The adaptive data rate (ADR) algorithm is a key component of the LoRaWAN protocol which controls the performance of a LoRaWAN Network. This modifies the data rate parameter of a device based on the current wireless conditions. In this article, we present substantive enhancements for the End Device and Network Server which reduce the convergence time for LoRaWAN devices to reach their optimal data rate. We extend the LoRaWAN module in ns-3 by adding ADR, enabling the simulation of realistic LoRaWAN networks, and add the implementation of the new enhancements in this module. The simulations show that these modifications can result in a significant reduction of the data rate convergence time for LoRaWAN devices and lead to an increased overall packet delivery rate for the network in a dynamic network environment. Our contributions are a publicly available implementation of ADR in ns-3, an analysis of the original algorithm behavior, and a novel version of the algorithm with enhancements that improve performance in every case while remaining easily integrable into an existing LoRaWAN system.

For wireless multimedia sensor networks a distributed cross-layer framework is proposed, which not only achieves an optimal tradeoff between network lifetime and its utility but also provides end-to-end delay-margin. The delay-margin, defined as the gap between maximum end-to-end delay threshold and the actual end-to-end delay incurred by the network, is exploited by the application layer to achieve any desired level of delay quality-of-service. For optimal performance tradeoff an appropriate objective function for delay-margin is required, which is obtained by employing sensitivity analysis. Sensitivity analysis is performed by incorporating delay-margin in the end-to-end delay constraints while penalizing its price in the objective function. For distributed realization of proposed cross-layer framework, the optimal tradeoff problem is decomposed into network lifetime, utility and delay-margin subproblems coupled through dual variables. The numerical results for performance evaluation show that compromising network utility does not guarantee both lifetime and delay-margin improvement, simultaneously, for the set of operating points. Performance evaluation results also reveal that the fairness among different delay-margins, corresponding to different source-destination node pairs, can be improved by relaxing the end-to-end delay threshold.

LPWAN technologies are defined by their focus on extended coverage while maintaining energy efficiency, at the expense of data throughput. In this research we enable the analysis of LoRa, a key LPWAN technology, in terms of energy efficiency. We perform real-world measurements of a standard LoRa chip and use the results to develop an energy consumption module in ns-3. Our contributions are an analysis of the energy consumption of different states in a LoRa transmission by the SX1272, the LoRa transceiver that is used in most common LoRaWAN devices, beyond what is provided in the datasheet, and an energy consumption module for use in three of the LoRaWAN ns-3 modules described in research, enabling more accurate energy consumption analysis of LoRa-based systems.

Abstract-We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output broadcast channel, where the secondary system consisting of a secondary base-station (BS) and a group of secondary users (SUs) is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm.

This paper presents an efficient approach to computing the capacity of multiple-input multiple-output (MIMO) channels under multiple linear transmit covariance constraints (LTCCs). LTCCs are general enough to include several special types of power constraints as special cases such as the sum power constraint (SPC), per-antenna power constraint (PAPC), or a combination thereof. Despite its importance and generality, most of the existing literature considers either SPC or PAPC independently. Efficient solutions to the computation of the MIMO capacity with a combination of SPC and PAPC have been recently reported, but were only dedicated to multipleinput single-output (MISO) systems. For the general case of LTCCs, we propose a low-complexity semi-closed-form approach to the computation of the MIMO capacity. Specifically, a modified minimax duality is first invoked to transform the considered problem in the broadcast channel into an equivalent minimax problem in the dual multiple access channel. Then alternating optimization and concave-convex procedure are utilized to derive water-filling-based algorithms to find a saddle point of the minimax problem. This is different from the state-of-the-art solutions to the considered problem, which are based on interiorpoint or subgradient methods. Analytical and numerical results are provided to demonstrate the effectiveness of the proposed low-complexity solution under various MIMO scenarios.