Complex building structures fundamentally limit indoor wireless performance. While the wireless industry consistently pursues optimal channel capacity, architectural constraints remain significant factors, largely neglected by conventional workflows. To bridge the gap, this paper expands the inter-disciplinary building wireless performance (BWP) framework by introducing a layout-specific evaluation of area spectral efficiency (ASE). We propose ASE gain as a novel metric to quantify the impact of building layouts on indoor wireless channel capacity. Furthermore, we derive and validate an analytical model for the ASE gain against Monte Carlo simulations. This approach significantly reduces the computation time from hours to minutes with a relative error of less than 1%. Numerical results demonstrate that the ASE gain is significantly influenced by building layouts and wireless parameters. For example, in multi-rectangular layouts, reducing the reception threshold density of 10 dBWm−2decreases the variation range by 0.2059 at 1 GHz, whereas it increases the range by 0.1990 at 28 GHz. For non-rectangular polygonal layouts, the variation range is one order of magnitude larger than in simple rectangular rooms. This work highlights the importance of integrating the ASE gain into future building design. It complements existing metrics such as power gain and interference gain to ensure that buildings provide sufficient margin for the future deployment of high-density networks.

In this paper, we commence with the first principles of wave propagation and polarization characteristics during reflection and transmission, and then propose a ray tracing algorithm that can fully capture polarization properties for simulated paths. Simulation results reveal that conventional cross-polarized ratio (XPR) models for multipath components (MPCs), which assume a consistent average value, do not align well with reality. Additionally, the assumption of a constant XPR tends to either overstate or understate the impact of depolarization. This misrepresentation, combined with inaccurate modeling of depolarization in traditional channel models, leads to degraded performance of polarization shift keying (PolarSK) in wireless communication systems. Thirdly, the Average Bit Error Probability (ABEP) performance of different PolarSK modulation configurations is analyzed based on the data from the proposed ray tracing model under various system configurations. These analyses show that indoor building materials with strong depolarization effects lead to irreducible ABEP floors, which cannot be resolved by increasing diversity gain or adjusting constellation diagram. Finally, in order to mitigate these ABEP floors, we introduce an iterative algorithm designed to minimize the floor effect of the ABEP in PolarSK by simultaneously optimizing the complex dielectric constant and thickness of each layer in the multi-layer building material. Numerical results indicate that, for a target ABEP on the order of 10−6, the proposed algorithm achieves a substantial signal to noise ratio (SNR) gain of up to approximately 25 dB, and for different constellation diagrams and different MIMO systems, the ABEP floors are completely eliminated within the considered SNR range, without any SNR penalty. At the same time, we provide a detailed explanation of the physical causes behind the occurrence of the ABEP floor effect. Overall, we establish building wireless performance (BWP) as a fundamental and quantifiable design objective for indoor environments and, by focusing on indoor polarized wireless channels, provide practical design guidelines while revealing that their achievable performance is inherently constrained by building material characteristics and architectural structures, thereby enabling wireless friendly indoor building and channel design toward 6G.

The efficiency of data retrieval is crucial for the lifecycle traceability of large-scale manufacturing products. This article proposes a novel on-chain and off-chain parallel retrieval method based on blockchain and Merkle Patricia Trie (MPT), which is called parallel retrieve index MPT (PRIMPT). In the proposed method, index MPT (InMPT) is designed to store the index information and the off-chain address of transactions based on MPT. In addition, the transaction division and the node division are proposed to realize the parallelization. Among them, the transaction division adds a new stage branch node under the root node of InMPT and divides transactions according to their stages, while the node division groups the nodes in the blockchain according to the derived most efficient group number and assigns retrieval requests to these node groups. Experiment results show that the PRIMPT method improves the efficiency by 44% compared with the state-of-the-art on-chain and off-chain method.

The prosthetic finger demands an impedance controller with outstanding control performance to satisfy the tracking tasks’ requirements in complicated surroundings. For this purpose, suppressing the disturbances caused by the internal and external environments is the main concern for enhancing the universality of forming the impedance controller. This article presents a noise-tolerant zeroing neural network (NTZNN)-based impedance learning controller, which aims to strengthen the anti-noise performance and calculation accuracy of the prosthesis finger controller under noise pollution. Besides, the proposed impedance learning controller consists of an NTZNN model for calculating the actual trajectories in noisy circumstances, an adaptive learning law for improving the convergence property, and an interactive control term to enhance the transient performance. Furthermore, the stability and convergence performances are verified with a candidate Lyapunov function. In addition, the simulative and experimental results showcase the cutting-edge noise-tolerant, high calculation accuracy, and excellent long-term control property of the proposed controller under the noise pollution, which achieves the accuracy at the order of 10-5rad for position level and at the order of 10-3rad/s for velocity level. Eventually, in the noise environment, the accuracy of root-mean-square error (RMSE) andL2-norm in position and velocity levels for utilizing the NTZNN-based learning impedance learning controller achieves 0.0056 rad, 0.0564 rad/s, 0.2217 rad, and 2.5216 rad/s, respectively.

