Cell-free massive multiple-input-multiple-output (CF-mMIMO) network and its low-complexity user-centric (UC) alternative rely on accurate and up-to-date channel state information (CSI) for combining and/or precoding a large number of signals received by the distributed antenna array to achieve their anticipated performance gains. When serving highly mobile Internet of Things (IoT) devices, limited Line-of-Sight (LoS) information and nonnegligible channel aging (CA) effects will undermine CSI acquisition and inevitably degrade system performance. As a result, fundamental limit assessment and performance degradation mitigation under imperfect CSI are important for practical system designs. In this article, we focus on the performance of UC CF-mMIMO IoT systems in the interaction of sufficient and insufficient LoS knowledge, nonisotropic Non-LoS components, and heterogeneous CA effects. The novel and exact closed-form expressions for uplink spectral efficiency (SE) under the impaired CSI are derived. Numerical results verify the correctness of the SE expressions and reveal that the system parameters, such as resource block length and pilot overhead, should be optimally preconfigured according to environmental information and transmission tasks to alleviate the performance degradation caused by the imperfect CSI. Finally, a UC soft handover scheme is designed to enhance the mobility support of CF-mMIMO IoT systems in practical implementation.

We investigate the effect of spatially and temporally correlated channels on the transmission performance of multicell multiuser massive multiple-input–multiple-output (MIMO) vehicular networks in generic nonisotropic scattering environments. A new channel model is established to evaluate the harmfulness of the nonisotropic-scattered Angle-of-Departure/Angle-of-Arrival (AoD/AoA) spread and the high mobility of users on the uplink transmission. We derive the expressions of achievable spectral efficiency (SE), taking into account the effects of Line-of-Sight propagation, channel aging, and pilot contamination. Specifically, two novel receive combining schemes, namely, the aging-aware maximum ratio combining and the aging-aware minimum-mean-square error combining, are presented to mitigate the SE decline caused by outdated channel state information. A low-complexity pilot assignment algorithm is proposed to suppress pilot contamination. We find that the quasi-static assumption of the channel may be unsafe for the system design of the vehicular networks even within a single transmission block period lasting from hundreds of microseconds to a few milliseconds. We observe that there exists an optimal block size$C_{\mathrm {opt}}$that maximizes area SE. Especially,$C_{\mathrm {opt}}$can be expressed as a function of movement speed, AoD spread, and AoA spread. Numerical results are presented to validate the efficacy of the proposed schemes and highlight the importance of correct performance evaluation for practical massive MIMO system designs.

Orthogonal time frequency space (OTFS) modulation has received extensive attention due to its good characteristics against delay and Doppler. However, the Doppler considered by OTFS is constant and the actual Doppler is time-varying. This paper investigates the performance of OTFS modulation under time-varying Doppler channels. First, a time-varying Doppler system model is given. Then the basic principle of OTFS is demonstrated. Finally, the performance of the OTFS system under some typical time-varying Doppler channels is given, which indicates that the time-varying Doppler has a certain impact on bit error performance, especially in high-frequency scenarios, such as Ka-band and E-band.

Cell-free (CF) massive multiple-input multiple-output network and its low-complexity user-centric (UC) alternative rely on accurate channel knowledge for combining and precoding to achieve their claimed performance gains. When serving vehicular users with high mobility, the non-negligible channel aging effect will inevitably degrade system performance, and accurate performance evaluation is essential to system design. In this paper, we investigate the aging uplink (UL) spectral efficiency (SE) in the UC CF networks with non-isotropic scattering conditions. We adopt the von Mises distribution to model the angle-of-departure (AoD), resulting in an analytically tractable channel autocorrelation function that allows us to analyze the time-varying properties of the channel for an arbitrary AoD spread and the user’s moving direction. We derive a closed-form signal-to-interference-and-noise ratio expression with large-scale fading decoding (LSFD) for an achievable UL SE. The simulated results in a 3GPP-recommended vehicular network scenario show that there is an optimal subframe length to achieve the maximum area average SE. The LSFD cooperative strategy of the UC CF network significantly increases the optimal subframe length in the non-isotropic scattering environment, which reduces the pilot overhead and improves the spectrum utilization eventually.

Sparse code multiple access (SCMA) technology has been widely studied thanks to its outstanding overload performance, which provides greater device access on limited time-frequency resources. We propose a variational learning based end-to-end SCMA network model termed as V-SCMA. In our model, SCMA codebooks are generated by a deep neural network (DNN) encoder. Motivated by the idea of variational learning, we derive the posterior probability of SCMA codeword send by each user from the point view of binary coding using the variational method. Then, we design an SCMA decoding network to learn this process of approaching the real posterior probability. The SCMA decoding network designed by us is a truncated recurrent neural network, which is simpler than other networks. In addition, a novel loss function is proposed to optimize V-SCMA. Compared with previous works, the new loss function is a tighter variational lower bound which improves the BER performance. Simulation results show that the proposed V-SCMA offers a better bit error rate (BER) performance and a lower computational complexity than conventional schemes.

