Method for post-activator-tag transmission orthogonalization

Abstract

A method and apparatus for post-activator-tag transmission (post-transfer) orthogonalization, the apparatus configured to: receive at least one reference signal from a second apparatus related to a session of a third apparatus; determining a combiner, wherein the determined combiner is configured to read a third device; receiving at least one activation signal and a reply signal, where the at least one activation signal is received from the second device, and where the reply signal is received from the third device; and applying the determined combiner to the at least one activation signal and the reply signal.

Description

Technical Field

[0001] The exemplary and non-limiting embodiments relate generally to devices requiring an activation signal and, more particularly, to isolating such a signal from a signal in reply to the activation. Background Art

[0002] In the field of Internet of Things (IoT) devices, it is known to activate IoT devices using an activation signal. Summary of the invention

[0003] The following summary is intended to be illustrative only. It is not intended to limit the scope of the claims.

[0004] According to one aspect, a device includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the device to at least: receive at least one reference signal from a second device associated with a session with a third device; determine a combiner, wherein the determined combiner is configured to read the third device; receive at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device and the reply signal is received from the third device; and apply the determined combiner to the at least one activation signal and the reply signal.

[0005] According to one aspect, a method includes: using a first device to receive at least one reference signal from a second device associated with a session with a third device; determining a combiner, wherein the determined combiner is configured to read the third device; receiving at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device, wherein the reply signal is received from the third device; and applying the determined combiner to the at least one activation signal and the reply signal.

[0006] According to one aspect, a device includes components for: receiving at least one reference signal from a second device associated with a session with a third device; determining a combiner, wherein the determined combiner is configured to read the third device; receiving at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device and wherein the reply signal is received from the third device; and applying the determined combiner to the at least one activation signal and the reply signal.

[0007] According to one aspect, a non-transitory computer-readable medium includes program instructions stored thereon for performing at least the following items: causing reception of at least one reference signal from a second device related to a session with a third device; determining a combiner, wherein the determined combiner is configured to read the third device; causing reception of at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device and wherein the reply signal is received from the third device; and applying the determined combiner to the at least one activation signal and the reply signal.

[0008] According to some aspects, the subject matter of the independent claims is provided. Some further aspects are defined in the dependent claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above aspects and other features are explained in the following description in conjunction with the accompanying drawings, in which:

[0010] Figure 1 is a block diagram of one possible and non-limiting example system in which example embodiments may be practiced;

[0011] Figure 2 is a diagram illustrating features described herein;

[0012] Figure 3 is a diagram illustrating features described herein;

[0013] FIG4 is a diagram illustrating features described herein;

[0014] Figure 5 is a diagram illustrating features described herein;

[0015] Figure 6 is a diagram illustrating features described herein;

[0016] Figure 7 is a diagram illustrating features described herein;

[0017] Figure 8 is a diagram illustrating features described herein; and

[0018] Fig. 9 is a flow chart showing the steps described herein. DETAILED DESCRIPTION

[0019] The following abbreviations that may appear in the specification and / or drawings are defined as follows:

[0020] 3GPP Third Generation Partnership Project

[0021] 5G Fifth Generation

[0022] 5GC 5G Core Network

[0023] ACR Activator Reader Channel

[0024] A-IoT Environmental IoT AMF Access and Mobility Management Function

[0025] AR Autoregressive

[0026] AT Activator to Tag

[0027] BS Base Station

[0028] CN Core Network

[0029] CNN Convolutional Neural Network

[0030] cRAN Cloud Radio Access Network

[0031] CU Central Unit

[0032] D2D Device to Device

[0033] DNN Deep Neural Network

[0034] DRX Discontinuous Reception

[0035] DU Distributed Unit

[0036] eMTC Enhanced Machine Type Communication

[0037] eNB (or eNodeB) Evolved Node B (e.g., LTE base station) EN-DC E-UTRA-NR Dual Connectivity

[0038] en-gNB or En-gNB node, provides NR user plane and control plane protocol termination to the UE and acts as a secondary node in EN-DC

[0039] E-UTRA Evolved Universal Terrestrial Radio Access, also known as LTE radio access technology

[0040] FDD Frequency Division Duplex

[0041] gNB (or gNodeB) is a base station for 5G / NR, which provides NR user plane and control plane protocol termination to UE and is connected to 5GC via NG interface. IAB Integrated Access and Backhaul I / F Interface IoT L1 Layer 1

[0044] LoRa or LORA Long Distance MAC Media Access Control MME Mobility Management Entity MSE Mean Square Error MT Mobile Terminal ng or NG new generation ng-eNB or NG-eNB Next Generation eNB NN Neural Network NR New Radio N / W or NW network OFDM Orthogonal Frequency Division Multiplexing O-RAN Open Radio Access Network PDCP Packet Data Convergence Protocol PHY Physical Layer ProSe Proximity Services RAN Radio Access Network RedCap Reduced Capacity RF RFID Radio Frequency Identification RLC Radio Link Control RRC Radio Resource Control RRH Remote Radio Head RS reference signal RSU Roadside Unit RU Radio Unit

[0045] Rx Receiver

[0046] SA Independence

[0047] SDAP Service Data Adaptation Protocol

[0048] SGW Service Gateway

[0049] SID Research Projects

[0050] SINR Signal to Interference and Noise Ratio

[0051] SIR Signal to Interference Ratio

[0052] SL Side Link

[0053] SMF session management functions

[0054] TDD Time Division Duplex

[0055] TSI Time Selective Information

[0056] TT Tag to Tag

[0057] Tx Transmitter

[0058] UE User Equipment (e.g., wireless, typically mobile device)

[0059] UPF User Plane Function

[0060] UWB Ultra Wideband

[0061] V2I Vehicle to Infrastructure

[0062] V2N Vehicle to Network

[0063] V2P Vehicle to Pedestrian

[0064] V2V Vehicle to Vehicle

[0065] V2X Vehicle to Everything

[0066] VNR Virtualized Network Function

[0067] ZF Force Zero

[0068] Go to Figure 1 , which shows a block diagram of one possible and non-limiting example in which the example may be practiced. The figure shows a user equipment (UE) 110, a radio access network (RAN) node 170 and a network element 190. Figure 1In the example of FIG. 1 , a user equipment (UE) 110 wirelessly communicates with a wireless network 100. A UE is a wireless device that can access the wireless network 100. The UE 110 includes one or more processors 120, one or more memories 125, and one or more transceivers 130, which are interconnected by one or more buses 127. Each of the one or more transceivers 130 includes a receiver Rx 132 and a transmitter Tx 133. The one or more buses 127 can be address, data, or control buses, and can include any interconnection mechanism, such as a series of lines on a motherboard or integrated circuit, optical fiber or other optical communication devices, etc. The FA "circuitry" can include dedicated hardware or hardware associated with software executable thereon. The one or more transceivers 130 are connected to one or more antennas 128. The one or more memories 125 include computer program code 123. The UE 110 includes a module 140, which includes one or both of the parts 140-1 and / or 140-2, which can be implemented in a variety of ways. Module 140 may be implemented in hardware as module 140-1, such as being implemented as part of one or more processors 120. Module 140-1 may also be implemented as an integrated circuit or by other hardware (such as a programmable gate array). In another example, module 140 may be implemented as module 140-2, which is implemented as computer program code 123 and executed by one or more processors 120. For example, one or more memories 125 and computer program code 123 may be configured to perform one or more operations described herein with user equipment 110 in conjunction with one or more processors 120. UE 110 communicates with RAN node 170 via wireless link 111.

