In this paper, we develop an energy minimization framework for wireless-powered multi-access edge computing (WP-MEC) networks, where two zero-energy reconfigurable intelligent surfaces (zeRISs) are employed to support energy harvesting and task offloading. In the downlink energy harvesting period, the hybrid access point (HAP) provides energy beamforming for multiple zero-energy devices and two zeRISs, where the harvested energy is used to offload tasks in the uplink period. Specifically, an optimization problem is formulated to minimize the energy consumption at the HAP, jointly considering the nonlinear energy harvesting model and cascaded RIS link. Next, an efficient iterative solution is designed to realize joint time allocation, HAP energy beamforming, and reflection coefficients for two-RIS by employing alternating optimization and semidefinite relaxation methods. Numerical results demonstrate the superiority of the algorithm. Compared to the corresponding single RIS setup, the energy consumption of HAP under the proposed two-zeRIS scheme is significantly reduced.

Massive machine communication is predicted to provide widespread and unparalleled connectivity for cellular Internet of Things (IoT) applications via multiple-input multiple-output (MIMO) and grant-free random access (GF-RA) techniques. Compressed sensing (CS) has been widely advocated to support massive connectivity due to the bursty nature of traffic transmission. In this paper, the joint activity detection and channel estimation in MIMO-enabled GF-RA system is formulated as a block single measurement vector (SMV) problem and efficiently addressed by using Bayesian-based CS algorithms. First, the pattern coupled sparse Bayesian learning (PCSBL) and block sparse Bayesian learning (BSBL) algorithms are introduced to solve this problem, where the potential block sparsity properties induced by multi-antenna reception are exploited by assigning the structured hyperpriors. Then, by embedding the Generalized Approximate Message Passing (GAMP) technique into the PCSBL and BSBL frameworks to enable effective approximation of posterior distributions, we propose two computationally efficient Bayesian learning algorithms, i.e., GAMP-PCSBL and GAMP-BSBL. Fortunately, the proposed Bayesian algorithms allow automatic learning of block sparse solutions without requiring noise level and user sparsity ratio as explicit conditions. Simulation results show that the proposed algorithms provide improved performance gains over the standard CS-based methods.

Massive machine-type communications (mMTC) are anticipated to be supported by grant-free random access (GF-RA) and multiple-input multiple-output (MIMO) techniques. Compressed sensing (CS) is extensively advocated to accommodate massive connectivity due to bursty data transmission. In this paper, we formulate the joint activity detection and channel estimation for the MIMO-enabled GF-RA system as a block single measurement vector (SMV) problem. First, the hierarchical block sparse Bayesian learning (BSBL) framework is developed to solve this problem, where the potential block sparse properties induced by multiantenna reception are exploited by assigning the structured hyperprior. Then, by integrating the generalized approximate message passing (GAMP) approach into the BSBL formulation to effectively approximate the posterior distribution, we propose a computationally efficient Bayesian learning algorithm named GAMP-BSBL. Fortunately, the proposed Bayesian algorithms enable automatic learning of block sparse solutions without requiring noise level and user sparsity ratio as explicit conditions. Simulation results show that the proposed algorithms provide improved performance gains as compared with the standard CS methods.

Orthogonal Time-Frequency-Space (OTFS) modulation can effectively suppress the effects of Doppler shift in high-speed mobile scenarios. At the same time, the accuracy of OTFS channel estimation is an important factor that affects the performance of OTFS. In this paper, we propose a Sparse Bayesian Learning (SBL) algorithm to quickly and accurately estimate the OTFS channel by combining the pilot pattern and the sparsity of the OTFS channel. First, we propose a new pilot pattern to prevent the contamination of information symbols on pilot symbols. Since the pilot pattern uses only partial guard symbols, it can also improve the spectral efficiency. Then, we propose the Student-T prior SBL (STSBL) algorithm to improve the speed and accuracy of OTFS channel estimation by exploiting the sparsity of the OTFS channel. Simulation results show that the normalized mean squared error (NMSE), bit error rate (BER), and throughput of the proposed scheme outperform the benchmark schemes.