Polarization shift keying (PolarSK) has been demonstrated as a spectrally efficient single-radio-frequency (RF) multiple-input multiple-output (MIMO) technique. We herein further reveal a pivotal characteristic of PolarSK: The inherent robustness to hardware impairments, which provides a theoretical guarantee for practical implementation. Specifically, we propose the first performance degradation evaluation framework for the PolarSK system under transmitter hardware impairments, which incorporates typical hardware impairments including in-phase/quadrature (I/Q) imbalance, nonlinearity, and phase noise. We derive the analytical average bit error probability (ABEP) expression of PolarSK under hardware impairments, along with the asymptotic ABEP floor. The theoretical derivations on both the approximate ABEP and the asymptotic floor are verified in 3GPP channels. Compared to state-of-the-art modulation systems in single-RF MIMO, the PolarSK system exhibits superior robustness against ABEP degradation under non-ideal hardware conditions, achieving up to 8 times lower ABEP.

Fluid antenna system (FAS) revolutionizes wireless communications via utilizing position-flexible antennas that dynamically optimize channel conditions and mitigate multipath fading. This innovation is particularly valuable in indoor environments, in which signal propagation is severely degraded due to structural obstructions and complex multipath reflections. In this paper, we investigate the channel modeling and the joint optimization of antenna positioning, beamforming, and power allocation for indoor FAS. In particular, we propose a layout-specific channel model, and employ the novel group relative policy optimization (GRPO) algorithm for tackling the optimization problem. Compared to the state-of-the-art Sionna model, our model achieves an 83.3% reduction in computation time with an approximately 3 dB increase in root-mean-square error (RMSE). When simplified to a two-ray model, our model allows for a closed-form antenna position solution with near-optimal performance. For the joint optimization problem, our GRPO algorithm outperforms proximal policy optimization (PPO) and other baselines in sum-rate, while requiring only 50.8% computational resources of PPO, thanks to its group advantage estimation. Simulation results show that increasing either the group size or trajectory length in GRPO does not yield significant improvements in sum-rate, suggesting that these parameters can be selected conservatively without sacrificing performance.

With the rapid expansion of smart applications in 6G-enabled low-altitude economy networks, edge intelligence has emerged as an essential technology to reduce latency and energy consumption by dynamically deploying edge servers close to end users. Unmanned aerial vehicles (UAVs) can each carry a small-sized edge server to offer computation services to multiple end users. In this paper, we consider a non-orthogonal multiple access (NOMA)-based two-layer UAV swarm edge intelligence network, which includes task-generating UAVs (TUAVs) and an edge server UAV (SUAV). SUAV offers extra computation capacity to all TUAVs to process their computational tasks remotely. To minimise the long-term total service delay and the total energy consumption of all TUAVs based on the tasks’ transmission and processing, we propose a proximal policy optimisation (PPO)-based offloading decision and resource allocation joint optimisation algorithm by optimising offloading decisions, communication and computation resources allocation, and transmit power for all TUAVs. Simulations demonstrate that our approach outperforms benchmark solutions in terms of long-term total service delay and total energy consumption of all TUAVs in a UAV swarm system.

For the Internet of things (IoT) network, the integrated localization and communication (ILAC) is expected to provide high localization and communication performance simultaneously. However, the existing research to evaluate the performance of ILAC systems fails to reveal the fundamental performance of ILAC systems in practical IoT network topology analytically. In this paper, we develop a unified analytical ILAC framework using stochastic geometry. We then validate the theoretical results obtained from the proposed analytical framework with the simulation results via extensive Monte Carlo simulations. We further analyse the communication coverage and localization coverage probability with respect to the network density, time-frequency-power domain resource allocation, and communication throughout and localization threshold. Finally, based on the ILAC simulation results, we reveal design guidance for ILAC systems. Specifically, we observe the fundamental trade-off between localization and communication performance attributed to the time-frequency-power domain resource allocation. Network density positively affects the ILAC performance, while power control is much less effective due to the dense network topology. The major observations are that time-domain (TD) resource allocation is preferred in dense networks with low localization CRB thresholds, while frequency-domain (FD) resource allocation dominates in sparse networks with large localization CRB thresholds.