[0069] The RAN node 170 in this example is a base station that provides access to the wireless network 100 by wireless devices such as UE 110. The RAN node 170 may be, for example, a base station for 5G (also referred to as New Radio (NR)). In 5G, the RAN node 170 may be a NG-RAN node, which is defined as a gNB or ng-eNB. A gNB is a node that provides NR user plane and control plane protocol termination to the UE and is connected to the 5GC (such as, for example, (multiple) network elements 190) via an NG interface. An ng-eNB is a node that provides E-UTRA user plane and control plane protocol termination to the UE and is connected to the 5GC via an NG interface. An NG-RAN node may include multiple gNBs, which may also include a central unit (CU) (gNB-CU) 196 and (multiple) distributed units (DU) (gNB-DU), where DU 195 is shown. Note that the DU may include or be coupled to and control a radio unit (RU). The gNB-CU is a logical node that hosts the RRC, SDAP and PDCP protocols of the gNB or the RRC and PDCP protocols of the en-gNB and controls the operation of one or more gNB-DUs. The gNB-CU terminates the F1 interface connected to the gNB-DU. The F1 interface is shown as reference 198, although reference 198 also shows a link between a remote element of the RAN node 170 and a centralized element of the RAN node 170, such as a link between a gNB-CU 196 and a gNB-DU 195. The gNB-DU is a logical node that hosts the RLC, MAC and PHY layers of the gNB or en-gNB, and its operation is partially controlled by the gNB-CU. One gNB-CU supports one or more cells. One cell is supported by only one gNB-DU. The gNB-DU terminates the F1 interface 198 connected to the gNB-CU. Note that the DU 195 is considered to include the transceiver 160, for example as part of the RU, but in some examples, the transceiver 160 may be part of a separate RU, for example, controlled by the DU 195 and connected to the DU 195. The RAN node 170 may also be an eNB (evolved NodeB) base station for LTE (Long Term Evolution), or any other suitable base station, access point, access node or node.

[0070] The RAN node 170 includes one or more processors 152, one or more memories 155, one or more network interfaces (N / WI / F) 161, and one or more transceivers 160, which are interconnected by one or more buses 157. Each of the one or more transceivers 160 includes a receiver Rx 162 and a transmitter Tx 163. The one or more transceivers 160 are connected to one or more antennas 158. The one or more memories 155 include computer program code 153. The CU 196 may include (multiple) processors 152, memories 155, and network interfaces 161. Note that the DU 195 may also contain its own memory and (multiple) processors and / or other hardware, but these are not shown.

[0071] The RAN node 170 includes a module 150, which includes one or both of the parts 150-1 and / or 150-2, and the module 150 can be implemented in a variety of ways. The module 150 can be implemented in hardware as a module 150-1, such as being implemented as part of one or more processors 152. The module 150-1 can also be implemented as an integrated circuit or by other hardware (such as a programmable gate array). In another example, the module 150 can be implemented as a module 150-2, which is implemented as a computer program code 153 and executed by one or more processors 152. For example, one or more memories 155 and the computer program code 153 are configured to cause the RAN node 170 to perform one or more operations described herein together with the one or more processors 152. Note that the functionality of the module 150 can be distributed, such as being distributed between the DU 195 and the CU 196, or implemented only in the DU 195.

[0072] One or more network interfaces 161 communicate over a network, such as via links 176 and 131. Two or more gNBs 170 may communicate using, for example, link 176. Link 176 may be wired or wireless or both, and may implement, for example, an Xn interface for 5G, an X2 interface for LTE, or other suitable interfaces for other standards.

[0073] The one or more buses 157 may be address, data, or control buses, and may include any interconnection mechanism, such as a series of wires on a motherboard or integrated circuit, optical fiber or other optical communication device, wireless channel, etc. For example, the one or more transceivers 160 may be implemented as a remote radio head (RRH) 19 for LTE or a distributed unit (DU) 195 for a gNB implementation for 5G, where other elements of the RAN node 170 may be physically located at a different location from the RRH / DU, and the one or more buses 157 may be partially implemented as, for example, a fiber optic cable or other suitable network connection to connect other elements of the RAN node 170 (e.g., central unit (CU), gNB-CU) to the RRH / DU 195. Reference 198 also indicates those suitable network link(s).

[0074] It is worth noting that the description herein indicates that a "cell" performs functions, but it should be clear that the equipment that forms the cell will perform these functions. The cell constitutes part of the base station. That is, each base station can have multiple cells. For example, there can be three cells for a single carrier frequency and associated bandwidth, each cell covering one-third of a 360-degree area, so the coverage area of ​​a single base station covers an approximate ellipse or circle. In addition, each cell can correspond to a single carrier, and the base station can use multiple carriers. Therefore, if there are three 120-degree cells per carrier and there are two carriers, then the base station has a total of 6 cells.

[0075] The wireless network 100 may include one or more network elements 190, which may include core network functions and provide connectivity to another network, such as a telephone network and / or a data communication network (e.g., the Internet), via one or more links 181. Such core network functions for 5G may include (multiple) access and mobility management functions (AMFs) and / or (multiple) user plane functions (UPFs) and / or (multiple) session management functions (SMFs). Such core network functions for LTE may include MME (mobility management entity) / SGW (serving gateway) functions. These are merely illustrative functions that may be supported by (multiple) network elements 190, and it is noted that both 5G and LTE functions may be supported. The RAN node 170 is coupled to the network element 190 via a link 131. The link 131 may be implemented as, for example, an NG interface for 5G, an S1 interface for LTE, or other suitable interfaces for other standards. The network element 190 includes one or more processors 175, one or more memories 171, and one or more network interfaces (N / WI / F) 180, which are interconnected by one or more buses 185. The one or more memories 171 include computer program code 173. The one or more memories 171 and computer program code 173 are configured to, together with the one or more processors 175, cause the network element 190 to perform one or more operations.

[0076] The wireless network 100 can implement network virtualization, which is the process of combining hardware and software network resources and network functions into a single software-based management entity (virtual network). Network virtualization involves platform virtualization, usually combined with resource virtualization. Network virtualization is classified into external virtualization and internal virtualization, where external virtualization combines many networks or parts of networks into a virtual unit and internal virtualization provides network-like functions to software containers on a single system. For example, a network can be deployed in a telecommunications cloud, with virtualized network functions (VNFs) running on, for example, data center servers. For example, network core functions and / or radio access networks (e.g., CloudRAN, O-RAN, edge cloud) can be virtualized. Please note that the virtualized entities produced by network virtualization are still implemented using hardware (such as processors 152 or 175 and memories 155 and 171) to some extent, and such virtualized entities produce technical effects.

[0077] It may also be noted that the operations of the example embodiments of the present disclosure may be performed by multiple cooperating devices (eg, cRAN).

[0078] Computer readable memories 125, 155, and 171 may be of any type suitable for the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory, and removable memory. Computer readable memories 125, 155, and 171 may be components for performing storage functions. Processors 120, 152, and 175 may be of any type suitable for the local technical environment and may include, as non-limiting examples, one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. Processors 120, 152, and 175 may be components for performing functions, such as controlling UE 110, RAN node 170, and other functions described herein.

[0079] In general, various example embodiments of user device 110 may include, but are not limited to, cellular telephones, such as smart phones with wireless communication capabilities, tablet computers, personal digital assistants (PDAs), portable computers with wireless communication capabilities, image capture devices with wireless communication capabilities (such as digital cameras), gaming devices with wireless communication capabilities, music storage and playback devices with wireless communication capabilities, Internet devices allowing wireless Internet access and browsing, tablet computers with wireless communication capabilities, and portable units or terminals that combine these functions.

[0080] Having thus introduced a suitable but non-limiting technical context for the practice of example embodiments of the present disclosure, the example embodiments will now be described in more detail.

[0081] The features described in this article generally relate to Internet of Things (IoT) devices, processes and / or use cases. Regarding IoT applications, 3GPP has specified NB-IoT / eMTC and NR RedCap before Release 18 to meet the requirements for low-cost and low-power devices for wide-area IoT communications. These IoT devices typically consume tens or hundreds of milliwatts of power during transceiver (i.e., sending and / or receiving) and cost only a few dollars. However, to realize the Internet of Everything, IoT devices with ten or even a hundred times lower cost and power consumption are needed, especially for a large number of applications that require battery-free devices.

