ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication

Recent advances in wireless communication with the enormous demands of sensing ability have given rise to the integrated sensing and communication (ISAC) technology, among which passive sensing plays an important role. The main challenge of passive sensing is how to achieve high sensing performance in the condition of communication demodulation errors. In this paper, we propose an ISAC network (ISAC-NET) that combines passive sensing with communication signal detection by using model-driven deep learning (DL). Dissimilar to existing passive sensing algorithms that first demodulate the transmitted symbols and then obtain passive sensing results from the demodulated symbols, ISAC-NET obtains passive sensing results and communication demodulated symbols simultaneously. Different from the data-driven DL method, we adopt the block-by-block signal processing method that divides the ISAC-NET into the passive sensing module, signal detection module and channel reconstruction module. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for passive sensing and communication in wireless communications.Comment: 29 pages, 11 figure

Walking Speed Detection from 5G prototype System

While most RF-sensing approaches proposed in the literature rely on short-distance indoor point-to-point instrumentation, actual large-scale installation of RF sensing suggests the use of ubiquitously available cellular systems. In particular, the 5th generation of the wireless communication standard (5G) is envisioned as a universal communication means also for Internet of Things devices. This thesis presents an investigation of device-free environmental perception capabilities in a 5G prototype system in two cases; walking speed and human presence detection, and elaborate a comparison with the former case and acceleration sensing analysis. This thesis attempts to analyze the perception capabilities of 5G system in order to recognize human mostly common activities and presence detection near transceiver devices which the instrumentation exploits a device-free system capable of detect activities without carrying devices capitalizing on environmental RF-noise. This is done via the study of existing and related literature. After that, the implementation and evaluation of walking speed and presence detection is described in details. In addition, evaluation consists of utilizing a prototypical 5G system with 52 OFDM carriers over 12.48 MHz bandwidth at 3.45 GHz, which we consider the impact of the number and choice of channels and compare the recognition performance with acceleration-based sensing. It was concluded that in realistic settings with five subjects, accurate recognition of activities and environmental situations can be a reliable implicit service of future 5G installations

Channel estimation and beam training with machine learning applications for millimetre-wave communication systems

The fifth generation (5G) wireless system will extend the capabilities of the fourth generation (4G) standards to serve more users and provide timely communication. To this end, the carriers of 5G systems will be able to operate at higher frequency bands, such as the millimetre-wave (mmWave) bands that span from 30 GHz to 300 GHz, to obtain greater bandwidths and higher data rates. As a result, the deployment of 5G networks is required to accommodate more antennas and offer pervasive coverage with controlled power consumption. The complexity of 5G systems introduces new challenges to traditional signal processing techniques. To address these challenges, a major step is to integrate machine learning (ML) algorithms into wireless communication systems. ML can learn patterns from datasets to achieve control and optimisation of complex radio frequency (RF) networks. This PhD thesis focuses on developing efficient channel estimation methods and beam training strategies with the application of ML algorithms for mmWave wireless systems. Firstly, the channel estimation and signal detection problem is investigated for orthogonal frequency-division multiplexing (OFDM) systems that operate at mmWave bands. A deep neural network (DNN)-based joint channel estimation and signal detection approach is proposed to achieve multi-user detection in a one-shot process for non-orthogonal multiple access (NOMA) systems. The DNN acts as the receiver, which can recover the transmitted data by learning the channel implicitly from suitable training. The proposed approach can be adapted to work for both single-input and single-output (SISO) systems and multiple-output and multipleoutput (MIMO) systems. This DNN-based approach is shown to provide good performance for OFDM systems that suffer from severe inter-symbol interference or where small numbers of pilot symbols are used. Secondly, the beam training and tracking problem is studied for mmWave channels with receiver mobility. To reduce the signalling overhead caused by frequent beam training, a lowcomplexity beam training strategy is proposed for mobile mmWave channels, which searches a set of selected beams obtained based on the recent beam search results. By searching only the adjacent beams to the one recently used, the proposed beam training strategy can reduce the beam training delay significantly while maintaining high transmission rates. The proposed strategy works effectively for channel datasets generated using either the stochastic or the raytracing channel model. This strategy is shown to approach the performance for an exhaustive beam search while saving up to 92% on the required beam training overhead. Thirdly, the proposed low-complexity beam training strategy is enhanced with the use of deep reinforcement learning (DRL) for mobile mmWave channels. A DRL-based beam training algorithm is proposed, which can intelligently switch between different beam training methods such that the average beam training overhead is minimised while achieving good spectral efficiency or energy efficiency performance. Given the desired performance requirement in the reward function for the DRL model, the spectral efficiency or energy efficiency can be maximised for the current channel condition by controlling the number of activated RF chains. The DRL-based approach can adjust the amount of beam training overhead required according to the dynamics of the environment. This approach can provide a good overhead-performance trade-off and achieve higher data rates in channels with significant levels of signal blockage

