and context-dependent adaptation of network functions paradigm. The technical approach is based on the innovative concept of adaptive (de)composition and allocation of mobile network functions, which flexibly decomposes the mobile network functions and places the resulting functions in the most appropriate location. By doing so, access and core functions no longer (necessarily) reside in different locations, which is exploited to jointly optimize their operation when possible. The adaptability of the architecture is further strengthened by the innovative software-defined mobile network control and mobile multi-tenancy concepts.

Iterative decoding methods have gained interest, initiated by the results of the so-called "turbo" codes. The theoretical description of this decoding, however, seems to be difficult. Therefore, we study the iterative decoding of block codes. First, we discuss the iterative decoding algorithms developed by Gallager (1962), Battail et al. (1979), and Hagenauer et al. (1996). Based on their results, we propose a decoding algorithm which only uses parity check vectors of minimum weight. We give the relation of this iterative decoding to one-step majority-logic decoding, and interpret it as gradient optimization. It is shown that the used parity check set defines the region where the iterative decoding decides on a particular codeword. We make plausible that, in almost all cases, the iterative decoding converges to a codeword after some iterations. We derive a computationally efficient implementation using the minimal trellis representing the used parity check set. Simulations illustrate that our algorithm gives results close to soft decision maximum likelihood (SDML) decoding for many code classes like BCH codes. Reed-Muller codes, quadratic residue codes, double circulant codes, and cyclic finite geometry codes. We also present simulation results for product codes and parallel concatenated codes based on block codes.

A novel construction method is presented for array codes correcting a single rectangular error cluster of size b/sub 1//spl times/b/sub 2/ or less. Encoding is done in such a way that parity check symbols are calculated row-or columnwise which are a word of a one-dimensional code correcting a phased burst. These parity check symbols are not transmitted but can be calculated by the receiver. Corresponding decoding algorithms are given. A code is constructed with maximum size n/sub 1/=b/sub 1/2/sup b2/, n/sub 2/=b/sub 2/2/sup b1/, and r=3b/sub 1/b/sub 2/ redundancy symbols which is close to the generalized Singleton bound r/spl ges/2b/sub 1/b/sub 2/. Its encoding and decoding complexity are low.