Grant-free non-orthogonal multiple access (GF-NOMA) based on multiple-input multiple-output (MIMO) has attracted much attention as a promising technique to support massive connectivity and bursty data transmission in massive machine-type communication. In this paper, we propose two compressed sensing based multiuser detection (MUD) algorithms for the MIMO-enabled GF-NOMA system. First, the spatially enhanced variational Bayesian inference (SE-VBI) algorithm is developed for MUD by exploiting the Gaussian mixture prior and diversity combining technique. Then, by applying the covariance-free (CoFe) strategy to the SE-VBI framework to estimate the diagonal elements of the posterior covariance, we propose a low-complexity MUD method named SE-CoFe-VBI. In particular, the proposed algorithms integrate the multivariate nature of the transmitted signal, i.e., discreteness, sparsity, and spatial correlation. Simulation results show that the proposed algorithms offer improved detection performance over the state-of-the-art spatially enhanced sparse Bayesian learning method.

Grant-free (GF) low-density signature orthogonal frequency division multiplexing (LDS-OFDM) is one of the most generic grant-free non-orthogonal multiple access (GF-NOMA) schemes. This paper proposes a two-stage active user detection (AUD) consisting of the initial AUD stage and the false alarm correction stage. In the initial AUD stage, an initial active user set is efficiently estimated by the conventional cover decoder, which may still contain a few false alarms. Hence, a false alarm correction stage is further invoked, which consists of two cooperative and iterative detection components, namely, message passing algorithm (MPA) based data decoder and belief propagation (BP) based false alarm corrector. Based on users’ signature spreading sequences in the initial active user set, a tanner graph with potential redundant-edges is constructed, on which MPA is executed for data decoding. In turn, with the aid of decoded data symbols, the remaining false alarms in the initial active user set are further removed by the false alarm corrector. Hence, the tanner graph could evolve iteratively. Furthermore, the false alarm performance of the initial AUD stage is theoretically analyzed. Based on this analysis, the LDS matrix used in GF LDS-OFDM is optimized. Finally, the complexity of our proposal is also provided.

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks and, thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are co-located with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This article applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency-division and nonorthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with the joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multivariable nonconvex problem, we split the target problem into several subproblems and provide a near-optimal low-complexity ID clustering scheme. Afterward, we derive optimal solutions to these subproblems, and a low-complexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.

Grant-Free Non-Orthogonal Multiple Access (GF-NOMA) is considered as a promising technology to support the massive connectivity of Machine-Type Communications (MTC). The design of efficient and high-performance multi-user detection (MUD) scheme is a challenging issue of GF-NOMA, especially when the number of active users is unknown and relatively high. This paper adopts Sparse Bayesian Learning (SBL) approaches to solve the MUD problem of GF-NOMA in MTC. The MUD problem within a certain access slot is formulated as a Single Measurement Vector (SMV) model and efficiently solved via SBL-based methods. To further improve the MUD performance, we set up a Multiple Measurement Vector (MMV) model and develop block SBL-based MUD methods, by exploiting the temporal correlation of user activity over successive access slots. Then to extend the usage of the aforementioned algorithms to the scenarios with relatively high, or quasi-sparse, user activity, we propose novel SBL-based MUD algorithms via post sparse error recovery methodology, for both the SMV and MMV problem models. Simulation results show that the proposed SBL-based MUD algorithms achieve substantial performance gain over traditional ones, especially when the number of active users is unknown and relatively high.

Multiuser detection (MUD) algorithms is regarded as a challenging issue that need to be resolved for Machine-Type Communications (MTC). In this paper, the MUD is formulated as a sparse signal recovery problem. Note that transmitted signals have sparse characteristics naturally existing in MTC communications, it is desirable to use Bayesian Compressive Sensing (BCS) technique to improve the efficiency of MUD problem. By exploiting the sparsity of transmitted signal, we first propose two-layer hierarchical model based sparse Bayesian learning on variational expectation maximization (SBL- VEM) algorithm to recover the transmitted signals. Furthermore, in order to make use of the support set information of the transmitted signal, the third layer is added, and we propose a novel sparse Bayesian learning support learning (SBL-SL) algorithm. The proposed two algorithms overcome the difficulty of unknown user activity factor in MUD. Simulation results show that the proposed two algorithms achieve better recovery performance than the conventional MUD solutions in MTC.

As a potential development direction of future transportation, the vacuum tube ultra-high-speed train (UHST) wireless communication systems have newly different channel characteristics from existing high-speed train (HST) scenarios. In this paper, a three-dimensional non-stationary millimeter wave (mmWave) geometry-based stochastic model (GBSM) is proposed to investigate the channel characteristics of UHST channels in vacuum tube scenarios, taking into account the waveguide effect and the impact of tube wall roughness on channel. Then, based on the proposed model, some important time-variant channel statistical properties are studied and compared with those in existing HST and tunnel channels. The results obtained show that the multipath effect in vacuum tube scenarios will be more obvious than tunnel scenarios but less than existing HST scenarios, which will provide some insights for future research on vacuum tube UHST wireless communications.

