Closed-form expressions for spatial correlation parameters for performance analysis of fluid antenna systems

The emerging fluid antenna technology enables a high-density positionswitchable antenna in a small space to obtain enormous performance gains for wireless communications. To understand the theoretical performance of fluid antenna systems, it is important to account for the strong spatial correlation over the different positions (referred to as ‘ports’). Previous works used a classical, generalised correlation model to characterise the channel correlation among the ports but were limited by the lack of degree of freedom of the model to imitate the correlation structures in an actual antenna. In this letter, it is proposed to use a common correlation parameter and to choose it by setting the correlation coefficient of any two ports to be the same as the average correlation coefficient of an actual fluid antenna taking up a linear space. A closedform expression for the spatial correlation parameter is first derived assuming that the number of ports is large, and it is illustrated that the correlation parameter depends only on the size of the fluid antenna but not the port density. Simpler expressions are then obtained for small and large sizes of fluid antenna. The resulting model is finally used to study the performance of fluid antenna systems. Simulation results based on the proposed model are provided to confirm the promising performance of fluid antenna in single and multiuser environments

Generalized Space-Time Super-Modulation for Headerless Grant-Free Rateless Multiple Access

This work introduces Generalized Space-Time Super-Modulation (GSTSM), a generalization of the recently proposed Space-Time Super-Modulation scheme that enables the transmission of additional, highly-reliable information on the top of conventionally transmitted symbols, without increasing the corresponding packet length. GSTSM jointly exploits the spatial and temporal dimensions of multiple-antenna systems but, in contrast to the initially proposed approach, it does not require the use of space-time block codes. Instead, GSTSM jointly elaborates on the concepts of spatial modulation and spatial diversity, while intentionally introducing temporal correlation to the transmitted symbol sequence. In the context of machine-type communications, GSTSM enables one-shot and grant-free medium access without transmitting additional headers to convey each machine’s ID. As a result, we show that GSTSM can provide throughput gains of up to 2.5 X compared to conventional header-based schemes, even in the case of colliding packets

Lifetime Maximization of an Internet of Things (IoT) Network based on Graph Signal Processing

The lifetime of an Internet of Things (IoT) system consisting of battery-powered devices can be increased by minimizing the number of transmissions per device while not excessively deteriorating the correctness of the overall IoT monitoring. We propose a graph signal processing based algorithm for partitioning the sensor nodes into disjoint sampling sets. The sets can be sampled on a round-robin basis and each one contains enough information to reconstruct the entire signal within an acceptable error bound. Simulations on different models of graphs, based on graph theory and on real-world applications, show that our proposal consistently outperforms state-of-the-art sampling schemes, with no additional computational burden

Collaborative Distributed Q-Learning for RACH Congestion Minimization in Cellular IoT Networks

Due to infrequent and massive concurrent access requests from the ever-increasing number of machine-type communication (MTC) devices, the existing contention-based random access (RA) protocols, such as slotted ALOHA, suffer from the severe problem of random access channel (RACH) congestion in emerging cellular IoT networks. To address this issue, we propose a novel collaborative distributed Q-learning mechanism for the resource-constrained MTC devices in order to enable them to find unique RA slots for their transmissions so that the number of possible collisions can be significantly reduced. In contrast to the independent Q-learning scheme, the proposed approach utilizes the congestion level of RA slots as the global cost during the learning process and thus can notably lower the learning time for the low-end MTC devices. Our results show that the proposed learning scheme can significantly minimize the RACH congestion in cellular IoT networks

Massive Machine-Type Communication (mMTC) Access with Integrated Authentication

We present a connection establishment protocol with integrated authentication, suited for Massive Machine-Type Communications (mMTC). The protocol is contention-based and its main feature is that a device contends with a unique signature that also enables the authentication of the device towards the network. The signatures are inspired by Bloom filters and are created based on the output of the MILENAGE authentication and encryption algorithm set, which is used in the authentication and security procedures in the LTE protocol family. We show that our method utilizes the system resources more efficiently, achieves lower latency of connection establishment for Poisson arrivals and allows a $87\%$ signalling overhead reduction. An important conclusion is that the mMTC traffic benefits profoundly from integration of security features into the connection establishment/access protocols, instead of addressing them post-hoc, which has been a common practice.Comment: Accepted for presentation at ICC 201