[0082] In recent years, the number of IoT connections has grown rapidly and is expected to reach hundreds of billions by 2030. As more and more "things" are expected to be interconnected to improve production efficiency and increase living comfort, this requires further reduction in the size, cost, and power consumption of IoT devices. In particular, it is impractical to regularly replace the batteries of all IoT devices due to the large consumption of materials and manpower. It has become a trend to use energy harvested from the environment to power IoT network devices to achieve self-sufficient communication, especially in applications with a large number of devices (e.g., ID tags and sensors).

[0083] The most critical issue of existing 3GPP technologies for the target use case (i.e. IoT) is the ability to collaborate with energy harvesting considering the limited device size. Cellular devices typically consume tens or even hundreds of milliwatts of power for transceiver processing. Taking the NB-IoT module as an example, the typical current consumption for receive processing is about 60mA (with a power supply voltage above 3.1V), and 70mA for transmit processing at 0dBm transmit power. In addition, considering that the size of the actual device is only a few square centimeters, the output power provided by a typical energy harvester is mostly less than 1 milliwatt. Since the available power is much less than the consumed power, it is impractical to directly power cellular devices through energy harvesting in most cases.

[0084] One possible solution is to integrate energy harvesting with rechargeable batteries or supercapacitors. However, there are still some issues that need to be addressed.

[0085] First, in practical situations, the life of both rechargeable batteries and supercapacitors may be shortened. It is difficult to provide a constant charging current or voltage through energy harvesting, and since the output power of the energy harvester is very small, long-term continuous charging is required. Both the inconsistent charging current and long-term continuous charging will damage the battery life. For supercapacitors, their life will be significantly shortened in high temperature environments (for example, less than 3 years at 50 degrees Celsius).

[0086] Second, the device size will be significantly increased. Since a small-sized button cell can only provide a few tens of milliamps of current, a battery with a much larger size (e.g., AA battery) is usually used to power a cellular device, which may even be larger than the module itself. In order to store energy for a suitable operating duration (e.g., one second), the capacitance required for a supercapacitor is on the order of one hundred millifarads. The size of such a supercapacitor may be larger than the NB-IoT module.

[0087] Third, both rechargeable batteries and supercapacitors can be more expensive than the module itself. Even if purchased in large quantities, the cost of a suitable battery or supercapacitor can be one to several dollars, nearly doubling the cost of the device.

[0088] Radio Frequency Identification (RFID) is the most well-known technology that supports battery-free tags (devices). Commercial passive RFID tags can consume as little as 1 microwatt. The key technologies to achieve such low power consumption are envelope detection for downlink data reception and backscatter communication for uplink data transmission.

[0089] RFID is designed for short-range communication, with a typical effective range of less than 10 meters. Since the air interface of RFID has hardly changed since 2005, the overly simple transmission scheme has become an obstacle to improving its link budget and supporting scalable network capabilities.

[0090] Attracted by the extremely low power consumption of backscatter communication, many non-3GPP technologies have begun to invest in related research, such as Wi-Fi, Bluetooth, Ultra Wideband (UWB) and Long Range (LORA). Various studies have shown that passive tags based on or with minor modifications to the above air interfaces can support power consumption of several microwatts or tens of microwatts.

[0091] A considerable part of the research is aimed at long-distance communication. Among them, LoRa tags implemented with commercial off-the-shelf components can send their sensing data to a receiver 381 meters away. Currently, most of the research focuses on independent detailed technologies for various optimization goals. It is difficult to see a comprehensive system design that fully meets the requirements of the target use case (i.e. IoT). However, the standardization of those technologies is agile and fast because the industry usually follows some de facto standards. Therefore, many products on the market will even follow private standards once it shows competitiveness in certain applications.

[0092] A passive radio is a device that harnesses the energy from a wireless signal sent over a specific carrier and / or bandwidth and charges a simple circuit system that, once activated, emits / reflects at least a signal encoding the ID of the passive radio. The typical system architecture around a passive radio includes:

[0093] - Activator: A device that sends an activation signal, intended to wake up a passive radio. In this disclosure, the terms "activator", "activator radio", "activation device", "second apparatus" and "second radio device" may be used interchangeably.

[0094] - Passive radio: utilizes energy over a range of frequencies and listens for an activation signal. Once such a signal is detected, the passive radio transmits / reflects a signal specific to the radio ID of the passive radio. In the present disclosure, the terms "passive radio", "tag", "third radio device", "third apparatus", "IoT device", and "a-IoT device" may be used interchangeably. It may be noted that example embodiments of the present disclosure are not limited to passive radios; a-IoT devices may also be semi-passive radios or active radios.

[0095] -Reader: A device that listens for and detects passive radio signals from a passive radio. The reader may or may not be co-located with the activator. The reader (radio / device) may be considered a radio configured to detect a-IoT signals (e.g., tag / reply signals). In this disclosure, the terms "reader", "reader radio", "reader device", "first apparatus", and "first radio device" may be used interchangeably.

[0096] RAN level research projects have been approved, most recently in RAN#98-e (RP-223396), where the following two main equipment types have been identified:

[0097] “…in terms of energy storage, the research will consider the following device characteristics:

[0098] (Passive) A purely battery-free device with absolutely no energy storage capability and completely dependent on the availability of external energy sources

[0099] (semi-passive) A device with limited energy storage capacity that does not require manual replacement or recharging.

[0100] Device categorization based on corresponding characteristics (e.g. energy source, energy storage capability, passive / active transmission, etc.) may be discussed during the study, taking into account the relevant use cases. The peak power consumption of a device should be limited by its practical form factor for the intended use case and should take into account its energy source…”

[0101] The objectives of this study project can be grouped into three areas: (i) deployment scenarios; (ii) design goals and (iii) performance evaluation. In terms of deployment scenarios, operations in unlicensed spectrum are emphasized:

[0102] “…identify suitable deployment scenarios and their characteristics, at least for the use cases / services agreed upon in SA1 “Ambient Power Support for IoT Research”, including at least the following aspects

[0103] Indoor / Outdoor Environment

[0104] Base station characteristics, such as macro / micro / pico cell-based deployment

[0105] Connectivity topology, including which node(s) (e.g., base station, UE, repeater, relay, etc.) can communicate with the target device

[0106] TDD / FDD and frequency bands in licensed or unlicensed spectrum

[0107] Coexistence with UEs and infrastructure in frequency bands used for existing 3GPP technologies

[0108] Device-originated and / or device-terminated service assumptions

[0109] NOTE: Multiple deployment scenarios may be identified for a use case, and a deployment scenario may be common to multiple use cases.

[0110] NOTE: In cases where multiple deployment scenarios are identified for a use case, the trade-offs between them should also be studied.

[0111] NOTE: Studies should not prioritize aspects of deployment that should be coordinated with SA, such as public or private networks, with or without CN connectivity.

[0112] NOTE: A representative use case can be studied for a group of use cases with similar requirements…”

[0113] In RAN#98ec, it was agreed to focus on three device types: Device A: passive device without energy storage; Device B: passive device with energy storage; and Device C: active device with energy storage. The design goals for power consumption are: Device A ≤ 10μW; Device A < Device B < Device C; and Device C ≤ 1mW. The device complexity design goals are: Device A: comparable to UHFRFID; Device A ≤ Device B ≤ Device C; and Device C: several orders of magnitude lower than NB-IoT. During RAN#99, the rapporteur listed the following functions that need to be addressed in the RAN for Ambient IoT:

[0114] “… Reporter: Assumptions about the functionality that potentially needs to be supported for Ambient IoT in the RAN need to be identified. To avoid detailed WG level analysis, potential functionality is expected to be generic and satisfy the RAN design goals for the Ambient IoT air interface. The following potential assumed RAN functionality was found in the submission (see also the design goals above).