A hybrid downlink system that simultaneously uses visible light communication (VLC) and radio frequency (RF) is investigated, assuming that only one of the two considered users is capable of receiving information over the optical band. In order to facilitate information transmissions from the VLC access point to the RF user, mixed VLC/RF relaying and simultaneous lightwave information and power transfer (SLIPT) are utilized. Moreover, a cognitive-based resource allocation policy and tractable bounds for the harvested energy are introduced. Furthermore, by taking into account the random location of the VLC and RF user terminals, the closed-form outage probability for the VLC user is given, while for the RF user, the outage probability is derived in terms of infinite-series, which is also approximated by a tractable closed-form expression. In addition, the outage probability of the RF user is minimized by optimizing the direct current (DC) component. Finally, simulations are provided to verify the accuracy of the theoretical analysis and demonstrate the effectiveness of the proposed optimization framework.

In massive communication networks, bursty traffic may cause an unexpected network overload. Traditionally, the access class barring (ACB) based random access (RA) schemes perform well in controlling network overload. However, as the number of contention devices increases, the performance of the ACB-based access control mechanism degrades severely, especially in terms of access success probability and average access delay. In recognition of the fact that existing ACB-based RA scheme does not take the influence of the physical random access channel (PRACH) state on RA performance into consideration, in this paper, we propose a joint RA control scheme involving two access check mechanisms, i.e., ACB check and PRACH channel quality check. As a result, the proposed joint scheme can not only reduce the number of contention de-vices in each RA slot through ACB check but also guarantee the success of preamble (PA) detection by means of PRACH quality check. Simulation results show that the proposed scheme outperforms the other ACB-based RA scheme in terms of access success probability, network load capacity, and the average number of PA transmission at the expense of increased total service time. As a result, compared with other access control mechanisms, the proposed scheme is more suitable for delay-tolerate services.

Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestion and severe access delay. In this paper, a semi-distribution congestion control algorithm (CCA) for event-driven M2M communications in LTE-Advanced networks is presented. This CCA uses the random access preamble idleness rate in a small number of slots to estimate the channel load. The eNodeB broadcasts the estimated value of traffic, so the M2M devices can calculate the control factor to control the flow. It is shown that, the throughput and delay performance of the proposed semi-distribution congestion control algorithm are significantly improved compared with the existing schemes.

The most promising feature of the fifth generation (5G) wireless networks is to support the Internet of Things (IoT) application, such as massive machine-type communications (mMTC). In mMTC scenario, a large number of users are connected to an access point, but very few of them are active at the same time, which motivates us to exploit Low-Activity Code Division Multiple Access (LA-CDMA) as the multiple access technology for MTC. The optimal maximum a posterior probability (MAP) assumes that the user activity factor is exactly known, which is in fact unknown in the practical detection scenarios. In this paper, we first formulate the LA-CDMA uplink into a multiple measurement vector (MMV) model for several continuous time slots, then we introduce a novel iterative reweighed (IR) algorithm to reconstruct the sparse signal. The new scheme overcomes the difficulty of unknown user activity factor and the simulation results over massive MTC systems demonstrate that the proposed algorithm achieves substantial performance gain over traditional detectors considerably.

As a hybrid MIMO architecture, beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter wave (mmWave) massive MIMO systems without obvious performance loss, in which, however, the number of users supported cannot be larger than that of RF chains. To break this fundamental limit, we introduce the concept of code-domain non-orthogonal multiple access (NOMA) into beamspace MIMO. A beam selection scheme is proposed first to maximize the sum-rate by utilizing the quasi-orthogonality of the beamspace channel. Furthermore, a low-complexity detection algorithm is developed to realize the transceiver design in mmWave communications using lens antennas. Finally, numerical results of the decoding complexity at receiver side are analyzed. Simulation results show that the proposed beamspace MIMO-NOMA in code domain can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO.

Enabling completely universal coverage, the space-ground communication system integration is one of the most important aspects in the fifth generation (5G) or even the next 6G wireless communications, which significantly benefits railways whose transportation lines always cross diverse environments. Based on this observation, to achieve seamless coverage for environment-diverse high-speed railways (HSRs), by leveraging the control/user-plane (C/U-plane) decoupling and cloud radio access network (C-RAN) technologies, we propose a space-ground integrated cloud railway network consisting of space and ground cloud layers, where in the space, baseband units (BBUs) of low earth orbit (LEO) satellites are collected and centrally-managed by geostationary earth orbit (GEO) satellites. To improve the mobility support and take advantage of the stable and ultra-wide terrestrial coverage of GEO satellites, we establish an additional backup space C-plane (BS-C-plane) connection between trains and GEO satellites. Under this architecture with diverse network resources, we develop a safety-oriented resource allocation scheme based on both the resource allocation priority of safety services and the network handover costs to deliver the safety-oriented services. Simulation results demonstrate that the proposed scheme can always meet the transmission requirements for safety services in HSRs.