Massive multiple-input multiple-output (MIMO) relies on accurate channel estimation for precoding and receiving to achieve its claimed performance advantages. When serving vehicular users, the rapid channel aging effect greatly hinders its advantages, and a careful system design is required to ensure an efficient use of wireless resources. In this paper, we investigate this problem for the first time in a non-isotropic scattering scenario. The von Mises distribution is adopted for the angle of arrival (AoA), resulting in a tunable channel temporal correlation coefficient (TCC) model, which can adapt to different AoA spread conditions through the k parameter and incorporates the isotropic Jakes-Clarke model as a special case. The simulated results in a Manhattan grid-type multi-cell network clearly demonstrate the impact of channel aging on the uplink spectral efficiency (SE) performance and moreover, in order to maximize the area average SE, the size of the transmission block should be optimally selected according to some linear equations of k.

In this paper, a new class of online fountain codes is proposed to provide high intermediate symbol recovery rates (ISRRs) with low overhead. The encoder consists of two phases. In the first phase, a zigzag decodable online fountain (ZDOF) is proposed to provide a high ISRR. We utilize the characteristics of zigzag decodable codes so that the online fountain codes start decoding in the first phase without having to wait for the receipt of symbols of degree one. In the second phase, the buffer decoding method (BDM) is proposed to utilize a buffer to hold discarded symbols so as to generate useful symbols and improve the decoding performance. In addition, we provide a theoretical analysis based on random graph theory to analyze the performance of the proposed scheme. The theoretical analysis and simulation results show that the proposed scheme outperforms conventional online fountain codes, and that the buffer size does not have to be large.

In overlay heterogeneous wireless networks, many mobile terminals may operate vertical handover simultaneously which is defined as Group Vertical Handover (GVHO). Conventionally, handover decision schemes designed for a single user may cause network congestion and increasing handover blocking probability in GVHO scenario. To provide seamless handover with QoS guarantee, a fuzzy clustering method (FCM) is proposed by combining the concurrent traffic attributes of GVHO users and the status of available candidate networks. After the combination, users are able to select the proper network based on the dynamic diagram, which is generated by FCM. The collision probability can also be decreased through allocating handover request properly in a specific time window. Compared to the other two schemes, the simulation results show that the proposed scheme reduces the handover blocking probability about 17% and 25% (the maximum rate) respectively.

With the increase of science project in size and complexity, it is necessary to integrate and share global research resources for distributed experimentation and simulation. This paper presents an architecture of Cooperative Remote Laboratories (CRLab) with scalability, flexibility and heterogeneity. It consists of a central Web server, simulation servers, instrument servers and experiment devices, which provide four logical functions including Parallel Computing, Virtual Instrument, Collaborative Simulation and Web 2.0. Based on these functions with interactive online features, the research can be more efficient and convenient just via a simple Web browser.

Given that the conventional Selected Mapping (SLM) scheme needs to multiply pseudo-random phase sequences to the transmitted data meanwhile transmitting the whole side information, an exhaustive entropy and chaotic phase sequence based SLM method was proposed for reduction the peak-to-average power ratio (PAPR) of Orthogonal frequency division multiplexing (OFDM) in the paper. In the novel algorithm, exhaustive entropy was used to evaluate the randomness of phase sequence, chaotic sequences to reduce the large side information and improved Lorenz sequence digitalization method to enlarge sequence entropy, expanding the number of the candidate phase sequences vector space with the lower transmitted parameters of phase sequence. Simulation results show that different quantization methods of chaotic sequence have different exhaustive entropy and PAPR values. Furthermore, compared with the traditional SLM method, the new scheme proposed in the paper has better performance in reducing PAPR of OFDM system by about 0.1-0.5dB.

A fair and adaptive call admission control algorithm for multimedia low Earth orbit (LEO) satellite networks was proposed. Based on current call dropping probability of destination cell, this algorithm reserves bandwidth for handoff calls using double threshold method. To avoid the discrimination of quality of service (QoS) caused by the allocation based on fair bandwidth, this algorithm adopts the bandwidth allocation rule based on fair QoS. Simulation results show that the proposed algorithm can accurately and adaptively reserve bandwidth, present satisfactory call blocking probability and greatly reduce handoff call dropping probability, while guarantees the high bandwidth utilization.