[0115] Security (authentication, encryption, data integrity, authorization)

[0116] Positioning / Localization / Ranging

[0117] Support for channel access rules related to unlicensed spectrum.

[0118] Coexist with legacy systems, devices and network deployments

[0119] Energy harvesting signals and / or use of traditional signals for energy harvesting

[0120] Device identification and management

[0121] Possibility of CN connectivity, including for sporadic and opportunistic small data between devices and the core

[0122] It is recommended to send LS to SA regarding the feasibility of solution with / without CN

[0123] Backscatter modulation

[0124] Envelope Detection in Receivers

[0125] Non-OFDM waveforms in DL, robustness to low-precision receiver architectures, compact protocol layer design

[0126] Anti-collision method / Random access process / Tag response load distribution

[0127] Interference mitigation (intra-reader, inter-reader, reader-to-cellular)

[0128] For topologies (1) and (2), gNB / UE / intermediate nodes may require limited full-duplex capabilities

[0129] Mobility Management Process

[0130] Communicate with all, a subset, or one of the existing Ambient IoT devices Activate and deactivate ambient IoT devices

[0131] NW configuration of signals and channels for communicating with Ambient IoT devices

[0132] Control of when tags reflect / react to received signals when addressed

[0133] DRX

[0134] Robust synchronization scheme for low-precision receiver architectures

[0135] Coverage enhancement technology with low equipment complexity..."

[0136] In TR 38.848, the following connectivity topologies for ambient IoT (A-IoT) networks and devices are defined for research purposes. In all these topologies, ambient IoT devices can be provided with carriers from (multiple) other nodes inside or outside the topology. The links in each topology can be bidirectional or unidirectional. The base station (BS), UE, auxiliary node or intermediate node can be multiple BSs or UEs respectively. The placement of a mixture of indoor and outdoor of these nodes is considered as a network implementation choice.

[0137] Ambient IoT devices can be described as low-power devices, low-cost devices, and / or devices with limited functionality / processing capabilities. An example of an ambient IoT (A-IoT) device is a passive radio that can harness the energy of a wireless signal sent over a specific carrier and / or bandwidth and charge a simple circuit system that, once activated, can emit / reflect a signal that can encode at least the ID of the passive radio. Other examples of A-IoT devices include semi-passive devices, battery-free devices, and low-cost simplified mobile devices. A-IoT devices can be used, for example, for indoor / outdoor positioning.

[0138] Reference now Figure 2 , shows an example of topology 1: BS<->AIoT device. In topology 1, ambient IoT device (220) communicates (230) directly and bidirectionally with base station (210). Communications between base station (210) and ambient IoT device (220) include ambient IoT data and / or signaling (230). This topology includes the possibility that the BS sending to the ambient IoT device is different from the BS receiving from the ambient IoT device.

[0139] Reference now Figure 3 , an example of topology 2 is shown: BS<->intermediate node<->AIoT device. In topology 2, the ambient IoT device (330) communicates bidirectionally (350) with the intermediate node (320) between the device (330) and the base station (310). In this topology, the intermediate node (320) can be a relay, an integrated access and backhaul (IAB) node, a UE, a repeater, etc., which can implement ambient IoT. The intermediate node (320) transmits information (340) between the BS (310) and the ambient IoT device (330).

[0140] Referring now to FIG. 4 , an example of Topology 3 is shown: BS<->Assistant Node<->AIoT Device<->BS Device. In Topology 3, the ambient IoT device (420) sends data / signaling (440) to the base station (410) and receives data / signaling (460) from the auxiliary node (430) (e.g., Figure 4A or the ambient IoT device (420) receives data / signaling (470) from the base station (410) and sends data / signaling (490) to the auxiliary node (430) (as shown); Figure 4B In this topology, the auxiliary node (430) can be a relay, IAB, UE, repeater, etc., which has the capability of the ambient Internet of Things IoT and receives data / signaling (450) from the base station (410) (as shown in FIG. Figure 4A ) or sends data / signaling to a base station (410) (such as Figure 4B shown).

[0141] Reference now Figure 5 , shows an example of topology 4: UE<->AIoT device. In topology 4, the ambient IoT device (520) communicates (530) bidirectionally with the UE (510). The communication (530) between the UE (510) and the ambient IoT device (520) includes ambient IoT data and / or signaling.

[0142] Reading ambient IoT devices is a challenging task due to the inherent nature of tag devices. More precisely, tags have no independent power supply, are usually mobile, and can only hear other radios in their own vicinity (typically a radius of 5-10 meters) due to low receiver complexity. For these reasons, tags are unable to perform typical initial access procedures (such as paging response, random access, and neighbor cell monitoring procedures). Moreover, the introduction of new tags in the area, the mobility of tags, data collection and transmission capabilities should be transparent to the NR NW. Due to the above limitations, the NR NW cannot apply typical NRUE paging operations, and alternatives need to be defined.

[0143] The NR NW can read (i.e. receive / decode data / signaling from a tag) only if the tag hears a loud enough activation signal so that: it can be sufficiently charged; and it can generate (in response to the activation signal) a signal that is loud enough to be heard by another nearby NW element (gNB, RSU, UE, typically no more than about 100m away, etc.).

[0144] Achieving the generation of a loud enough signal is not trivial. Multiple tags may reply on the same time-frequency resource, thereby interfering with each other during the response. This type of interference is called tag-to-tag (TT) interference, and it affects the reader's ability to distinguish between tags that respond concurrently by using the same time-frequency resource. In addition, tags typically reply / reflect on the same carrier as used by the activation signal. Since the activation signal can be several orders of magnitude stronger than the tag reply, a phenomenon known as activator-to-tag (AT) interference occurs. In other words, the tag reply is drowned out by the activation signal, and the reader must be able to isolate the activation signal before attempting to detect the tag.

[0145] A technical effect of an example embodiment of the present disclosure may be to enable reading of tags that suffer from activator-to-tag (AT) interference in a 5G NR NW.

[0146] In the present disclosure, the terms “reply signal”, “a-IoT (device) signal” and “tag signal” may be used interchangeably.

[0147] In an example embodiment, the reader may be enabled to isolate the activation signal from the tag signal. In an example embodiment, the reader may learn and / or predict the channel behavior between itself and the activator during a preparation step when the tag remains silent. In an example embodiment, the reader may use the learning / information to determine and / or predict a combiner so that the post-processed activation signal and the tag signal: are in orthogonal or isolated space; or the activation signal appears to be suppressed on the reader side. In an example embodiment, the application of the combiner may cause the tag signal to become distinguishable from the activation signal. In an example embodiment, the combiner may be determined based on all or part of the channel knowledge.

[0148] In an example embodiment, the preparation step may be included in a tag reading session in which the activator and the reader may communicate with each other (i.e., the tag is activated, the tag sends a reply, and the reader detects the reply). To this end, a preparation reference signal may need to be exchanged between the activator and the reader.

[0149] In this disclosure, the terms "reference signal" and "preparation reference signal" may be used interchangeably.

[0150] In an example embodiment, preparation signaling can occur shortly before a tag read session (e.g., within the channel coherence time), for example, so that the channel activator-reader acquired during the preparation step can be used to isolate the two signals transmitted later (i.e., the activation signal and the tag reply signal). In order to determine how quickly a read must be triggered after the preparation step, a preconfigured set of time slots can be reserved for potential activations. The preconfigured set of time slots can include (multiple) time slots in which the activation signal can be sent. Then, a unique time slot can be selected for the actual transmission based on the results of the preparation step and the processing capabilities of the reader.