CPM Training Waveforms With Autocorrelation Sidelobes Close to Zero

Continuous phase modulation (CPM) plays an important role in wireless communications due to its constant envelope signal property and tight spectrum confinement capability. Although CPM has been studied for many years, CPM training waveforms having autocorrelations with zero sidelobes have not been reported before, to the best of our knowledge. Existing works on the CPM system design mostly assume that the channel fading coefficients are either perfectly known at the receiver or estimated using random CPM training waveforms. In this correspondence paper, we propose a novel class of CPM training waveforms displaying autocorrelation sidelobes close to zero. The key idea of our construction is to apply differential encoding to Golay complementary pair having perfect aperiodic autocorrelation sum properties

Low-Complexity Channel Estimation and Multi-User Detection for Uplink Grant-Free NOMA Systems

Grant-free non-orthogonal multiple access (NOMA) scheme is a promising candidate to accommodate massive connectivity with reduced signalling overhead for Internet of Things (IoT) services in massive machine-type communication (mMTC) networks. In this letter, we propose a low-complexity compressed sensing (CS) based sparsity adaptive block gradient pursuit (SA-BGP) algorithm in uplink grant-free NOMA systems. Our proposed SA-BGP algorithm is capable of jointly carrying out channel estimation (CE), user activity detection (UAD) and data detection (DD) without knowing the user sparsity level. By exploiting the inherent sparsity of transmission signal and gradient descend, our proposed method can enjoy a decent detection performance with substantial reduction of computational complexity. Simulation results demonstrate that the proposed method achieves a balanced trade-off between computational complexity and detection performance, rendering it a viable solution for future IoT applications

Predictive resource allocation for URLLC using empirical mode decomposition

Abstract. Empirical mode decomposition (EMD) based hybrid prediction methods can be an efficient way to allocate resources for ultra reliable low latency communication (URLLC). In this thesis, we have considered efficient resource allocation for the downlink channel at the presence of several interferers. Initially, we have generated desired signal that we need to transmit in downlink and total interference signal that will affect the desired signal transmission. Then, we used EMD to decompose the total interference signal power into intrinsic mode functions (IMFs) and residual. Due to the properties of EMD, decomposed IMFs become less random as IMF number increases. As a result of that property, prediction model training process become less complex and prediction accuracy also increases as randomness of signal decreases. Long short term memory (LSTM) deep neural network method and auto regressive integrated moving average (ARIMA) time series method are deployed to predict future interference power values based on historical values. For each decomposed component (IMFs and residual), two prediction models have been trained using LSTM and ARIMA methods. Finally, predicted components of IMFs and residual are added together to form total predicted interference power. According to the predicted interference power, resources are allocated for downlink transmission of the signal and evaluated it with the baseline estimation techniques. The research demonstrates that the suggested method achieves near optimal resource allocation for URLLC

A Survey on the 5th Generation of Mobile Communications: Scope, Technologies and Challenges

The 5th Generation (5G) of mobile communicationswill impact the costumers Quality of Experience (QoE) by ad-dressing the current mobile networks usage trends and providingthe technological foundation for new and emerging services.Additionally, 5G may provide a unified mobile communicationplatform, with multiple purposes, leveraging industries, servicesand economic sectors. In this paper, a 5G tutorial is presented,including the 5G drivers, main use cases, vertical markets anda current status of the standardization process. Furthermore,several 5G key enabling technologies are presented, concerningthe Radio Access Network (RAN) and Core Network (CN)perspectives. Finally, a brief outline over the Internet of Things(IoT) concept and current research topics is presented

Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems

We propose a novel deep energy autoencoder (EA) for noncoherent multicarrier multiuser single-input multipleoutput (MU-SIMO) systems under fading channels. In particular, a single-user noncoherent EA-based (NC-EA) system, based on the multicarrier SIMO framework, is first proposed, where both the transmitter and receiver are represented by deep neural networks (DNNs), known as the encoder and decoder of an EA. Unlike existing systems, the decoder of the NC-EA is fed only with the energy combined from all receive antennas, while its encoder outputs a real-valued vector whose elements stand for the subcarrier power levels. Using the NC-EA, we then develop two novel DNN structures for both uplink and downlink NC-EA multiple access (NC-EAMA) schemes, based on the multicarrier MUSIMO framework. Note that NC-EAMA allows multiple users to share the same sub-carriers, thus enables to achieve higher performance gains than noncoherent orthogonal counterparts. By properly training, the proposed NC-EA and NC-EAMA can efficiently recover the transmitted data without any channel state information estimation. Simulation results clearly show the superiority of our schemes in terms of reliability, flexibility and complexity over baseline schemes.Comment: Accepted, IEEE TW