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is considered as a promising waveform for Internet of Things (IoT) due to its capability to mitigate interference among asynchronous users. However, as a multicarrier technique, the FBMC-OQAM signal also has a high peak-to-average power ratio (PAPR), which is detrimental to low-power transmission. To address this issue, this article proposes a novel discrete Fourier transform (DFT)-spread scheme to exhaustively reduce the PAPR of the FBMC-OQAM signal. In this scheme, the transmitting data symbols are first mapped with a conjugate symmetry rule and then coded by the DFT. Using this method, the preprocessing step in FBMC-OQAM that involves separating the real and imaginary parts of the quadrature amplitude modulation (QAM) symbols can be avoided. Compared with existing DFT-spread FBMC-OQAM schemes, the proposed scheme demonstrates better PAPR performance while maintaining equivalent spectral efficiency. Additionally, the computational complexity of the proposed scheme experiences only a slight increase, thereby preserving a relatively low computational burden. Numerical simulation results validate the effectiveness of the proposed scheme. Furthermore, the effect of the prototype filter on the PAPR is investigated, and a tradeoff exists between the PAPR and out-of-band performance.

Edge content caching of Internet of Vehicles (IoVs) is a key technology for alleviating backhaul strain and reducing access latency. To protect the privacy of vehicular users, Federated learning (FL) is employed by sharing vehicles’ local models instead of data. However, vehicles may leave the coverage range of serving node before completing the local model training. To enhance model aggregation efficiency, asynchronous Federated learning (AFL) is employed, which allows asynchronous aggregation without waiting for all vehicles to update their local models. In practice, the local models are susceptible to malicious tampering during the global aggregation process. To solve this problem, we propose a secure AFL (SAFL) framework by incorporating a Z-score-based weight detection method within AFL. Moreover, To improve caching efficiency and adapt to the highly dynamic IoV environments, we introduce an innovative proactive caching approach by combining a conditional variational autoencoder and generative adversarial network to predict popular contents, thereby improving the cache hit ratio. Additionally, based on the prediction results of popular content, we optimize intelligent decision-making using multiagent deep reinforcement learning (DRL) to reduce the content transmission delay. Extensive simulations are performed based on real-world datasets and experimental results demonstrate that the proposed SAFL and multiagent DRL hybrid technique outperforms other baseline approaches.

The maximum indoor wireless network performance is determined by building design, making the evaluation of building wireless performance (BWP) essential during the design phase. In this study, we aim to propose a novel BWP evaluation framework that, for the first time, analyzes the influence of user equipment (UE) aperture antenna. The framework provides an analytical examination of how UE aperture antenna impacts BWP metrics, specifically interference gain (IG) and power gain (PG).We begin by refining the path gain model to incorporate the effects of aperture antenna, which allows us to derive analytical expressions for IG and PG. To validate the accuracy of the model, Monte Carlo simulations are first conducted. Subsequently, the study concludes by identifying the underlying causes of the discrepancy between two models, one accounting for aperture antenna effects and the other not. Taking the 1 GHz band as an example, numerical results demonstrate that the existing model underestimates IG by more than 50% due to the neglect of aperture antenna. At 28 GHz, the underestimation becomes more pronounced, with discrepancies increasing by a factor of 3 to 10. In contrast, the PG shows only minor variations between the two analytical models with peak deviations reaching up to 50%. These deviations, resulting from the omission of aperture considerations, may critically compromise key metrics of BWP. Therefore, our proposed model, which factors in aperture effects, is essential for accurately assessing BWP during the building design phase.

Unmanned-aerial-vehicle (UAV) communications assisted by aerial intelligent reflecting surfaces (A-IRSs) have emerged as a highly regarded technology aiming to increase the communication capacity of cellular networks and improve user communication quality. However, the performance of combining UAV-carried A-IRSs with heterogeneous cellular networks (HetNets) has not been fully investigated. In this article, we develop a novel framework to analyze the downlink (DL) performance of an UAV-carried cooperative A-IRSs-enhanced HetNet. First, we model the cascaded fading of the A-IRS-assisted communication channel as a mixture of gamma variables and approximate its distribution function by using the moment matching method. Next, we obtain the exact closed-form expressions of the user-association probability and the probability density function (PDF) of the distance between any user and its serving BS. Then, we derive the Laplace transform of the received signal and the interference power of all types of users for our proposed A-IRS deployment strategy. Finally, we derive analytic expressions of the DL coverage probability and the energy efficiency of UAV networks, which are validated by the simulation results. The results demonstrate that our proposed theoretical framework significantly enhances the precision of analysis, and aligns closely with real-world scenarios. We also show the performance enhancement brought by cooperative A-IRSs, the advantages of our proposed A-IRS deployment strategy within HetNet, as well as the impact of the interference introduced by A-IRSs on direct transmissions.

