Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader

Backscatter communication (BSC) is being realized as the core technology for pervasive sustainable Internet-of-Things applications. However, owing to the resource-limitations of passive tags, the efficient usage of multiple antennas at the reader is essential for both downlink excitation and uplink detection. This work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the reader and backscattering coefficients (BC) at the tags. Since, this joint problem is nonconvex, we first present individually-optimal designs for the TRX and BC. We show that with precoder and {combiner} designs at the reader respectively targeting downlink energy beamforming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low and high signal-to-noise-ratio regimes. Based on these developments, an iterative low-complexity algorithm is proposed to yield an efficient jointly-suboptimal design. Thereafter, we discuss the practical utility of the proposed designs to other application settings like wireless powered communication networks and BSC with imperfect channel state information. Lastly, selected numerical results, validating the analysis and shedding novel insights, demonstrate that the proposed designs can yield significant enhancement in the sum-backscattered throughput over existing benchmarks.Comment: 17 pages, 5 figures, accepted for publication in IEEE Transactions on Communication

A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)

Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multi-core neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure

Reliable Transmission of Short Packets through Queues and Noisy Channels under Latency and Peak-Age Violation Guarantees

This work investigates the probability that the delay and the peak-age of information exceed a desired threshold in a point-to-point communication system with short information packets. The packets are generated according to a stationary memoryless Bernoulli process, placed in a single-server queue and then transmitted over a wireless channel. A variable-length stop-feedback coding scheme---a general strategy that encompasses simple automatic repetition request (ARQ) and more sophisticated hybrid ARQ techniques as special cases---is used by the transmitter to convey the information packets to the receiver. By leveraging finite-blocklength results, the delay violation and the peak-age violation probabilities are characterized without resorting to approximations based on large-deviation theory as in previous literature. Numerical results illuminate the dependence of delay and peak-age violation probability on system parameters such as the frame size and the undetected error probability, and on the chosen packet-management policy. The guidelines provided by our analysis are particularly useful for the design of low-latency ultra-reliable communication systems.Comment: To appear in IEEE journal on selected areas of communication (IEEE JSAC

Multiuser Diversity Management for Multicast/Broadcast Services in 5G and Beyond Networks

The envisaged fifth-generation (5G) and beyond networks represent a paradigm shift for global communications, offering unprecedented breakthroughs in media service delivery with novel capabilities and use cases. Addressing the critical research verticals and challenges that characterize the International Mobile Telecommunications (IMT)-2030 framework requires a compelling mix of enabling radio access technologies (RAT) and native softwarized, disaggregated, and intelligent radio access network (RAN) conceptions. In such a context, the multicast/broadcast ser vice (MBS) capability is an appealing feature to address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. Embracing the MBS capability as a primary component of the envisaged 5G and beyond networks comes with multiple open challenges. In this research, we contextualize and address the necessity of ensuring stringent quality of service (QoS)/quality of experience (QoE) requirements, multicasting over millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies, and handling complex mobility behaviors. In the broad problem space around these three significant challenges, we focus on the specific research problems of effectively handling the trade-off between multicasting gain and multiuser diversity, along with the trade-off between optimal network performance and computational complexity. In this research, we cover essential aspects at the intersection of MBS, radio resource management (RRM), machine learning (ML), and the Open RAN (O-RAN) framework. We characterize and address the dynamic multicast multiuser diversity through low-complexity RRM solutions aided by ML, orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques in 5G MBS and beyond networks. We characterize the performance of the multicast access techniques conventional multicast scheme (CMS), subgrouping based on OMA (S-OMA), and subgrouping based on NOMA (S-NOMA). We provide conditions for their adequate selection regarding the specific network conditions (Chapter 4). Consequently, we propose heuristic methods for the dynamic multicast access technique selection and resource allocation, taking advantage of the multiuser diversity (Chapter 5.1). Moreover, we proposed a multicasting strategy based on fixed pre-computed multiple-input multiple-output (MIMO) multi-beams and S-NOMA (Chapter 5.2). Our approach tackles specific throughput requirements for enabling extended reality (XR) applications attending multiple users and handling their spatial and channel quality diversity. We address the computational complexity (CC) associated with the dynamic multicast RRM strategies and highlight the implications of fast variations in the reception conditions of the multicast group (MG) members. We propose a low complexity ML-based solution structured by a multicast-oriented trigger to avoid overrunning the algorithm, a K-Means clustering for group-oriented detection and splitting, and a classifier for selecting the most suitable multicast access technique (Chapter 6.1). Our proposed approaches allow addressing the trade-off between optimal network performance and CC by maximizing specific QoS parameters through non-optimal solutions, considerably reducing the CC of conventional exhaustive mechanisms. Moreover, we discuss the insertion of ML-based multicasting RRM solutions into the envisioned disaggregated O-RAN framework (Chapter 6.2.5). We analyze specific MBS tasks and the importance of a native decentralized, softwarized, and intelligent conception. We assess the effectiveness of our proposal under multiple numerical and link level simulations of recreated 5G MBS use cases operating in μWave and mmWave. We evaluate various network conditions, service constraints, and users’ mobility behaviors