In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicRx), replacing the channel estimation, equalization and symbol demapping blocks. We leverage domain knowledge to design the input with spiking encoding and propose a deep convolutional SNN with spike-element-wise residual connections. We integrate an SNN with artificial neural network (ANN) hybrid architecture to obtain soft outputs and employ surrogate gradient descent for training. We focus on generalization across diverse scenarios and robustness through quantized aware training. We focus on interpretability of NeuromorphicRx for 5G-NR signals and perform detailed ablation study for 5G-NR signals. Our extensive numerical simulations show that NeuromorphicRx is capable of achieving significant block error rate performance gain compared to 5G-NR receivers and similar performance compared to its ANN-based counterparts with 7.6× less energy consumption.

Continuous-variable quantum key distribution (CV-QKD) maps information onto the quadrature components of electromagnetic waves, so that off-the-shelf wireless transceivers can be utilized. This motivates the move from optical to Terahertz (THz) bands. However, wireless THz channels suffer from severe path loss, while the mobility of wireless users imposes doubly selective fading. Against this background, we propose a new CV-QKD regime that relies on hybrid beamforming (HBF) assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) system, where the channel’s transmissivity and robustness against double selectivity are overcome by HBF and OTFS, respectively. Secondly, in order to provide channel state information (CSI) for both the transmitter (CSI-T) and receiver (CSI-R), practical channel estimation methods are conceived. They operate in the time-frequency domain for OFDM and in the delay-Doppler domain for OTFS. Thirdly, soft-decision detection is devised for our MIMO OFDM/OTFS aided multi-dimensional reconciliation (MDR) scheme. Low-density parity-check (LDPC) coding is invoked for further improving secure CV-QKD transmission distance in the THz band. Our simulation results demonstrate that the proposed HBF MIMO OTFS-based CV-QKD system relying on realistic estimated CSI is capable of achieving an adequate secret key rate (SKR) and secure transmission distance in hostile doubly selective THz channels.

Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for multi-antenna wireless networks. In this work, we study the performance of RSMA in downlink overloaded networks, where the number of transmit antennas is smaller than the number of users. RSMA has been investigated in overloaded networks in previous works by formulated optimization problems and their solutions by interior-point methods. This limits the practical use of such designs in practical systems. Our aim is to develop low-complexity precoding, rate, and power allocation techniques for RSMA for its use in practical overloaded networks. First, we provide analysis and closed-form expressions for optimal power and rate allocations considering max-min fairness when low-complexity precoders are employed. The derived closed-form solutions are used to propose a low-complexity RSMA system design for precoder selection and resource allocation for arbitrary number of users and antennas under perfect and imperfect Channel State Information at the Transmitter (CSIT). We compare the performance of the proposed design with benchmark designs based on Space Division Multiple Access (SDMA) with and without user scheduling. By numerical results, we show that the proposed low-complexity RSMA design achieves a significantly higher rate compared to the SDMA-based benchmark designs under perfect and imperfect CSIT.

Orthogonal time frequency space (OTFS) is a modulation technique which is robust against the disruptive effects of doubly-selective channels. In this paper, we perform an experimental study of OTFS by a real-time software defined radio (SDR) setup. Our SDR consists of a Graphical Processing Unit (GPU) for signal processing programmed using Sionna and TensorFlow, and Universal Software Radio Peripheral (USRP) devices for air interface. We implement a low-latency transceiver structure for OTFS and investigate its performance under various Doppler values. By comparing the performance of OTFS with Orthogonal Frequency Division Multiplexing (OFDM), we demonstrate that OTFS is highly robust against the disruptive effects of doubly-selective channels in a real-time experimental setup.

Wearable and home IoT applications require low-power connectivity to maximize battery life. Sensing a modulated electric field, referred to as capacitively coupled communication, is a promising alternative to communication via electromagnetic waves. However, achieving a reliable over-the-air communication through electric field remains a research challenge. We describe an efficient electric field based over-the-air communication system using frequency shift keying (FSK) which achieves arm’s length communication range with throughput suitable for real-time sensor streaming or even audio streaming. This is achieved through a highly sensitive electric potential receiver allowing communication when weakly coupled, such as between two devices far from any other object or human body. The digital FSK receiver uses an undersampling technique to reduce the complexity of the implementation. Another contribution of this work is the use of low-power ARM microcontroller to perform the modulation/demodulation, with a few additional off-the-shelf digital and analog components. We present a detailed performance analysis of the system when varying the transmit voltage and communication distance in five different scenarios/environments. The performance is analyzed and compared in terms of bit error rate (BER) and throughput. The system is capable of providing reliable communication link up to 1.2m with a user throughput of 75 kbps in the best case scenario, when placed over tarmac road. It is also shown that the system achieves a user throughput of at least 80 kbps for a distance of 70cm in the worst case scenario, when both the devices are hanged in air through strings.