[0151] In an example embodiment, the reader UE may isolate the activation signal and the tag signal after they have been sent. For example, assume a scenario between the activator UE, the reader UE, and the tag. The activation signal is represented as s a , and the tag signal is denoted as s. The activator-reader (ARC) channel response is denoted as h a The tag-reader channel response is denoted as h. Tag detection may include the following: A reference signal is sent by the activator to the reader at time t0 so that the reader can estimate and the maximum Doppler shift The estimation can be done using the most advanced methods available.

[0152] In the event that the quality of the activator-reader channel estimate is not good enough, the reader may trigger the activator to send an additional transmission of the reference signal. This determination may be based on the measured signal to interference plus noise ratio (SINR) (i.e., a very low SINR associated with the reference signal may result in a poor estimate) or the variance of the soft channel estimate (i.e., in the event of a large variance, it may mean that the average is not guaranteed to be a good point estimate).

[0153] Use estimated The reader UE may: schedule the activator for transmission of an activation signal at time t1; and obtain the combiner The activation signal may be applied after it has been sent at a determined time slot t1, for example, so that the activation signal and the tag signal are aligned away from each other (ie isolated).

[0154] Assume that at time t1, the reader receives the signal

[0155] y(t1)=h(t1)s+h a (t1)s a +n(t1) (1)

[0156] Where Nr is the number of receiving antennas, the first term is the useful tag signal, the second term is the activation signal, and the third term is additive white Gaussian noise.

[0157] The reader can combine Applied to the signal y(t1) to isolate the label signal:

[0158]

[0159] Combiners It can be obtained by: using And the activation signal is suppressed.A technical effect of example embodiments of the present disclosure may be that the activation power seen by the reader is minimized.

[0160] In an example embodiment, the reader UE may obtain a combiner. The reader UE may first use To approximate

[0161]

[0162] Wherein f represents the reconstruction function that can be obtained by the UE.

[0163] In a first example embodiment, the reconstruction function may be implemented as a linear interpolation (eg, robust Wiener filtering).

[0164] In a second example embodiment, the reader UE may collect samples l = -1:-L, t l ≤ t0 and an autoregressive (AR) model of order P <= L is formulated for the channel response. The model parameters are then approximated using the collected past samples. Once the model is obtained, it can be used to estimate the channel at the current time.

[0165] In a third exemplary embodiment, the function f can be obtained by applying supervised learning tools (e.g., via a neural network such as a deep neural network (DNN), a convolutional neural network (CNN), etc.) and is formulated as a channel prediction regression task, e.g., using the true channel response h a (t1) as a label, and the inputs t0, t1, and the regressor can compute the parameters a of the function i , b i , i = 1:N, where, for example, f i (x) = a i x + b i , such that the estimated channel response and the true h a (t1) are minimized.

[0166] In an exemplary embodiment, after the reader UE has approximated , the reader UE can compute a combiner:

[0167]

[0168] The technical effect of the exemplary embodiments of the present disclosure can be the minimization of the activation signal.

[0169] In a fourth exemplary embodiment, the reader UE can approximate the labeled channel response h(t1) using past responses obtained at time t < t1, e.g., when the tag detection is periodic. Then, instead of picking a combiner w that suppresses the activation signal, the reader UE can compute a combiner w that maximizes the tag-to-activation signal ratio (i.e., the post-combining signal-to-interference ratio (SIR) of the tag).

[0170] In a fifth example embodiment, the reader can use partial channel information and adjust the current combiner. For example, the reader can use information about the difference between h(t0) and h(t1) to directly adjust the combiner w(t0) to the value w(t1). For example, if the combiner at time t is derived using (4), and the reader has obtained a function z that approximates the relationship between h(t0) and h(t1), i.e., h(t1)≈z(h(t0)), then the reader can use (4) and z to learn the direct relationship between w(t0) and w(t1), i.e., w(t1)=q(w(t0), t1, t0). Learning can be directed to determining the parameters of the function w, for example, so that the relationship between the current and past combiners can be well described. Learning can be implemented by applying tools from supervised learning (e.g., deep neural networks) or reinforcement learning.

[0171] A neural network (NN) is a computational graph that includes two or more layers of computation. Each layer may include one or more units, where each unit may perform a basic computation. A unit may be connected to one or more other units, and the connections may have weights associated with them. Weights may be used to scale the signal passing through the associated connection. Weights may be learnable parameters, i.e., values ​​that can be learned from training data. There may be other learnable parameters, such as those of a batch normalization layer.

[0172] The two most widely used architectures for neural networks are feedforward and recurrent architectures. Feedforward neural networks do not include feedback loops; each layer takes input from one or more of the previous layers and provides output, which is used as input for one or more of the subsequent layers. Units within a layer take input from unit(s) in one or more previous layers and provide output to unit(s) in one or more subsequent layers.

[0173] The initial layers, i.e. the layers close to the input data, extract semantically low-level features from the received data, and the intermediate and final layers extract more higher-level features. After the feature extraction layer, there may be one or more layers that perform specific tasks such as classification, semantic segmentation, object detection, denoising, style transfer, super-resolution, etc. In a recurrent neural network, there is a feedback loop that makes the network stateful, i.e. it is able to remember or retain information or state.

[0174] Neural networks and other machine learning tools may be able to learn properties from input data in a supervised or unsupervised manner. Such learning may be the result of a training algorithm or a meta-level neural network that provides the training signal.

[0175] The training algorithm may involve changing some properties of the neural network so that the output of the neural network is as close as possible to the desired output. Training may involve changing the properties of the neural network in order to minimize or reduce the error in the output, also known as the loss. Examples of loss include mean squared error (MSE), cross entropy, etc. In recent deep learning techniques, training is an iterative process where, at each iteration, the algorithm modifies the weights of the neural network to gradually improve the output of the network, i.e., to gradually reduce the loss.

[0176] Reference now Figure 6 , a signaling flow chart according to an example of a first example embodiment is shown. At 610, the reader may receive a reference signal from the activator at time t0. At 620, the reader may estimate the ACR, time selective information (TSI), and the maximum Doppler shift at t0. At 630, the reader may select a time t1 for tag activation (i.e., transmitting the activation signal from the activator to the tag). At 640, the reader may calculate the ACR at t1 (e.g., according to the first example embodiment). At 650, the reader may determine the combiner w(t1) based at least in part on the calculated ACR at t1. At 660, the reader may receive a tag reply signal and an activation signal at time t1. At 670, the reader may isolate the signal via the combiner w(t1). At 680, the reader may complete tag detection.

[0177] Reference now Figure 7 , a signaling flow chart according to an example of a second example embodiment is shown. At 710, the reader may receive a reference signal from an activator at, for example, time t0, t-1, ..., tL. At 720, the reader may estimate the ACR, TSI, and maximum Doppler shift at t0. At 730, the reader may select time t1 for tag activation. At 740, the reader may calculate the ACR at t1 using a P-order AR model (e.g., according to a second example embodiment). At 750, the reader may determine a combiner w(t1) based at least in part on the ACR calculated at t1. At 760, the reader may receive a tag reply signal and an activation signal at time t1. At 770, the reader may isolate the signal via the combiner w(t1). At 780, the reader may complete tag detection.

[0178] Reference now Figure 8 , shows examples of the SINR of a tag before (810, 830, 850, 870) and after (820, 840, 860, 880) alignment after the activation signal, which can illustrate the potential technical effects of the example embodiments of the present disclosure (i.e., aligning the activation signal away from the tag signal via the determination of the zero-forcing combiner and application in the reader). Figure 8In the example of , the reader can be equipped with 64 receive antennas. It can be observed that the orthogonalization can be successful even in very low tag-to-activation SINR regimes. For example, for a tag SINR of -50dB, the application of a combiner at the reader can increase the post-processing SINR to 5dB. Note that for simplicity, Figure 8 Ideal ARC acquisition at the reader is considered.

