Integrated Sensing and Communications.md · GitHub
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- ISAC is expected to improve spectral and energy efficiencies, while reducing both hardware and signaling costs
- It attempts to merge sensing and communication into a single system, which previously competed over various types of resources
- ISAC dates back to the 1960s
- Radar's development has been profoundly affected by wireless communications, and vice versa
- Since its birth in the first half of the 20th century, radar systems have been deployed worldwide, carrying out various sensing tasks, such as geophysical monitoring, air traffic control, weather observation, and surveillance for defence and security
- The termRADAR was first used by the US Navy as an acronym for "RAdio Detection and Ranging" in 1939
- During the two World Wars, radar was independently and secretly created by different nations, and was soon put into use in the war to provide early warning of incoming threats
- Led by mechanical motors, a classical rotary radar searches for targets in the space via periodically rotating its antenna(s).
- Such radars face several critical challenges, e.g., the lack of multi-functionality and flexibility, as well as being relatively easy to jam and interfer.
- In view of this, the phased-array technique, a.k.a. the electronically-scanned array technique, was born at the right moment
- Instead of mechanically rotating its antennas, phased arrays generate spatial beams of signals that can be electronically steered to different directions
- Recent ISAC-related industrial activities and research efforts fill the gap between academia and industry communities
- Seven potential ISAC application scenarios followed by several key use cases for each
- Sensing as a Service
- Integrated sensing into current IoT devices and cellular networks
- Enhanced localization and tracking
- Area imaging
- RF imaging technology generates highresolution, day-and-night, and weather-independent images for a multitude of applications
- S&C Performance Metrics
- Detection: Detection refers to making binary/multiple decisions on the state of a sensed object, given the noisy and/or interfered observations. Detection metrics include detection probability, false-alarm probability, MSE, and CRB.
- Estimation: Estimation refers to extracting useful parameters of the sensed object from the noisy/interferentially interfered observations, and may include estimating distance/velocity/angle/quantity/size of target(s).
- Recognition: Recognition refers to understanding what is sensed based on the noisy or interfered observations and is typically defined as a classification task on the application layer, whose performance is evaluated by recognition accuracy. For higher-layer applications, recognition accuracy is at the core of learning based schemes.
- When wireless resources are shared between S&C functionalities, their integration into a common infrastructure allows the design of scapable tradeoffs between, often contradictory, sensing and communication objectives and metrics.
- Detection vs. Communication: PHY tradeoffs can be analyzed by investigating the relationship between the native performance metrics of S &C, which follows exactly the spirit of the information theoretical framework.
- (a) = joint passive sensing and communication
- (b) Joint active sensing and multi-user communication
- While the assumption of non-overlapping resources makes the analysis more tractable, this results in low efficiency
- To this end, the authors of [112] consider optimizing estimation performance of an ISAC system via the use of a common waveform, where the temporal, spectral, power, and signaling resources are fully reused for S&C, thus to achieve the maximum integration gain
Resolution of a Radar is Determined by Its Physical Limit, or the Maximum Available Amount of Temporal, Spectral, and Spatial Resources
- The capacity in (19) trades off with the fundamental communication metrics as it is specifically restricted to the identifiability of targets.
- Inspired by classical rate distortion theory^5, the authors of [119] [120] proposed the "estimation rate" as a sensing metric.
- Non-Overlapped Resource Allocation
- S&C can be scheduled on orthogonal/non-overlapped wireless resources, such that they do not interfere with each other
- Time-Division ISAC: Time-division ISAC is the most loosely coupled waveform design, which can be conveniently implemented into the existing commercial systems
- Fully Unified Waveform: Fully unified ISAC waveforms are generally designed following three philosophies
- Sensing-Centric Design: SCD aims to incorporate the communication functionality into existing sensing waveforms/infrastructures
- Communication-centric Design: CCD aims to provide insight into the performance of the waveform
- Waveform Design: The transmission duration is split into radar cycle and radio cycle cycles
- Spatial-division: spatial-division is a simple option which is typically constructed on the basis of an OFDM waveform, but is not as flexible as time-division
- Communication-centric design is to implement the sensing functionality over an existing communication waveform/system, i.e., communication is the primary functionality to be guaranteed.
- The randomness brought by the communication data may considerably degrade the sensing performance.
- While SCD and CCD schemes realize ISAC to a certain extent, they fail to formulate a scalable tradeoff between S&C.
- JD aims at conceiving an ISAC waveform from the ground-up, instead of relying on existing sensing and communication waveforms. This offers extra DoFs and flexibility.
- In addition to bearing the information matrixSC, the ISAC signalX should possess a number of aforementioned features that are favorable for sensing, which would be quite challenging to be implemented simultaneously in a single waveform.
- An alternative option is to approximate a well-designed pure sensing waveform X 0, e.g., orthogonal chirp waveform which is known to have superior sensing performance.
- An ISAC receiver should be able to decode useful information from the communication signal and at the same time detect/estimate targets from the echoes.
