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Communications Engineering - Project Description

Compressed sensing for generalized coded spatial modulation in the massive MIMO downlink

Several key technologies are currently discussed for the evolution of next generation mobile cellular radio systems (5G). While multiple antenna systems have already been established in current standards, different extensions have the potential to be integral part of future systems. Deploying a huge number of antennas at the base station (massive MIMO) is one proposal to overcome interference limitations in cellular networks. Moreover, spatial modulation is a technique that transmits information by selecting only one out of N antennas. Protagonists of this approach claim that it saves hardware costs and reduces power consumption by replacing multiple power amplifiers and filters by a single hardware chain and a fast switch connecting the chosen antenna to the hardware chain.

In the field of digital signal processing, compressed sensing has attracted a lot of attention in the last decade and found its way from pure mathematical research to various application areas. Regarding communication systems, compressed sensing has been applied e.g. for spectrum sensing in cognitive radio systems or estimating doubly dispersive communication channels.

This project will combine the ideas of massive MIMO, generalized spatial modulation and compressed sensing to a new promising framework. It shall illuminate the potential of spatial modulation in massive MIMO systems and benchmark it with alternative approaches being currently discussed. Achievable data rate, outage probability and computational complexity are major performance measures to be investigated.

In particular, generalized spatial modulation will be considered for the downlink of a wireless communication system employing a huge number of antennas at the base station as proposed for massive MIMO systems. Information is conveyed by selecting a few transmit antennas from the large set of available antennas and additionally via the modulated signals itself. Since only a few antennas are selected, the vector to be detected at the receiver is sparse so that the application of compressed sensing techniques is obvious. The project focuses on the adaptation and optimization of compressed sensing algorithms to the investigated system. Basis pursuit and greedy algorithms will be extended especially to exploit knowledge about the system model and to deal with soft information for iterative detection strategies. The system design shall be optimized with respect to important parameters like required number of antennas at transmitter and receiver as well as the number of activated antennas. Moreover, appropriate coding strategies shall be derived and integrated into the system concept ensuring a robust transmission and taking into account the specific characteristics of the system.


Franz, D.; Kühn, V.:
Approximate Message Passing for Sparse Large MIMO Systems with Prior Information. 21st International ITG Workshop on Smart Antennas, Berlin, Germany, 2017
Franz, D.; Kühn, V.:
Coded generalized spatial modulation for structured large scale MIMO systems. 13th International Symposium on Wireless Communication Systems (ISWCS), pp. 32-36, Poznan, Poland, 2016