[0179] Example embodiments of the present disclosure may be implemented for an IAB node. The IAB node logically includes a mobile terminal (MT) (e.g., an IAB MT) that communicates with an upstream node and a RAN component (such as a distributed unit (DU) (e.g., an IAB DU)) that communicates with a downstream IAB node or subscriber UE 110. In NR / 5G, the gNB may include a split architecture in which a centralized unit (CU) and a distributed unit (DU) each perform a subset of the functions of the gNB. In other words, the MT portion of the IAB node may be used to communicate with a parent node, while the DU portion of the IAB node may be used to communicate with a child node or a UE. The mobile terminal (MT) of the IAB node may communicate with a parent node (such as an IAB donor node). The IAB donor node may include a centralized unit of the gNB and may also include a distributed unit. The distributed unit (DU) of the IAB node may communicate with a child node or a user equipment.

[0180] Example embodiments of the present disclosure may be implemented for SL devices. SL devices may be used to provide communication between a vehicle and a network, (multiple) infrastructure, (multiple) other vehicles, or (multiple) other road users in the surrounding / neighboring area. Such communication may enable proximity services (ProSe), or the transmission of information about the surrounding environment between close-range devices, such as device-to-device (D2D) communication technology. This direct communication may be available even when network coverage is not available. Additionally or alternatively, the NR SL method may be implemented in scenarios that are not related to traffic users, such as public safety scenarios and / or commercial scenarios. SL use cases may involve the Internet of Things (IoT) and the automotive industry (e.g., to reduce the risk of accidents and provide a safer driving experience). These use cases may include message exchanges between vehicles (V2V), vehicles and pedestrians (V2P), vehicles and infrastructure (V2I), and vehicles and networks (V2N), and may be referred to as vehicle-to-everything (V2X). The allocation of V2V resources (i.e., time and frequency resources) in a cell may be controlled by the cellular network structure or may be performed autonomously by individual vehicles (e.g., their UE devices). The sidelink may use the same or a different carrier frequency or frequency band as the cellular communication.

[0181] Fig. 9Potential steps of an example method 900 are shown. Example method 900 may include: 910, receiving at least one reference signal from a second device associated with a session with a third device; 920, determining a combiner, wherein the determined combiner is configured to read the third device; 930, receiving at least one activation signal and a reply signal at a second time, wherein at least one activation signal is received from the second device, wherein the reply signal is received from the third device; and 940, applying the determined combiner to the at least one activation signal and the reply signal. Example method 900 may be performed, for example, using a reader UE, a radio configured to detect a passive radio, an IoT signal, or an a-IoT signal (e.g., a tag / reply signal), a reader radio, a reader device, a first device, a first radio device, etc. The third device may be, for example, a passive radio, a tag, an IoT device, an a-IoT device, etc.

[0182] According to an example embodiment, a device may include: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the device to at least: receive at least one reference signal from a second device associated with a session with a third device; determine a combiner, wherein the determined combiner may be configured to read the third device; receive at least one activation signal and a reply signal, wherein at least one activation signal is received from the second device and wherein the reply signal is received from the third device; and apply the determined combiner to the at least one activation signal and the reply signal.

[0183] An example device may include a reader.

[0184] The second device may include an activator.

[0185] The third device may include an ambient IoT device.

[0186] The determined combiner may be configured to at least one of minimize a contribution of the at least one activation signal in the channel towards the second device or to isolate the at least one activation signal in the channel towards the second device.

[0187] The example apparatus may be further configured to determine one or more first conditions of the channel toward the second apparatus, wherein the combiner may be determined based at least in part on the one or more first conditions.

[0188] The one or more first conditions may be determined based at least in part on at least one reference signal.

[0189] The one or more first conditions may include at least one of: a channel response of a channel toward the second device at a time when the at least one reference signal is received, a Doppler shift at a time when the at least one reference signal is received, a signal to interference plus noise ratio at a time when the at least one reference signal is received, or a variance of at least one estimate of the channel toward the second device.

[0190] The example apparatus may be further configured to determine a time at which at least one of the activation signal and the reply signal is received based at least in part on the one or more first conditions.

[0191] The time at which at least one activation signal and the reply signal are received may be determined based at least in part on at least one reference signal.

[0192] Determining the combiner may include an example device that can also be configured as follows: determining one or more second conditions of a channel toward a second device at a time when at least one activation signal and a reply signal are received based at least in part on the one or more first conditions and a reconstruction function; and determining the combiner based at least in part on the one or more second conditions.

[0193] The reconstruction function may include at least one of the following: a linear interpolation function or a robust Wiener filter.

[0194] The reconstruction function may include a channel prediction task performed using the model.

[0195] Determining the combiner may include an example device that is also configured to: determine one or more second conditions of a channel toward a second device at one or more times that are earlier than or equal to the time when at least one reference signal is received; estimate one or more third conditions of the channel toward the second device at a time when at least one activation signal and a reply signal are received based at least in part on the one or more first conditions and the one or more second conditions using an autoregressive model; and determine the combiner based at least in part on the estimated one or more third conditions of the channel toward the second device.

[0196] Determining the combiner may include an example device that is also configured to: determine one or more second conditions of a channel toward a second device at one or more times earlier than the time when at least one activation signal and a reply signal are received; estimate one or more third conditions of the channel toward the second device at the time when at least one activation signal and a reply signal are received based at least in part on the one or more second conditions; and determine the combiner based at least in part on the one or more third conditions, wherein the determined combiner is configured to maximize the quality of reception of the reply signal.

[0197] The ratio between the reply signal and the at least one activation signal may comprise a signal to interference ratio associated with the third device.

[0198] The determined combiner may include an example device that is also configured to: determine a first combiner for determining a time at which at least one reference signal is received; estimate, based at least in part on partial channel information, a difference between one or more first conditions and one or more second conditions of a channel toward a second device at a time at which at least one activation signal and a reply signal are received; and adjust the first combiner based at least in part on the estimated difference, wherein the determined combiner includes the adjusted combiner.

[0199] According to one aspect, an example method may be provided, comprising: utilizing a first device to receive at least one reference signal from a second device associated with a session with a third device; determining a combiner, wherein the determined combiner may be configured to read the third device; receiving at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device, wherein the reply signal is received from the third device; and applying the determined combiner to the at least one activation signal and the reply signal.

[0200] The first device may include a reader.

[0201] The second device may include an activator.

[0202] The third device may include an ambient IoT device.

[0203] The determined combiner may be configured to at least one of minimize a contribution of the at least one activation signal in the channel towards the second device or to isolate the at least one activation signal in the channel towards the second device.

[0204] The example method may also include determining one or more first conditions of the channel toward the second device, wherein the combiner may be determined based at least in part on the one or more first conditions.

[0205] The one or more first conditions may be determined based at least in part on at least one reference signal.

[0206] The one or more first conditions may include at least one of: a channel response of a channel toward the second device at a time when the at least one reference signal is received, a Doppler shift at a time when the at least one reference signal is received, a signal to interference plus noise ratio at a time when the at least one reference signal is received, or a variance of at least one estimate of the channel toward the second device.

[0207] The example method may also include determining a time at which at least one of the activation signal and the reply signal is received based at least in part on the one or more first conditions.

[0208] The time at which at least one activation signal and the reply signal are received may be determined based at least in part on at least one reference signal.

[0209] Determining the combiner may include: determining one or more second conditions of a channel toward a second device at a time when at least one activation signal and a reply signal are received based at least in part on the one or more first conditions and a reconstruction function; and determining the combiner based at least in part on the one or more second conditions.

[0210] The reconstruction function may include at least one of the following: a linear interpolation function or a robust Wiener filter.

[0211] The reconstruction function may include a channel prediction task performed using the model.

[0212] Determining the combiner may include: determining one or more second conditions of the channel toward the second device at one or more times earlier than or equal to the time when at least one reference signal is received; estimating one or more third conditions of the channel toward the second device at the time when at least one activation signal and a reply signal are received based at least in part on the one or more first conditions and the one or more second conditions using an autoregressive model; and determining the combiner based at least in part on the estimated one or more third conditions of the channel toward the second device.

