Browsing by Subject "Energy harvesting communications"

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Open Access
Coding schemes for energy harvesting and multi-user communications
(2017-12) Dabirnia, Mehdi
Many wireless communication and networking applications can bene t from energy harvesting and wireless energy transfer including wireless sensor networks, radio frequency identi cation systems and wireless body networks. Some of the advantages that energy harvesting provides for such applications include energy self-su ciency, ability to implement them in hard-to-reach places, reducing the required battery size or even removing the battery completely from the wireless units. In such systems the required energy for the system operation is obtained from a renewable energy source such as solar, thermal or kinetic energy or from a man-made source such as radio frequency (RF) signals, arti cial light, etc. While there has been decades of designs and developments of energy harvesting nodes from circuit and device engineering perspectives, only recent studies consider the speci c constraints of these systems from a communications point of view, and signi cant challenges and problems still remain unsolved, particularly, at the physical layer. With the motivation of addressing some of the above challenges, our main focus in this thesis is the design and analysis of capacity approaching coding schemes for several energy harvesting and multiuser scenarios; in particular, by exploiting nonlinear codes concatenated with low-density parity-check (LDPC) codes for these scenarios. First, novel code design approaches are studied for the joint energy and information transfer speci cally, employment of nonlinear trellis codes (NLTCs) in serial concatenation with outer LDPC codes is proposed, and an algorithm is developed to design the NLTCs prior to optimizing the outer LDPC code using the EXIT analysis. The designed codes are shown to improve upon the o -the-shelf point-to-point (P2P) codes and outperform the alternative of utilizing linear codes with time switching and the reference scheme of concatenating LDPC codes with nonlinear memoryless mappers (NLMMs). This coding approach is then examined for the energy harvesting channel (EHC) implementing two decoding approaches at the receiver side wherein the rst one ignores the memory in the battery state, while the second one incorporates this memory into the trellis. Compared with the P2P codes and the reference schemes, the newly designed codes consistently o er better performance. This code design approach is explored for the case of discrete memoryless interference channels (DMICs) implementing the Han-Kobayashi (HK) encoding and decoding strategy as well. A stability condition is derived for the concatenated coding scheme and it is utilized in the process of designing the outer LDPC code employing the EXIT analysis. It is demonstrated that the designed codes achieve rate pairs close to the optimal boundary of the HK subregion and outperform the single user codes with time sharing. Furthermore, code design principles are also investigated for the two-user Gaussian interference channel with fading employing trellis-based codes with short block lengths. Finally, the problem of designing explicit and implementable codes is studied for a two-user interference channel with energy harvesting transmitters, and a design framework is proposed employing similar techniques developed for the DMIC and EHC.
Open Access
Information rates of energy harvesting communications with intersymbol interference
(Institute of Electrical and Electronics Engineers Inc., 2019) Duman, Tolga M.; Stojanovic, M.
We consider energy harvesting communications over an intersymbol interference (ISI) channel corrupted by additive white Gaussian noise. We assume that the energy arrivals at the transmitter, which is equipped with a finite battery, are independent and identically distributed, and provide a method of computing achievable information rates with channel inputs selected from a finite alphabet. We illustrate the methodology via a set of examples quantifying the effects of energy arrival rates as well as battery capacity on the achievable information rates.
Open Access
On federated learning over wireless channels with over-the-air aggregation
(2022-07) Aygün, Ozan
A decentralized machine learning (ML) approach called federated learning (FL) has recently been at the center of attention since it secures edge users’ data and decreases communication costs. In FL, a parameter server (PS), which keeps track of the global model orchestrates local training and global model aggregation across a set of mobile users (MUs). While there exist studies on FL over wireless channels, its performance on practical wireless communication scenarios has not been investigated very well. With this motivation, this thesis considers wireless FL schemes that use realistic channel models, and analyze the impact of different wireless channel effects. In the first part of the thesis, we study hierarchical federated learning (HFL) where intermediate servers (ISs) are utilized to make the server-side closer to the MUs. Clustering approach is used where MUs are assigned to ISs to perform multiple cluster aggregations before the global aggregation. We first analyze the performance of a partially wireless approach where the MUs send their gradients through a channel with path-loss and fading using over-the-air (OTA) aggregation. We assume that there is no inter-cluster interference and the gradients from the ISs to the PS are sent error-free. We show through numerical and experimental analysis that our proposed algorithm offers a faster convergence and lower power consumption compared to the standard FL with OTA aggregation. As an extension, we also examine a fully-wireless HFL setup where both the MUs and ISs send their gradients through OTA aggregation, taking into account the effect of inter-cluster interference. Our numerical and experimental results reveal that utilizing ISs results in a faster convergence and a better performance than the OTA FL without any IS while using less transmit power. It is also shown that the best choice of cluster aggregations depends on the data distribution among the MUs and the clusters. In the second part of the thesis, we study FL with energy harvesting MUs with stochastic energy arrivals. In every global iteration, the MUs with enough energy in their batteries perform local SGD iterations, and transmit their gradients using OTA aggregation. Before sending the gradients to the PS, the gradients are scaled with respect to the idle time and data cardinality of each MU, through a cooldown multiplier, to amplify the importance of the MUs that send less frequent local updates. We provide a convergence analysis of the proposed setup, and validate our results with numerical and neural network simulations under different energy arrival profiles. The results show that the OTA FL with energy harvesting devices performs slightly worse than the OTA FL without any energy restrictions, and that utilizing the excess energy for more local SGD iterations gives a better convergence rate than simply increasing the transmit power.