Multiple Parallel Concatenated Gallager Codes and Their Applications

Due to the increasing demand of high data rate of modern wireless communications, there is a significant interest in error control coding. It now plays a significant role in digital communication systems in order to overcome the weaknesses in communication channels. This thesis presents a comprehensive investigation of a class of error control codes known as Multiple Parallel Concatenated Gallager Codes (MPCGCs) obtained by the parallel concatenation of well-designed LDPC codes. MPCGCs are constructed by breaking a long and high complexity of conventional single LDPC code into three or four smaller and lower complexity LDPC codes. This design of MPCGCs is simplified as the option of selecting the component codes completely at random based on a single parameter of Mean Column Weight (MCW). MPCGCs offer flexibility and scope for improving coding performance in theoretical and practical implementation. The performance of MPCGCs is explored by evaluating these codes for both AWGN and flat Rayleigh fading channels and investigating the puncturing of these codes by a proposed novel and efficient puncturing methods for improving the coding performance. Another investigating in the deployment of MPCGCs by enhancing the performance of WiMAX system. The bit error performances are compared and the results confirm that the proposed MPCGCs-WiMAX based IEEE 802.16 standard physical layer system provides better gain compared to the single conventional LDPC-WiMAX system. The incorporation of Quasi-Cyclic QC-LDPC codes in the MPCGC structure (called QC-MPCGC) is shown to improve the overall BER performance of MPCGCs with reduced overall decoding complexity and improved flexibility by using Layered belief propagation decoding instead of the sum-product algorithm (SPA). A proposed MIMO-MPCGC structure with both a 2X2 MIMO and 2X4 MIMO configurations is developed in this thesis and shown to improve the BER performance over fading channels over the conventional LDPC structure

Secondary spectrum usage in TV white space

Currently, the use of TV frequencies is exclusively license based with the area not covered by licensed TV transmitters being known as TV white space. In TV white space, the spectrum can be reused by a secondary user. This thesis studies how the TV white space can be used by a cellular system. The study addresses the problems of how the access to the spectrum is arranged, how the spectrum usage is constrained and how much capacity a secondary system will have. The access to TV white space can be arranged by using spectrum sensing or a geolocation database. This spectrum sensing relies on the performance of the signal detection algorithm. The detector has to operate in a fading environment where it should identify very low signal levels. In this thesis, the detector performance in a slow and fast fading environment is modeled. The model indicates that for a sufficiently long measurement time the impact of the fast fading can be averaged out. Unfortunately, simple single antenna-based detectors are not able to operate at a low enough signal-to-noise level. We propose a novel multi antenna-based detection algorithm that is specially designed to operate in a fading environment. TV white space is characterized by the amount of spectrum available for secondary usage. Because of the signal detection errors, a system using the sensing-based access is not able to use the entire available spectrum. This dissertation provides a method for estimating the spectrum utilization efficiency. The method illustrates how the detection error level affects the amount of available spectrum. One of the central questions studied in this thesis is how to describe the interference generated by the secondary transmitters. In the conventional model, the interference is computed as the sum of the interfering powers from individual transmitters. An alternative approach, pursued here, is to characterize the transmitter by its transmission power density per area. With such a model, the interference computation is done by integrating over the secondary system deployment area. The proposed method simplifies the interference estimation process. In data communication systems the spectrum attractiveness depends on the data rate the system can provide. Within the scope of this work, the achievable data rate is computed for a cellular system. Such computation is described as an optimization problem. The solution to this problem is found by searching for the optimal power allocation among the cochannels and the adjacent channels of a nearby TV transmitter

Proceedings of the 9th MIT/ONR workshop on C3 Systems, held at Naval Postgraduate School and Hilton Inn Resort Hotel, Monterey, California June 2 through June 5, 1986

GRSN 627729"December 1986."Includes bibliographical references and index.Sponsored by Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, Cambridge, Mass., with support from the Office of Naval Research. ONR/N00014-77-C-0532(NR041-519) Sponsored in cooperation with IEEE Control Systems Society, Technical Committee on C.edited by Michael Athans, Alexander H. Levis