No abstract

Radio environment maps depict the coverage area of cellular networks. They are usually estimated by interpolating sparse measurements gathered in test drives. Typical estimation techniques rely on physical or statistical propagation models, known base station locations, topographic data, and/or building data. In this paper, we present DeepREM: a set of two deep-learning models (U-Net and CGAN) that estimate REMs from sparse measurements without requiring any additional information. A physical raytracing simulator with geographic and building data is required during the model training, but not for its operation afterwards. DeepREM models are capable of estimating two radio parameters: i) reference signal received power (RSRP) and ii) BS coverage (cell indices). Extensive testing shows that DeepREM models outperform state-of-the-art methods in terms of root mean squared error (RMSE) and mean absolute error (MAE), and that CGAN has better generalization capabilities in the analyzed scenarios (in particular when the input distribution does not fit the training dataset). Achieved RMSE and MAE are 6.32 and 4.54 dBm for RSRP estimation, while error rate was around 11% for BS coverage estimation. Moreover, our training dataset and models are publicly available and can be used to speed up and improve the accuracy of current REM estimation techniques.

Millimeter wave (mm-wave) and massive MIMO have been proposed for next generation wireless systems. However, there are many open problems for the implementation of those technologies. In particular, beamforming is necessary in mm-wave systems in order to counter high propagation losses.However, conventional beamsteering is not always appropriate in rich scattering multipath channels with frequency selective fading, such as those found in indoor environments. In this context, time-reversal (TR) is considered a promising beamforming technique for such mm-wave massive MIMO systems. In this paper, we analyze a baseband TR beamforming system for mm-wave multi-user massive MIMO.We verify that, as the number of antennas increases, TR yields good equalization and interference mitigation properties, but inter-user interference (IUI) remains a main impairment. Thus, we propose a novel technique called interference-nulling TR (INTR) to minimize IUI. We evaluate numerically the performance of INTR and compare it with conventional TR and equalized TR beamforming. We use a 60 GHz MIMO channel model with spatial correlation based on the IEEE 802.11ad SISO NLoS model. We demonstrate that INTR outperforms conventional TR with respect to average BER per user and achievable sum rate under diverse conditions, providing both diversity and multiplexing gains simultaneously. Index TermsThe authors are with the 2 Time-reversal, beamforming, mm-wave, massive MIMO, interference mitigation.

The most relevant linear precoding method for frequency-flat MIMO broadcast channels is block diagonalization (BD) which, under certain conditions, attains the same nonlinear dirty paper coding channel capacity. However, BD is not easily translated to frequency-selective channels, since space-time information is required for transceiver design. In this paper, we demonstrate that BD is feasible in frequency-selective MIMO broadcast channels to eliminate inter-user interference (IUI) if the transmit block length is sufficiently large, and if the number of transmit antennas is greater than the number of users. We also propose three different approaches to mitigate/eliminate inter-symbol interference (ISI) in block transmissions: i) time-reversal-based BD (TRBD) which maximizes spatial focusing around the receivers using transmitter processing only, ii) equalized BD (EBD) which minimizes the ISI using transmitter processing only, and iii) joint processing BD (JPBD), which uses linear processing at the transmitter and the receiver to suppress ISI. We analyze the theoretical diversity and multiplexing gains of these techniques, and we demonstrate that JPBD approximates full multiplexing gain for a sufficiently large transmit block length. Extensive numerical simulations show that the achievable rate and probability of error performance of all the proposed methods improve those of conventional time-reversal beamforming. Moreover, JPBD provides the highest achievable rate region for frequencyselective MIMO broadcast channels. Index TermsThis work was supported by a Fulbright Colombia fellowship. Parts of this work were submitted to the IEEE Global Communications Conference 2016 [1]. The authors are with the