This paper proposes a novel meta-learning based hyper-parameter optimization framework for wireless network traffic prediction (NTP) models. The primary objective is to accumulate and leverage the acquired hyper-parameter optimization experience, enabling the rapid determination of optimal hyper-parameters for new tasks. In this paper, an attention-based deep neural network (ADNN) is employed as the base-learner to address specific NTP tasks. The meta-learner is an innovative framework that integrates meta-learning with the k-nearest neighbor algorithm (KNN), genetic algorithm (GA), and gated residual network (GRN). Specifically, KNN is utilized to identify a set of candidate hyper-parameter selection strategies for a new task, which then serves as the initial population for GA, while a GRN-based chromosome screening module accelerates the validation of offspring chromosomes, ultimately determining the optimal hyper-parameters. Experimental results demonstrate that, compared to traditional methods such as Bayesian optimization (BO), GA, and particle swarm optimization (PSO), the proposed framework determines optimal hyper-parameters more rapidly, significantly reduces optimization time, and enhances the performance of the base-learner. It achieves an optimal balance between optimization efficiency and prediction accuracy.

The emerging drones enabled air-to-ground (A2G) communications in millimeter-wave (mm-wave) bands are facing the Doppler effect that arises from the inevitable wobbling of the drones. The fast time-varying channel for drones A2G communications can make the channel state information (CSI) from channel estimation outdated, i.e., imperfect CSI. In this article, we introduce two detectors to demodulate the received signal and get the instantaneous bit error probability (BEP) of a mm-wave drones A2G link under imperfect CSI. Based on the designed detectors, we propose an adaptive modulation scheme to maximize the average transmission rate under imperfect CSI by optimizing the signal transmission time subject to the maximum tolerable BEP. A power control policy is proposed in conjunction with the adaptive modulation to minimize the transmission power while maintaining both the BEP under the threshold and the maximized average transmission rate. Numerical results show that the proposed adaptive modulation scheme in conjunction with the power control policy can maximize the temporally averaged transmission rate while reducing the power consumption by up to 50% for wobbling mm-wave drones A2G links.

Orbital angular momentum (OAM) offers a novel and promising approach for resource reuse in wireless communication technology due to its theoretically infinite modality. This paper presents the large-scale fading characteristics of channel modeling. It comprehensively analyzes the path loss and Rician K-factor of OAM channels based on extensive measurements conducted at 30 GHz in multiple scenarios. Firstly, the paper introduces the traditional path loss fitting model. Building upon this, an innovative OAM model based on Laguerre-Gaussian (LG) beams is proposed, supported by actual measurements. Subsequently, the paper analyzes the absorption of OAM beams in different environments, including the lobby, office and corridor, utilizing measured data. It further characterizes the channel's distinct characteristics based on the observed performance in these diverse environments.

In cell-free multiple input multiple output (MIMO) networks, multiple base stations (BSs) collaborate to achieve high spectral efficiency. Nevertheless, high penetration loss due to large blockages in harsh propagation environments is often an issue that severely degrades communication performance. Considering that intelligent reflecting surface (IRS) is capable of constructing digitally controllable reflection links in a low-cost manner, we investigate an IRS-enhanced downlink cell-free MIMO network in this paper. We aim to maximize the weighted sum rate (WSR) of all the users by jointly optimizing the transmit beamforming at the BSs and the reflection coefficients at the IRS. To address the optimization problem, we propose a fully distributed machine learning algorithm. Different from the conventional iterative optimization algorithms that require a central processing at the central processing unit (CPU) and large amount of channel state information and signaling exchange between the BSs and the CPU, in the proposed algorithm, each BS can locally design its beamforming vectors. Meanwhile, the IRS reflection coefficients are determined by one of the BSs. Simulation results show that the deployment of IRS can significantly boost the WSR and that the proposed algorithm can achieve a high WSR with a low computational complexity.