In this contribution, an algorithm based on the space-alternating generalized expectation-maximization principle is proposed for estimating the locations of scatterers involved in the last-hops of propagation paths when a large-scale antenna array is used in a receiver for channel measurement. The underlying generic parametric model is constructed under the spherical wave-front assumption, which allows characterizing a path with a new parameter, i.e., the distance between the scatterer at the last-hop of the path and a specific receiving antenna, additional to the conventional parameters characterizing a specular path under the plane wave-front assumption. Cramér-Rao lower bounds of mean squared errors are derived for the parameter estimators in a single-path scenario, and their accuracy is evaluated through Monte Carlo simulations. The performance of the algorithm when being applied in reality is also evaluated through experiments conducted in an office with a carrier frequency of 9.5 GHz, a bandwidth of 500 MHz, and the receiver equipped with a 121-element virtual array. The proposed signal model and algorithm can be extended to the case of localizing the scatterers in the first- and last-hops of paths when large-scale antenna arrays are used in both the transmitter and the receiver.

When a person is performing repetitive daily activities (e.g. walking, cycling etc.) with equipped motion sensor such as accelerometer, its measured motion signals can go up or down and fluctuates in a periodical manner. In addition, recent researches indicate that the Received Signal Strength (RSS) through wireless channels between on-body sensors and the hub in a Wireless Body Area Network (WBAN) also shares a similar periodical pattern regards to the same body movement. Finding the connections of the two, can enable low-power tracking of body movement and infer corresponding channel changes to provide optimized MAC communications between sensor nodes and the hub. However, since nodes can be placed at different parts of the body experiencing different motion intensities and velocities, it is not straightforward to directly map the motion sensor measurements to the Channel RSS. In this paper, we proposed the notion of motion index which is an effective mechanism to achieve synchronization between the channel periodicity and the corresponding body motion. The proposed M-MAC protocol then reacts to such WBAN channel periodicity and gives high medium access priority to sensors with good channel condition hence increases communication reliability, while buffering data otherwise. We conducted experiments based on real-life on-body channel measurements, and our initial results yield promising results with 10% - 20% PDR increase and 3 dB transmitting power reduction compared to the conventional methods.

In this contribution, a recently conducted measurement campaign is introduced for investigating the characteristics of propagation channels for massive multiple-input multiple-output (MIMO) scenarios in a lecture hall environment. The channel responses for waves of higher frequency band ranging from 13 GHz to 17 GHz was measured with a vertically standing virtual two-dimensional (2-D) 20 × 20 = 400-element planar antenna array at the receiver (Rx) side, and an omni-directional antenna at the transmitter (Tx) side. Measurements were performed for four different locations of the Tx antenna in line-of-sight (LoS) scenarios. The variation of channel characteristics such as the narrowband channel gain, the K-factor and the composite delay spread, across the 2-D array aperture is investigated. The results are important for generating realistic channel realizations for the designing and performance evaluation of the algorithms for massive MIMO communication in the context of the fifth generation (5G) wireless networks.

The Non Line-of-Sight (NLoS) issue is considered as a key challenge to mature indoor localization technologies. Using Time of Arrival (TOA) on Ultra-wideband (UWB) signal is able to provide fine localization accuracy if NLoS-biased anchor nodes can be identified. In this paper, an antenna cluster selection scheme is proposed to identify the NLoS-biased node, therefore improve the localization accuracy and provide location-aware communication service. In particular, the area of interest with NLoS human body blockage is divided into multiple convex hull based effective localization areas (ELAs). Four antennas, from a distributed antenna system, are deployed in each ELA, where the most severely NLoS-biased antenna is identified and discarded, two other antennas are selected to execute a 2-dimension (2D) indoor localization. The remaining antenna uses this location information to provide location-aware communication service. Performance is evaluated using a channel sounder's measurement data and a simulator, benchmarked to the traditional parametric identification method.

Using Time of Arrival (TOA) on ultra-wideband (UWB) signal is well known as a promising indoor localization technique. However, Non Line-of-Sight (NLoS) issue resulting from complicated scenarios is considered as a key challenge to transfer this technology into mature products. In this paper, a convex hull based node selection method is proposed to improve the accuracy of NLoS identification and mitigation. In particular, two reference nodes are selected adaptively from four nodes to carry out a 2-dimension (2D) indoor localization in a convex hull based effective localization area (ELA). Performance is evaluated using measurement data collected from a Medav UWB SIMO channel sounder, benchmarked to the parametric identification method and the first peak detection method.

