Numerous candidate systems have emerged to address the connectivity requirements of Low Power Wireless Access (LPWA) Internet of Things (IoT) applications for massive Machine Type Communication (MTC). This paper analyzes the radio interface design of LoRaWAN systems operating in the North American 915 MHz licence-exempt frequency band. After providing a detailed overview of LoRaWAN system and connectivity structuring, the performance of LoRaWAN radio interface under the default North American mode of operation is thoroughly analyzed. Specifically, coverage, capacity and End Device (ED) throughput rates are determined for defined LoRaWAN Data Rate (DR) classes. Analysis of the LoRaWAN radio interface reveals a requirement of non-uniform ED distribution to attain maximal coverage and capacity, with the DR class achieving maximal coverage providing 1% of the peak capacity and the DRs providing 76% of the maximal capacity confined within 26% of the peak coverage area.

We consider the problem of optimizing the allocation of available energy across training and data symbols under linear precoding in multiuser downlink systems with imperfect channel state information (CSI). Our figure of merit is the sum rate across all users. This paper derives a lower bound on achievable rate under linear precoding and extends existing precoder designs to the case of imperfect CSI. Optimality and separability results for the energy allocation and precoder design problems that were found previously for sum-MSE minimization are extended to the problem of sum-rate maximization when channels are modelled using uncorrelated Rayleigh block fading with equal variances of the fading coefficients. Simulation results suggest significant improvements in achievable rate under the proposed algorithm.

In the multiuser downlink, power allocation for linear precoders that minimize the sum of mean squared errors under a sum power constraint is a non-convex problem. Many existing algorithms solve an equivalent convex problem in the virtual uplink and apply a transformation based on uplink-downlink duality to find a downlink solution. In this letter, we analyze the optimality criteria for the power allocation subproblem in the virtual uplink, and demonstrate that the optimal solution leads to identical power allocations in the downlink and virtual uplink. We thus extend the known duality results and, importantly, simplify the existing algorithms used for iterative transceiver design.

We consider linear precoding and decoding in the downlink of a multiuser multiple-input, multiple-output (MIMO) system, wherein each user may receive more than one data stream. We propose several mean squared error (MSE) based criteria for joint transmit-receive optimization and establish a series of relationships linking these criteria to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. In particular, we show that achieving the maximum sum throughput is equivalent to minimizing the product of MSE matrix determinants (PDetMSE). Since the PDetMSE minimization problem does not admit a computationally efficient solution, a simplified scalar version of the problem is considered that minimizes the product of mean squared errors (PMSE). An iterative algorithm is proposed to solve the PMSE problem, and is shown to provide near-optimal performance with greatly reduced computational complexity. Our simulations compare the achievable sum rates under linear precoding strategies to the sum capacity for the broadcast channel.

This paper develops linear preceding schemes for the downlink in multiuser multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with multiple data streams per user. We extend an existing multiuser MIMO algorithm, that jointly optimizes the power allocation and the transmit and receive filters, to MIMO- OFDM systems. One extension is to solve the resulting problem of joint power allocation across OFDM subcarriers. This paper also presents efficient methods to reduce the computational load of the algorithm by interpolating the precoding and decoding matrices corresponding to different OFDM subcarriers. The simulations show that the proposed interpolation scheme outperforms previously known schemes, but requires that the precoder for each subcarrier be tailored to the interpolated receiver.

In this paper we solve the problem of linear precoding for the downlink in multiuser multiple-input multiple-output (MIMO) systems. The transmitter and the receivers may be equipped with multiple antennas and each user may receive multiple data streams. Our objective is to jointly optimize the power allocation and transmit-receive filters for all users. We develop the optimization for two different criteria: (1) minimizing the total transmitted power while satisfying SINR constraints and (2) minimizing the sum mean squared error given a total power budget. We take advantage of the duality between the uplink and downlink to derive the solution.

In this paper we propose a novel method for joint transmit-receive linear optimization in the downlink of a multiuser MIMO communication system. This new method adapts existing joint linear optimization algorithms from the single user domain for application to the multiuser domain. The optimum transmit matrix is obtained using an iterative procedure based on a minimum mean-squared error (MMSE) criterion and a per-user power constraint; the optimum receive matrices for each user are then derived under an MMSE constraint. The proposed technique improves performance and increases data throughput in multiuser scenarios.