- In the event that the two signals do not overlap, conventional signal processing can be applied unalteredly, as both S&C are interference free.
- It is a type of receiver where the radar interference is pre-canceled before communication symbols are decoded.
- The authors analyze in detail the SER for commonly-employed constellations, including PAM, QAM, and PSK.
- In the regime of mid/high radar INR, one may consider to recover/estimate the radar interference first and pre-cancel it from the mixed receptiony
- A good ISAC receiver design should exploit the structural information of the S&C signals
- Ideal constellation is with concentric hexagon shape for low-INR regime and is with unequally spaced PAM shape for high-inR counterpart
- The sensing functionality is expected to be integrated into the future wireless network to form a perceptive network
- Communication can assist sensing with the following two levels of design methodologies
- Frame-Level ISAC: Sensing supported by default communication frame structures and protocols, such as Wi-Fi 7 and 5G NR^6
- Network-Level IsAC: Distributed/Networked sensing supported by state-of-the-art wireless network architectures
- In a PMN, sensing can be performed by using downlink or uplink signals, which are transmitted from a BS or a UE, respectively
- Downlink Mono-static Sensing: Downlink signals transmitted from the BS to the UE are exploited for sensing, while the BS receives the echo signals reflected from targets by its own receiver
- Uplink Mono-Static Sensing
- Uplink Bi-static Sensor: Uplink signals are used for sensing which hits the target(s) and is reflected to another BS or UE
- Networked sensing has been well-studied for a variety of sensing systems including wireless sensor network (WSN), multi-static radar, and distributed MIMO radar
- A typical C-RAN consists of a pool of base band units (BBUs), a large number of remote radio heads (RRHs), and a fronthaul network that connects RRHs to BBUs
- The sensing operation is performed by multiple sensing nodes, and the sensed results are collected by a centralized unit for further processing, e.g., data fusion
- Information-level fusion: Each sensing node performs individual sensing by its own observations
- Signal-Level fusion: The signals, instead of the sensed parameters, are collected and fused at the centralized unit
- Target Return as an Outlier in C-RAN Scheduling: On top of the interference management, sensing operations also impose challenges in resource scheduling in a PMN.
- Network Synchronization: A more critical challenge happens in the sensing scenario between multiple UEs and RRHs.
- UEs are unlikely to be clocksynchronized due to the wireless channel in between. This leads to severe phase noise in terms of timing offset (TO) and carrier frequency offset (CFO) between the sensing transmitter and receiver.
- Communication systems are often assisted by sensing in a general setting
- For example, estimation of CSI before data transmission by sending pilots from transmitter to receiver
- Spectrum sensing in the context of cognitive radio
- In mmWave communication systems, a communication link is configured via classical beam training protocols
- To guarantee the communication QoS for latency-critical applications such as V2X networks, the beam training overhead needs to be reduced to the minimum
- Once the communication link is established, i.e., the initial access is accomplished by beam training, both the transmitter and receiver are required to keep tracking the variation of the optimal beam pairs for the purpose of preserving the communication quality
- Beam tracking schemes exploit the temporal correlation between adjacent signal blocks
- By doing so, the search space of the beams can be maintained to a small interval centered around the previous beam, thus avoiding the transmission of redundant pilots
- The S&C coordination gain is achieved by reducing the training overheads, but at the cost of extra radar hardware
- T =P(ˆxn− 1 ), (72) where T stands for thenth predicted state based on the(n−1)th estimate, andP(·) is a predictor
- Can be designed either through model-based or model-free methods
- Model-based prediction typically relies on the vehicle’s kinetic model
- Freezer approaches can be built upon machine learning frameworks
- Once the ISAC signal hits the vehicle, it will be partially recived by the vehicle's receiver, and will also be partially reflected back to the RSU.
ISAC Based Beam Tracking /prediction Schemes Outperform the Communication-only Protocols in the Following Aspects:
- No downlink pilots are needed: The entire ISAC signal block is exploited for both V2I communication and vehicle sensing where dedicated pilots are no longer needed. This reduces downlink overheads, while at the same time improving radar estimation performance.
- Significant matched-filtering gain: The matched filtering gain that spans the whole communication block is much more significant than that of the feedback based scheme, where only a limited number of pilots are used for beam tracking. As a result, the estimation accuracy is improved.
- No uplink feedbacks are needed. The uplinkfeedback signal is replaced with the echo signal reflected by the vehicle, which reduces the uplink overheads.
- ISAC Meets Edge Intelligence: edge intelligence pushes the computationintensive artificial intelligence (AI) tasks from the centralized cloud to distributed BSs at the wireless network edge, in order to efficiently utilize the massive data generated at a large number of edge devices
- Integration between ISAC-and-edge-intelligence integration introduces more complicated tradeoffs among sensing, communication, and computation
- The demand of higher sensing accuracy and resolution in ISAC may lead to more data to be processed, which thus induce higher communication and computational burden
- To deal with such tradeoffs, a joint design over the sensing-communicationcomputation flow is crucial, in the ultimate goals of wireless networks.