In this paper, we present the end-to-end performance analysis of a dual-hop amplify-and-forward (AF) relay system. In particular, the outage performance of a system which employs maximum ratio combining (MRC) and transmit antenna selection at the relay is analyzed. We consider systems which operate over heterogeneous channels, where the channels in the first and second hops can either follow a Rayleigh or Rician distribution. Closed-form expressions are derived for the outage probability in the high SNR regime. We further demonstrate through simulations that for a specific configuration, the Rice factor of the first channel does not have a significant impact on the end-to-end outage performance. We corroborate our derivations through simulation results.

We present a multi-mode power amplifier (PA) set with an antenna selection mechanism in a body area relaying network for healthcare applications. The PA set of the relay node is designed to work in two modes where the RF switch utilized for antenna selection is located between the highly efficient narrowband PAs and a low-efficiency wideband PA supporting two heterogeneous networks. Our study shows that, unlike in the wideband PA mode, where antenna selection is always desirable for transmission reliability, antenna selection is not always preferred in the narrowband PA mode since in this mode, the PA needs to turn on and off constantly. Consequently, extra switch delay and energy consumption occur. To balance the tradeoff between the relay node's energy efficiency and transmission reliability, both the switching probability between two modes and bounds on the probability of staying on the current antenna are analyzed. Numerical results are provided to corroborate the improved performance of the proposed architecture and the switch/stay mechanism.

We consider an opportunistic spectrum usage model facilitated with periodic spectrum sensing and handoff in a two-hop selective relay network. A novel sensing and fusion algorithm is proposed under a signalling bandwidth-constrained condition by introducing a quantized soft sensing and a test statistic restoration process. The reliability of secondary transmissions is studied, where we derive expressions for the probability of collision, and the throughput of secondary users. Numerical results manifest the advantages of the proposed sensing algorithm in selective relay networks in light of the missed detection probability, signalling cost, collision probability and throughput.

In this paper a switch-stay model is proposed for spectrum access by a secondary user in the presence of a primary user, which considers the impact of spectrum handoff delay. This model is then studied to balance the trade-off between the secondary user's energy efficiency and transmission reliability in light of the probability of collision, throughput and delay constraints. Bounds on the probability of stay on the current channel are derived from multiple constraints. An antenna selection sensing mechanism is then proposed to relax these bounds by enhancing the sensing performance. The advantages of the proposed antenna selection sensing are verified by numerical results.

This paper considers the spectrum management problem in a secondary cognitive radio network where secondary nodes in the network operate on a single channel. There is a leader node which is responsible for tasks such as primary detection, node coordination and channel scanning. Channels are sensed in the order of their detection probabilities. The leader node changes on each scan depending on the credit history generated using a fair credit based mechanism. It has been shown that the proposed scheme can achieve better energy efficiency in terms of overall low battery consumption, better fairness, and seamless switch over when migrating to another channel.

In a cognitive radio system, the accuracy of spectrum sensing is critical for both primary and secondary systems. Both cooperative sensing and single-user multiple-antenna techniques have been proposed in the literature to enhance the accuracy of spectrum sensing. However, these techniques may require additional signaling and hardware costs, and can lead to user reliability problems or energy consumption issues. To avoid these problems, we propose a scheme that requires no extra signaling and low hardware costs, while still offering substantial diversity gains for enhancing the sensing performance. This is achieved by combining antenna selection with the spectrum sensing mechanism. We analyze the diversity order of the proposed scheme and show that no loss occurs relative to the case of single-user multiple-antenna sensing. This benefit is achieved while keeping a low implementation complexity as only a subset of RF chains are used in a given time period. Simulations show that a substantial sensing gain can be obtained compared to using other traditional multiple-antenna techniques in some practical cases.

For the radar spectrum to be shared efficiently a good sensing capability within a secondary cognitive communication system is required. In this paper, the swept radar's rotation mechanism is explored to improve the sensing performance. Several node teaming algorithms are proposed for cooperative sensing along with the use of weighted sensing algorithms in a swept radar scenario. These teaming algorithms are considered in respect of the mobile team node selection and the sensing task assignments of the team nodes. Performance results show that selecting appropriate sensing nodes to join the sensing-active team in different sensing cycles and exploring their frequency diversity (to perform the sensing task at the most suitable frequency subchannels), yields a substantial improvement in performance. In addition, it is illustrated that proper node teaming algorithms should be chosen based on several key factors, including the characteristics of the primary signal and the sensing team node's computational capabilities.