Media-Based MIMO: A New Frontier in Wireless Communications

The idea of Media-based Modulation (MBM), is based on embedding information in the variations of the transmission media (channel state). This is in contrast to legacy wireless systems where data is embedded in a Radio Frequency (RF) source prior to the transmit antenna. MBM offers several advantages vs. legacy systems, including "additivity of information over multiple receive antennas", and "inherent diversity over a static fading channel". MBM is particularly suitable for transmitting high data rates using a single transmit and multiple receive antennas (Single Input-Multiple Output Media-Based Modulation, or SIMO-MBM). However, complexity issues limit the amount of data that can be embedded in the channel state using a single transmit unit. To address this shortcoming, the current article introduces the idea of Layered Multiple Input-Multiple Output Media-Based Modulation (LMIMO-MBM). Relying on a layered structure, LMIMO-MBM can significantly reduce both hardware and algorithmic complexities, as well as the training overhead, vs. SIMO-MBM. Simulation results show excellent performance in terms of Symbol Error Rate (SER) vs. Signal-to-Noise Ratio (SNR). For example, a $4\times 16$ LMIMO-MBM is capable of transmitting $32$ bits of information per (complex) channel-use, with SER $\simeq 10^{-5}$ at $E_b/N_0\simeq -3.5$dB (or SER $\simeq 10^{-4}$ at $E_b/N_0=-4.5$dB). This performance is achieved using a single transmission and without adding any redundancy for Forward-Error-Correction (FEC). This means, in addition to its excellent SER vs. energy/rate performance, MBM relaxes the need for complex FEC structures, and thereby minimizes the transmission delay. Overall, LMIMO-MBM provides a promising alternative to MIMO and Massive MIMO for the realization of 5G wireless networks.Comment: 26 pages, 11 figures, additional examples are given to further explain the idea of Media-Based Modulation. Capacity figure adde

Cognitive Sub-Nyquist Hardware Prototype of a Collocated MIMO Radar

We present the design and hardware implementation of a radar prototype that demonstrates the principle of a sub-Nyquist collocated multiple-input multiple-output (MIMO) radar. The setup allows sampling in both spatial and spectral domains at rates much lower than dictated by the Nyquist sampling theorem. Our prototype realizes an X-band MIMO radar that can be configured to have a maximum of 8 transmit and 10 receive antenna elements. We use frequency division multiplexing (FDM) to achieve the orthogonality of MIMO waveforms and apply the Xampling framework for signal recovery. The prototype also implements a cognitive transmission scheme where each transmit waveform is restricted to those pre-determined subbands of the full signal bandwidth that the receiver samples and processes. Real-time experiments show reasonable recovery performance while operating as a 4x5 thinned random array wherein the combined spatial and spectral sampling factor reduction is 87.5% of that of a filled 8x10 array.Comment: 5 pages, Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa) 201

Symbol-Level Noise-Guessing Decoding with Antenna Sorting for URLLC Massive MIMO

Supporting ultra-reliable and low-latency communication (URLLC) is a challenge in current wireless systems. Channel codes that generate large codewords improve reliability but necessitate the use of interleavers, which introduce undesirable latency. Only short codewords can eliminate the requirement for interleaving and reduce decoding latency. This paper suggests a coding and decoding method which, when combined with the high spectral efficiency of spatial multiplexing, can provide URLLC over a fading channel. Random linear coding and high-order modulation are used to transmit information over a massive multiple-input multiple-output (mMIMO) channel, followed by zero-forcing detection and guessing random additive noise decoding (GRAND) at a receiver. A variant of GRAND, called symbol-level GRAND, originally proposed for single-antenna systems that employ high-order modulation schemes, is generalized to spatial multiplexing. The paper studies the impact of the orthogonality defect of the underlying mMIMO lattice on symbol-level GRAND, and proposes to leverage side-information that comes from the mMIMO channel-state information and relates to the reliability of each receive antenna. This induces an antenna sorting step, which further reduces decoding complexity by over 80\% when compared to bit-level GRAND

Ordered reduced set successive detector for low complexity, quasi-ML MIMO detection

An Ordered Reduced Set Successive Detector (RSSD) for the V-BLAST spatial multiplexing scheme that uses a general two-dimensional non-uniform set partitioning for different symbols. The detector provides improved diversity and SNR gains at reduced complexity compared to a uniform set partitioning based detector. The detector can be used to reduce the complexity, with a small tradeoff in performance. Further, it is possible to obtain a quasi-ML performance using the disclosed detector at a reduced, yet fixed, complexity