In this paper, we study the spatial bandwidth for line-of-sight (LOS) channels with linear large-scale antenna arrays (LSAAs) in 3D space. We provide approximations to the spatial bandwidth at the center of the receiving array, of the form$C R^{-B}$, where$R$is the radial distance, and$C$and$B$are directional-dependent and piecewise constant in$R$. The approximations are valid in the entire radiative region, that is, for$R$greater than a few wavelengths. When the length of the receiving array is small relative to$R$, the product of the array length and the spatial bandwidth provides an estimate of the available spatial degree-of-freedom (DOF) in the channel. In a case study, we apply these approximations to the evaluation of spatial multiplexing regions under random orientation conditions. The goodness-of-fit of the approximations is demonstrated and some interesting findings about the DOF performance of the channel under 3D and 2D orientation restrictions are obtained, e.g., that, under some conditions, it is better to constrain the receiving array orientation to be uniform over the unit circle in the 2D ground plane rather than uniform over the 3D unit sphere.

This paper proposes to leverage intelligent reflecting surface (IRS) to realize radio-frequency-chain-free uplink-transmissions (RFCF-UT) for an integrated sensing, communication and computation system. In this communication paradigm, IRS works as an information carrier, whose elements are capable of adjusting their amplitudes and phases to collaboratively portray an electromagnetic image like a dynamic quick response (QR) code, rather than a familiar reflection device. Meanwhile, a dual-functional radar-communication base station (BS) is used as a scanner to collect and recognize the information on each IRS carrying the data of its associated user equipment, while detecting the radar target. Based on the established system model, partial and binary data offloading strategies are respectively considered. By defining a performance metric named weighted throughput capacity (WTC), two maximization problems of WTC are formulated. According to the coupling degree of optimization variables in the objective function and the constraints, each optimization problem is firstly decomposed into two subproblems. Then, the methods of linear programming, fractional programming, integer programming and alternative optimization are developed to solve the subproblems. The simulation results demonstrate the achievable WTC of the considered system, thereby validating RFCF-UT.

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to implement downlink communications for a multi-cell multiple-input multiple-output (MIMO) network, which represents a brand-new communication framework. In such a network, one active macro cell base station (MBS) is deployed for radiating energy signal, while each IRS acts as a small cell base station to realize its own information transmission by modulating and reflecting the signal from the MBS. Under this paradigm, we investigate two optimization problems, namely weighted sum rate maximization problem and max-min fairness problem, aiming at satisfying different communication requirements. To seek their optimal solutions, Lagrangian dual transform and alternate methods are employed to optimize the active beamforming vector at the MBS and the passive beamforming vectors at all the IRSs. Additionally, an element clustering scheme is developed to reduce computation and control complexity, in which each element cluster works like a unit and all clusters can collaborate to communicate with users. Extensive simulations are conducted to evaluate the achievable communication performance and to verify the feasibility of the proposed IRS backscatter enabled downlink multi-cell MIMO network.

In this article, a time-efficient mechanism is proposed to solve the joint unmanned aerial vehicle (UAV) base station deployment and user/sensor association (UDUA) problem aiming at maximizing the downlink sum transmission throughput and reducing the time of online computations. In this work, two relevant subproblems are decoupled from the joint UDUA problem: the first one is denoted as the user association subproblem, for certain UAV base station (UAV-BS) positions, this subproblem is settled for finding the strategy which matches aerial and ground nodes optimally. The second subproblem is the positioning of UAV-BSs in order to obtain the best possible solution to the user association subproblem from all possible positioning combinations for the UAV-BSs. In the proposed mechanism, the Kuhn–Munkres algorithm is used to solve the first subproblem as an equivalent bipartite matching problem. For the UAV-BS deployment subproblem, when the two user distributions own a high similarity, we theoretically prove that little performance decline will be introduced when the new user distribution’s optimal strategy is compared with choosing the optimal UAV-BS deployment strategy of stored user distributions. Based on our mathematical analyses, the similarity level between user distributions is well defined and becomes the key to solve the second subproblem. According to experimental findings, the proposed UDUA mechanism, when compared to benchmark approaches, can provide near-optimum system performance with regard to average downlink total transmission throughput and failure rate with significantly decreased computing time.