[0213] Determining the combiner may include: determining one or more second conditions of the channel toward the second device at one or more times earlier than the time when at least one activation signal and the reply signal are received; estimating one or more third conditions of the channel toward the second device at the time when at least one activation signal and the reply signal are received based at least in part on the one or more second conditions; and determining the combiner based at least in part on the one or more third conditions, wherein the determined combiner is configured to maximize the quality of reception of the reply signal.

[0214] The ratio between the reply signal and the at least one activation signal may include a signal to interference ratio associated with the third device.

[0215] Determining the combiner may include: determining a first combiner for a time when at least one reference signal is received; estimating, based at least in part on partial channel information, differences between one or more first conditions and one or more second conditions of a channel toward a second device at a time when at least one activation signal and a reply signal are received; and adjusting the first combiner based at least in part on the estimated differences, wherein the determined combiner includes an adjusted combiner.

[0216] According to an example embodiment, a device may include: a circuit system configured to perform the following: receiving at least one reference signal from a second device associated with a session with a third device; a circuit system configured to perform the following: determining a combiner, wherein the determined combiner can be configured to read the third device; a circuit system configured to perform the following: receiving at least one activation signal and a reply signal, wherein at least one activation signal is received from the second device, wherein the reply signal is received from the third device; and a circuit system configured to perform the following: applying the determined combiner to the at least one activation signal and the reply signal.

[0217] According to an example embodiment, a device may include: a processing circuit system; a memory circuit system including a computer program code, the memory circuit system and the computer program code being configured to, together with the processing circuit system, enable the device to: receive at least one reference signal from a second device associated with a session with a third device; determine a combiner, wherein the determined combiner may be configured to read the third device; receive at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device and wherein the reply signal is received from the third device; and apply the determined combiner to the at least one activation signal and the reply signal.

[0218] As used in this application, the term "circuitry" may refer to one or more or all of the following: (a) a hardware circuit implementation only (such as an implementation in analog and / or digital circuitry only) and (b) a combination of hardware circuitry and software, such as (as applicable): (i) a combination of (multiple) analog and / or digital hardware circuits with software / firmware and (ii) any portion of (multiple) hardware processors (including (multiple) digital signal processors), software and (multiple) memories with software that work together to enable a device (such as a mobile phone or server) to perform various functions and (c) (multiple) hardware circuits and / or (multiple) processors, such as (multiple) microprocessors or portions of (multiple) microprocessors, which require software (e.g., firmware) to operate, but the software may not be present when software is not required to operate. This definition of circuitry applies to all uses of the term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of only a hardware circuit or processor (or multiple processors) or a portion of a hardware circuit or processor and its (their) accompanying software and / or firmware. The term circuitry also covers, for example, a baseband integrated circuit or a processor integrated circuit for a mobile device or a similar integrated circuit in a server, cellular network device, or other computing or networking device, if applicable to a particular claim element.

[0219] According to an example embodiment, a device may include components for: receiving at least one reference signal from a second device associated with a session with a third device; a circuit system configured to perform the following: determining a combiner, wherein the determined combiner may be configured to read the third device; a circuit system configured to perform the following: receiving at least one activation signal and a reply signal, wherein at least one activation signal is received from the second device, wherein the reply signal is received from the third device; and a circuit system configured to perform the following: applying the determined combiner to the at least one activation signal and the reply signal.

[0220] The device may include a reader.

[0221] The second device may include an activator.

[0222] The third device may include an ambient IoT device.

[0223] The determined combiner is configured to at least one of minimize a contribution of the at least one activation signal in the channel toward the second device or to isolate the at least one activation signal in the channel toward the second device.

[0224] The component may be further configured to determine one or more first conditions of the channel toward the second device, wherein the combiner may be determined based at least in part on the one or more first conditions.

[0225] The one or more first conditions may be determined based at least in part on at least one reference signal.

[0226] The one or more first conditions may include at least one of: a channel response of a channel toward the second device at a time when the at least one reference signal is received, a Doppler shift at a time when the at least one reference signal is received, a signal to interference plus noise ratio at a time when the at least one reference signal is received, or a variance of at least one estimate of the channel toward the second device.

[0227] The component may also be configured to determine when at least one of the activation signal and the reply signal is received based at least in part on the one or more first conditions.

[0228] The time at which at least one activation signal and the reply signal are received may be determined based at least in part on at least one reference signal.

[0229] The component configured to determine the combiner may include a component configured to: determine one or more second conditions of a channel toward a second device at a time when at least one activation signal and a reply signal are received based at least in part on the one or more first conditions and a reconstruction function; and determine the combiner based at least in part on the one or more second conditions.

[0230] The reconstruction function may include at least one of the following: a linear interpolation function or a robust Wiener filter.

[0231] The reconstruction function may include a channel prediction task performed using the model.

[0232] The component configured to determine the combiner may include a component configured to: determine one or more second conditions of the channel toward the second device at one or more times earlier than or equal to the time when at least one reference signal is received; estimate one or more third conditions of the channel toward the second device at the time when at least one activation signal and a reply signal are received based at least in part on the one or more first conditions and the one or more second conditions using an autoregressive model; and determine the combiner based at least in part on the estimated one or more third conditions of the channel toward the second device.

[0233] The components configured to determine a combiner may include components configured to: determine one or more second conditions of a channel toward a second device at one or more times earlier than the time when at least one activation signal and a reply signal are received; estimate one or more third conditions of the channel toward the second device at the time when at least one activation signal and a reply signal are received based at least in part on the one or more second conditions; and determine the combiner based at least in part on the one or more third conditions, wherein the determined combiner is configured to maximize the quality of reception of the reply signal.

[0234] The ratio between the reply signal and the at least one activation signal may include a signal to interference ratio associated with the third device.

[0235] The component configured to determine the combiner may include a component configured to: determine a first combiner for a time when at least one reference signal is received; estimate a difference between one or more first conditions and one or more second conditions of a channel toward a second device at a time when at least one activation signal and a reply signal are received based at least in part on partial channel information; and adjust the first combiner based at least in part on the estimated difference, wherein the determined combiner includes an adjusted combiner.

[0236] A processor, memory, and / or example algorithms (which may be encoded as instructions, programs, or code) may be provided as example means for providing or causing the execution of operations.

[0237] According to an example embodiment, a non-transitory computer-readable medium includes instructions stored thereon, which, when executed using at least one processor, cause the at least one processor to: cause at least one reference signal to be received from a second device associated with a session with a third device; determine a combiner, wherein the determined combiner is configured to read the third device; cause at least one activation signal and a reply signal to be received, wherein the at least one activation signal is received from the second device and wherein the reply signal is received from the third device; and apply the determined combiner to the at least one activation signal and the reply signal.

[0238] According to an example embodiment, a non-transitory computer-readable medium includes program instructions stored thereon for performing at least the following: causing at least one reference signal to be received from a second device associated with a session with a third device; determining a combiner, wherein the determined combiner is configured to read the third device; causing at least one activation signal and a reply signal to be received, wherein the at least one activation signal is received from the second device and wherein the reply signal is received from the third device; and applying the determined combiner to the at least one activation signal and the reply signal.

[0239] According to another example embodiment, a non-transitory program storage device readable by a machine may be provided, tangibly embodying instructions executable by the machine for performing operations, the operations comprising: causing at least one reference signal to be received from a second device associated with a session with a third device; determining a combiner, wherein the determined combiner is configured to read the third device; causing at least one activation signal and a reply signal to be received, wherein the at least one activation signal is received from the second device, wherein the reply signal is received from the third device; and applying the determined combiner to the at least one activation signal and the reply signal.

