Flexibly supporting multiple services, each with different communication requirements and frame structure, has been identified as one of the most significant and promising characteristics of next generation and beyond wireless communication systems. However, integrating multiple frame structures with different subcarrier spacing in one radio carrier may result in significant inter-service-band-interference (ISBI). In this paper, a framework for multi-service (MS) systems is established based on a subband filtered multi-carrier system. The subband filtering implementations and both asynchronous and generalized synchronous (GS) MS subband filtered multi-carrier (SFMC) systems have been proposed. Based on the GS-MS-SFMC system, the system model with ISBI is derived and a number of properties on ISBI are given. In addition, low-complexity ISBI cancelation algorithms are proposed by precoding the information symbols at the transmitter. For asynchronous MS-SFMC system in the presence of transceiver imperfections, including carrier frequency offset, timing offset, and phase noise, a complete analytical system model is established in terms of desired signal, inter-symbol-interference, inter-carrier-interference, ISBI, and noise. Thereafter, new channel equalization algorithms are proposed by considering the errors and imperfections. Numerical analysis shows that the analytical results match the simulation results, and the proposed ISBI cancelation and equalization algorithms can significantly improve the system performance in comparison with the existing algorithms.
Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.
Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.
It has been claimed that filter bank multicarrier (FBMC) systems suffer from negligible performance loss caused by moderate dispersive channels in the absence of guard time protection between symbols. However, a theoretical and systematic explanation/analysis for the statement is missing in the literature to date. In this paper, based on one-tap minimum mean square error (MMSE) and zero-forcing (ZF) channel equalizations, the impact of doubly dispersive channel on the performance of FBMC systems is analyzed in terms of mean square error of received symbols. Based on this analytical framework, we prove that the circular convolution property between symbols and the corresponding channel coefficients in the frequency domain holds loosely with a set of inaccuracies. To facilitate analysis, we first model the FBMC system in a vector/matrix form and derive the estimated symbols as a sum of desired signal, noise, intersymbol interference (ISI), intercarrier interference (ICI), interblock interference (IBI), and estimation bias in the MMSE equalizer. Those terms are derived one-by-one and expressed as a function of channel parameters. The numerical results reveal that under harsh channel conditions, e.g., with large Doppler spread or channel delay spread, the FBMC system performance may be severely deteriorated and error floor will occur.
The term space wave refers to a wave that is propagating through an open medium with a diverging wavefront as it moves away from the source. The term guided wave denotes a variety of wave configurations that are being carried by a closed/open/partially open structure [1]. A guided wave may also be known as a bounded wave, which is a term inspired by its propagation mechanism, where a traveling wave is bounded by two dissimilar mediums with different electromagnetic (EM) characteristics; the wave propagates along the interface and decays exponentially to the surrounding mediums.
Sparse code multiple access (SCMA) is a promising air interface candidate technique for next-generation mobile networks, especially for massive machine-type communications. In this paper, we design a low-density parity-check (LDPC) coded SCMA detector by combining the sparse graphs of LDPC and SCMA into one joint sparse graph (JSG). In our proposed scheme, the SCMA sparse graph defined by small size indicator matrix is utilized to construct the JSG, which is termed as sub-graph-based JSG of SCMA (SG-JSG-SCMA). In this paper, we first study the binary-LDPC coded SG-JSG-SCMA system. To combine the SCMA variable node and LDPC variable node into one joint variable node, a nonbinary LDPC (NB-LDPC) coded SG-JSG-SCMA is also proposed. Furthermore, to reduce the complexity of NB-LDPC coded SG-JSG-SCMA, a joint trellis representation (JTR) is introduced to represent the search space of NB-LDPC coded SG-JSG-SCMA. Based on JTR, a low complexity joint trellis-based detection and decoding algorithm is proposed to reduce the computational complexity of the NB-LDPC coded SG-JSG-SCMA system. According to the simulation results, SG-JSG-SCMA brings significant performance improvement compare with the conventional receiver using the disjoint approach, and it can also outperform a turbo-structured receiver with comparable complexity. Moreover, the joint approach also has advantages in terms of processing latency compare with the turbo approaches.
Toward 6G networks, such as virtual reality (VR) applications, Industry 4.0, and automated driving, demand mobile-edge computing (MEC) techniques to offload computing tasks to nearby servers, which, however, causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, enabling low latency and high throughput. For this sake, this article investigates the resource management for the offload transmission of the mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computing-oriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation, and power allocation is formulated as a multiobjective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using the $\epsilon $ -constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that the gap between Pareto front generated by the three-stage iterative resource allocation algorithm and the real Pareto front is less than 0.16%. Furthermore, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-II, while significantly outperforms the other traditional schemes.
A cell-free Massive multiple-input multiple-output (MIMO) system is considered, where the access points (APs) are linked to a central processing unit (CPU) via the limited-capacity fronthaul links. It is assumed that only the quantized version of the weighted signals are available at the CPU. The achievable rate of a limited fronthaul cell-free massive MIMO with local minimum mean square error (MMSE) detection is studied. We study the assumption of uncorrelated quantization distortion, which is commonly used in literature. We show that this assumption will not affect the validity of the insights obtained in our work. To investigate this, we compare the uplink per-user rate with different system parameters for two different scenarios; 1) the exact uplink per-user rate and 2) the uplink per-user rate while ignoring the correlation between the inputs of the quantizers. Finally, we present the conditions which imply that the quantization distortions across APs can be assumed to be uncorrelated.
Network slicing has been identified as one of the most important features for 5G and beyond to enable operators to utilize networks on an as-a-service basis and meet the wide range of use cases. In physical layer, the frequency and time resources are split into slices to cater for the services with individual optimal designs, resulting in services/slices having different baseband numerologies (e.g., subcarrier spacing) and / or radio frequency (RF) front-end configurations. In such a system, the multi-service signal multiplexing and isolation among the service/slices are critical for the Physical-Layer Network Slicing (PNS) since orthogonality is destroyed and significant inter-service/ slice-band-interference (ISBI) may be generated. In this paper, we first categorize four PNS cases according to the baseband and RF configurations among the slices. The system model is established by considering a low out of band emission (OoBE) waveform operating in the service/slice frequency band to mitigate the ISBI. The desired signal and interference for the two slices are derived. Consequently, one-tap channel equalization algorithms are proposed based on the derived model. The developed system models establish a framework for further interference analysis, ISBI cancelation algorithms, system design and parameter selection (e.g., guard band), to enable spectrum efficient network slicing.
Sparse code multiple access (SCMA) is an emerging paradigm for efficient enabling of massive connectivity in future machine-type communications (MTC). In this letter, we conceive the uplink transmissions of the low-density parity check (LDPC) coded SCMA system. Traditional receiver design of LDPC-SCMA system, which is based on message passing algorithm (MPA) for multiuser detection followed by individual LDPC decoding, may suffer from the drawback of the high complexity and large decoding latency, especially when the system has large codebook size and/or high overloading factor. To address this problem, we introduce a novel receiver design by applying the expectation propagation algorithm (EPA) to the joint detection and decoding (JDD) involving an aggregated factor graph of LDPC code and sparse codebooks. Our numerical results demonstrate the superiority of the proposed EPA based JDD receiver over the conventional Turbo receiver in terms of both significantly lower complexity and faster convergence rate without noticeable error rate performance degradation.
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