We propose an indoor lightwave downlink wireless communication network with the non-orthogonal multiple access (NOMA) technology, that consists of one visible light communication (VLC) access point (AP) and a pair of randomly located users. Although both users can directly receive information from the AP, the performance of the far user is degraded compared to the near one due to the asymmetrical channel gains. Thus, the user cooperation strategy is proposed to improve the performance of the far user by using mixed VLC/RF relaying technique in parallel with the wireless optical direct link. To efficiently exploit the available heterogeneous links, the concept of cross-band selection combining (CBSC) is introduced, according to which the far user is continuously served by either the mixed VLC/RF or the direct VLC link. Meanwhile, the performance of the proposed scheme is thoroughly investigated and compared to appropriate baselines. To this end, we derive closed-form expressions for the outage probability of each user as well as the system sum throughput. Finally, simulation results are provided to verify the effectiveness of the proposed scheme and the accuracy of the corresponding analysis.

The paper studies the multi-cell cooperation scheme under the time and frequency doubly selective channels, in which the multi-cell co-channel interference are jointly eliminated with the inter-symbol interference to enhance the transmission reception. The basic idea of interference alignment is exploited, with which the link-level interference and network-level interference are aligned to the same dimension before canceled. Instead of assuming the perfect channel information is available at the receivers, an embedded pilot framework is also proposed to track and update the channel state information every short time period, making the proposed scheme more feasible for practical application. Additionally, the pilot design is also discussed and an optimal embedded pilot scheme is derived.

Power domain non-orthogonal multiple access (NOMA), which multiplexes multiple users in power domain to achieve high spectrum efficiency, is a promising technology for the fifth-generation (5G) wireless networks and beyond. In this work, we study time and power allocation for two-hop NOMA relaying with decode-and-forward (DF) protocol, to satisfy two NOMA users' different quality of service (QoS) requirements. The performance metrics of outage probability and ergodic capacity are used to evaluate two NOMA users, respectively. The closedform expression for outage probability is also derived. Furthermore, to obtain optimal time allocation factor to achieve the minimum outage probability, we formulate it as one optimization problem and develop a golden section search (GSS)-based algorithm to solve it. Also, the tradeoff between time and power allocation is further analysed to improve the system performance. Numerical and simulation results show that the proposed time and power allocation DF-NOMA behaves better than the traditional equal-time slot cooperative NOMA.

The newly proposed sparse-code multiple-access (SCMA) has been shown to approach the single-user performance even the overload reaches 150% when message passing algorithm (MPA) detection is applied. Obviously, combined with multiple-input multiple-output (MIMO) techniques, the spectrum efficiency of SCMA system can be further improved. While maximum likelihood (ML) detection is optimal for MIMO-SCMA, the detection complexity would grow exponentially when the number of both the antennas and users increase. In this paper, we propose a novel low complexity joint MPA detection scheme for downlink MIMO-SCMA to utilize the sparsity of the channel and codeword. The analysis and simulation results show that compared with ML detection, the proposed joint MPA can decrease the complexity significantly while keep the BER performance unchanged.

With the high speed railway (HSR) systems becoming increasingly popular and the explosive growth in the number of mobile devices, the provision of a better user experience on high speed trains is a pressing problem. The user experience includes not only the transmission rate, but also the transmission delay caused by handover failure. The special linear topology of HSR system leads to the fact that the transmission rate has high dependence on the position of trains. However, the linear topology also makes it possible to implement efficient power allocation (PA) along the railway to improve the system performance. In this paper, we seek to find an appropriate PA scheme to achieve the trade-off between the transmission rate and the delay. A sub- optimum PA scheme is proposed which can, under a certain delay constraint, maximize the average capacity. The simulation results show that the proposed PA scheme can reduce the delay at the cost of slightly reduced capacity, hence providing some insights into the optimal system design.