[0240] According to another example embodiment, a non-transitory computer-readable medium includes instructions that, when executed by a device, cause the device to perform at least the following: cause at least one reference signal to be received from a second device associated with a session with a third device; determine a combiner, wherein the determined combiner is configured to read the third device; cause at least one activation signal and a reply signal to be received, wherein the at least one activation signal is received from the second device and wherein the reply signal is received from the third device; and apply the determined combiner to the at least one activation signal and the reply signal.

[0241] A computer-implemented system comprises: at least one processor and at least one non-volatile memory, the at least one non-volatile memory storing instructions that, when executed by the at least one processor, cause the system to at least perform: cause at least one reference signal to be received from a second device associated with a session with a third device; determine a combiner, wherein the determined combiner is configured to read the third device; cause at least one activation signal and a reply signal to be received, wherein the at least one activation signal is received from the second device and the reply signal is received from the third device; and apply the determined combiner to the at least one activation signal and the reply signal.

[0242] A computer-implemented system comprising: a component for receiving at least one reference signal from a second device associated with a session with a third device; a component for determining a combiner, wherein the determined combiner is configured to read the third device; a component for causing reception of at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device and wherein the reply signal is received from the third device; and a component for applying the determined combiner to the at least one activation signal and the reply signal.

[0243] The term "non-transitory" as used herein is a limitation of the medium itself (ie, tangible, not a signal), not a limitation of data storage persistence (eg, RAM vs. ROM).

[0244] It should be understood that the foregoing description is illustrative only. Various alternatives and modifications may be designed by those skilled in the art. For example, the features described in the dependent claims may be combined with each other in any suitable (multiple) combination. In addition, the features in the above-mentioned different embodiments may be selectively combined into new embodiments. Therefore, this specification is intended to cover all such alternatives, modifications and variations that fall within the scope of the appended claims.

Claims

1. A method for communication, comprising: receiving, using the first device, at least one reference signal from the second device associated with a session with the third device; determining a combiner, wherein the determined combiner is configured to read the third device; receiving at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device, and wherein the reply signal is received from the third device; as well as The determined combiner is applied to the at least one activation signal and the reply signal.

2. The method according to claim 1, further comprising: One or more first conditions of a channel toward the second device are determined, wherein the combiner is determined based at least in part on the one or more first conditions.

3. The method of claim 2, wherein determining the combiner comprises: determining, based at least in part on the one or more first conditions and a reconstruction function, one or more second conditions of the channel toward the second device at a time when the at least one activation signal and the reply signal are received; as well as The combiner is determined based at least in part on the one or more second conditions.

4. The method according to claim 3, wherein the reconstruction function comprises at least one of the following: A linear interpolation function, or Robust Wiener filtering. The method of claim 3 , wherein the reconstruction function comprises a channel prediction task performed using a model.

6. The method of claim 2, wherein determining the combiner comprises: determining one or more second conditions of the channel toward the second device at one or more times earlier than or equal to a time when the at least one reference signal is received; estimating, using an autoregressive model, one or more third conditions of the channel toward the second device at a time when the at least one activation signal and the reply signal are received based at least in part on the one or more first conditions and the one or more second conditions; as well as The combiner is determined based at least in part on the estimated one or more third conditions of the channel toward the second device.

7. The method of claim 2, wherein determining the combiner comprises: determining one or more second conditions of the channel toward the second device at one or more times earlier than when the at least one activation signal and the reply signal are received; estimating one or more third conditions of the channel toward the second device at a time when the at least one activation signal and the reply signal are received based at least in part on the one or more second conditions; as well as The combiner is determined based at least in part on the one or more third conditions, wherein the determined combiner is configured to maximize a quality of reception of the reply signal.

8. The method of claim 2, wherein determining the combiner comprises: a first combiner determining a time for the at least one reference signal to be received; estimating, based at least in part on the partial channel information, differences between the one or more first conditions and one or more second conditions of the channel toward the second device at times when the at least one activation signal and the reply signal are received; as well as The first combiner is adjusted based at least in part on the estimated difference, wherein the determined combiner comprises the adjusted combiner.

9. An apparatus for communication, comprising means for: receiving at least one reference signal from a second device associated with a session with a third device; determining a combiner, wherein the determined combiner is configured to read the third device; receiving at least one activation signal and a reply signal, wherein the at least one activation signal is received from the second device, and wherein the reply signal is received from the third device; as well as The determined combiner is applied to the at least one activation signal and the reply signal.

10. The device of claim 9, wherein the device comprises a reader.

11. The device of claim 9 or 10, wherein the second device comprises an activator.

12. The apparatus according to any one of claims 9 to 11, wherein the third apparatus comprises an ambient IoT device.

13. The apparatus according to any one of claims 9 to 12, wherein the determined combiner is configured as at least one of the following: minimizing a contribution of at least one activation signal in a channel towards the second device, or The at least one activation signal in the channel toward the second device is isolated.

14. The apparatus according to any one of claims 9 to 13, wherein the component is further configured for: One or more first conditions of a channel toward the second device are determined, wherein the combiner is determined based at least in part on the one or more first conditions.

15. The apparatus of claim 14, wherein the one or more first conditions are determined at least in part based on the at least one reference signal.

16. The apparatus according to claim 14 or 15, wherein the one or more first conditions include at least one of the following: a channel response of the channel towards the second apparatus at a time when the at least one reference signal is received, a Doppler shift at a time when the at least one reference signal is received, a signal to interference plus noise ratio at a time when the at least one reference signal is received, or a variance of at least one estimate of the channel toward the second device.

17. The apparatus according to any one of claims 14 to 16, wherein the component is further configured to: Based at least in part on the one or more first conditions, determining when the at least one activation signal and the reply signal are received.

18. The apparatus of claim 17, wherein times at which the at least one activation signal and the reply signal are received are determined at least in part based on the at least one reference signal.

19. The apparatus of any one of claims 14 to 18, wherein the means configured to determine the combiner comprises means configured to: determining, based at least in part on the one or more first conditions and a reconstruction function, one or more second conditions of the channel toward the second device at a time when the at least one activation signal and the reply signal are received; and The combiner is determined based at least in part on the one or more second conditions.

20. The apparatus of claim 19, wherein the reconstruction function comprises at least one of the following: A linear interpolation function, or Robust Wiener filtering.

21. The apparatus of claim 19, wherein the reconstruction function comprises a channel prediction task performed using a model.

22. The apparatus of any one of claims 14 to 18, wherein the means configured to determine the combiner comprises means configured to: determining one or more second conditions of the channel toward the second device at one or more times earlier than or equal to a time when the at least one reference signal is received; estimating, using an autoregressive model, one or more third conditions of the channel toward the second device at a time when the at least one activation signal and the reply signal are received based at least in part on the one or more first conditions and the one or more second conditions; as well as The combiner is determined based at least in part on the estimated one or more third conditions of the channel toward the second device.

23. The apparatus of any one of claims 14 to 18, wherein the means configured to determine the combiner comprises means configured to: determining one or more second conditions of the channel toward the second device at one or more times earlier than when the at least one activation signal and the reply signal are received; estimating one or more third conditions of the channel toward the second device at a time when the at least one activation signal and the reply signal are received based at least in part on the one or more second conditions; as well as The combiner is determined based at least in part on the one or more third conditions, wherein the determined combiner is configured to maximize a quality of reception of the reply signal.

24. The apparatus of claim 23, wherein the ratio between the reply signal and the at least one activation signal comprises: A signal-to-interference ratio associated with the third device.

25. The apparatus of any one of claims 14 to 18, wherein the means configured to determine the combiner comprises means configured to: a first combiner determining a time for the at least one reference signal to be received; estimating, based at least in part on the partial channel information, differences between the one or more first conditions and one or more second conditions of the channel toward the second device at times when the at least one activation signal and the reply signal are received; as well as The first combiner is adjusted based at least in part on the estimated difference, wherein the determined combiner comprises the adjusted combiner.

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