This study focuses on the design of downlink transmission protocols in multi‐cell multi‐user mobile networks, where co‐channel interference has been recognised as a challenging issue particularly for the users close to the boundary of cells. The key idea of this study is to jointly apply interference alignment (IA) and pre‐cancellation to the addressed scenario, where the former technique can effectively increase the overall system throughput and the latter can significantly boost the diversity gains and reception reliability. To ensure the diversity gains can be achieved with zero‐forcing IA, a precoder optimisation scheme is proposed based on the well‐known iterative interference leakage minimisation scheme. Both analytic and numerical results have been developed to show the capacity and diversity gains obtained by using the proposed scheme. Besides, the computational complexity of the proposed scheme and the effects of imperfect channel state information on the performance are studied as well.

This paper studies the multi-cell cooperative transmission based on interference alignment (IA). A new macro-diversity approach is presented in which each BS not only transmits data to its serving users but also transmits data to the users belonging to the cooperative BSs. By coherently designing the precoding and detection matrices, desired signals from multi-BSs are detected to achieve macro-diversity while undesired signals are aligned to the same dimension before zero-forced by the detector. Additionally, an iterative algorithm based on the well-known leakage interference minimization approach is presented for the implementation of interference alignment in macro-diversity scenario. Simulation results show the proposed scheme has better outage probability performance at the high signal-to-noise ratio (SNR) region compared with conventional interference alignment, confirming the macro-diversity is achieved.

Although V-OFDM system and DFT scrambling vector OFDM system are suitable for frequency-selective fading channels, channel estimation error may significantly degrade the achievable BER performance. In this paper, a new frequency-domain scrambling differential detection and equalization is proposed for DFT-SV-OFDM system, where a scrambling decision feedback is used at the receiver to provide a reliable noise-reference signal, thus reducing the error propagation. Compared with the two existing systems, the new scheme shows a comparable performance but more robust against the channel estimation error.

Abstract In this paper, large code division multiple access (CDMA) random access systems employing the decorrelator and minimum mean square error (MMSE) detectors are investigated over Rayleigh fading channels under the assumption that both the number of users and the spreading gain tend to infinity, but their ratio converges to a constant. The signal to interference ratio (SIR) is shown to converge almost surely to a constant and the bit‐error rate (BER) is expressed as a function of the traffic load, transmission probability, channel coefficient, and distribution of transmission power. Furthermore, the throughput, the spectrum efficiency, and the stability region are analyzed and simulated. For dominating systems, it is shown that the MMSE detector achieves much higher throughput and spectral efficiency than decorrelator detector. Besides, it is also disclosed that, when the signal to noise ratio (SNR) is larger than an optimum value, the spectrum efficiency increases as the ratio of bit energy to noise power spectrum density (Eb/N0) increases; however, when SNR is smaller than the optimum value, the spectrum efficiency decreases as Eb/N0 increases. For ordinary stable systems, it is demonstrated that their stability region gets narrower as the traffic load increases. Copyright © 2010 John Wiley & Sons, Ltd.

In this letter, we introduce a simple approach to generate Z-periodic complementary set (ZPCS) by performing phase shift operation on periodic complementary sequence set (PCS). Favorable correlation properties of ZPCS enable its applications not only in MIMO channel estimation as optimal training sequences, but also in MC-CDMA system as spreading sequences to efficiently handle multipath interference (MI) and multiple access interference (MAI). Our approach provides flexible choices for zero correlation zone (ZCZ) length and set size. The resultant ZPCS is optimal with respect to the theoretical bound.

In this paper, based on a belief-propagation decoding strategy, a class of generalized parity-check codes called complex rotary codes is investigated. It is shown that, by using iterative sum-product decoding, the complex rotary codes have a much lower decoding complexity than Turbo codes, but have almost the same performance for the high code rate and short frame case (frame length< 500 bits). It is also shown that the prime block size of complex rotary codes is essential to achieve better performance because of its uniform checking characteristic.

In this paper, the inverse discrete Fourier transform (IDFT) results of oversampled Walsh Hadamard (WH) sequences and WH-based complementary (CP) sequences are derived, based on which, the peak factors (PF) of MC-CDMA signals are investigated for both the two sequence sets. The upper bounds of PF for both BPSK and QPSK modulation are presented in this paper and the PF bounds presented in B.J. Choi and L. Hanzo (2003) can be regarded as special cases of the bounds derived here. Compared with accurate computing which requires prohibitive computations, the upper bounds obtained provide a more effective way to estimate the PF of BPSK and QPSK modulated MC-CDMA signals employing WH or WH-based CP sequences. In addition, based on the IDFT properties of WH sequences, a simplified search for optimum WH subset in the context of BPSK modulation is derived.