The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in the user equipment (UE) to the edge to reduce the processing latency. However, it incorporates the task transmission latency, which may become the bottleneck of latency requirements. Data compression (DC) has been considered as one of the promising techniques to reduce the transmission latency. By compressing the computation tasks before transmitting, the transmission delay is reduced due to the shrink transmitted data size, and the original computing task can be retrieved by employing data decompressing (DD) at the edge nodes or the centre cloud. Nevertheless, the DC and DD incorporate extra processing latency. For the F-RAN system, the latency performance has not been investigated considering the DC and DD processes. Therefore, in this work, the successful data compression probability (SDCP), i.e., the probability of the task execution latency being smaller than a target latency and the signal to interference ratio (SIR) of the received signal being higher than a threshold, is defined to analyse the latency performance of the DC-enabled F-RAN. Moreover, to analyse the impact of compression offloading ratio (COR), which determines the proportion of tasks being compressed at the edge, on the SDCP of the F-RAN, a novel hybrid compression mode is proposed based on the queueing theory. Based on this, the closed-form result of SDCP in the large-scale DC-enabled F-RAN is derived by combining the Matern cluster process and M/G/1 queueing model, and validated by the Monte-Carlo simulation. Based on the derived SDCP results, the effects of COR on the SDCP is analysed numerically. The results show that the SDCP with the optimal COR can be enhanced with a maximum value of 0.3 and 0.55 as compared with the cases of compressing all computing tasks at the edge and at the UE, respectively. Moreover, for the system requiring the minimal latency, the proposed hybrid compression mode can alleviate the requirement on the backhaul capacity.

This article proposes an improved general zeroing neural network model to suppress noise and to enhance the real-time performance of solving TVQP problems. The proposed model allows nonconvex activation functions and has noise suppression characteristics, i.e., the NCNSZNN model. Theoretical analyses show that the developed NCNSZNN model converges globally to an accurate solution to the TVQP problem and is robust in the case of MN. Illustrative examples and comparisons are supplied to verify the validity and superiority of the proposed model for online solving TVQP constrained by EAI with MN.

Reconfigurable intelligent surface (RIS), which provides indirect line-of-sight (LoS) transmission paths between the receiver and its blocked transmitter, has been proposed as one of the promising technologies for network performance enhancement. Recent works indicate that both the numbers of RISs and unit cells per RIS have a significant impact on network performance. However, with a given total number of unit cells, the joint analysis of these two factors considering blockage effect has not been investigated. In this paper, the coverage probability of a three-dimensional downlink millimeter wave RIS-assisted network is analyzed. We first derive the acceptable area where a LoS RIS is capable to satisfy the signal-to-noise ratio threshold at the receiver. Next, we model the centers of building blockages and human-body blockages as two independent Poisson point processes and derive the probability that indirect LoS transmissions exist. Then, we derive and validate the analytical upper and lower bounds of the coverage probability as the functions of network parameters and blockage densities. We also derive and validate the closed-form coverage probability when RISs are much closer to UE than BS. Finally, we propose a general network cost model for RIS-assisted network. Results show that in terms of coverage enhancement, densely deployed small-scale RISs outperform sparsely deployed large-scale RISs in scenarios with dense blockages or short transmission distances, while sparsely deployed large-scale RISs are preferable in scenarios with sparse blockages or long transmission distances.

In this paper, we propose a meta-learning based cell-level network traffic prediction framework (ML-TP), which can provide the proper initial weight vector for the learning model of a new prediction task based on the prediction task’s meta-features. In the ML-TP, each prediction task forms a base-task, and a multi-layer long short-term memory (LSTM) network is constructed as its base-learner. Through fast Fourier transform (FFT) analyses of real-world network traffic data, we find that the five most dominating frequency components can capture the cell-level traffic variations, and hence can be used as a base-task’s meta-features. We prove that the well-trained weight vector of a previous base-task’s base-learner is likely to be a proper initial weight vector of a new base-task’s base-learner if the meta-features of the two base-tasks are close to each other in the Euclidean space. Accordingly, we propose a K-nearest neighbours (KNN) algorithm based meta-learner to deal with the meta-task in the ML-TP. Numerical tests show that the ML-TP can significantly increase the base-learners’ after-training prediction accuracy and learning efficiency in terms of the number of base-samples and the number of epochs needed in each base-learner’s fine-tuning progress.

This paper first proposes an ambient wireless energy harvesting intelligent reflecting surface (IRS)-based backscatter communication system, where an active transmission from an access point (AP) to a primary user (PU) and IRS-based backscatter communication coexist in a certain area. Specifically, the system operation consists of two phases. In the first one, the AP communicates with the PU, while the signal energy reaching the IRS can be fed into the energy harvester of IRS. In the second one, the signal from the AP is modulated into new signal for backscatter communication. By jointly optimizing the beamformers at the AP and the IRS as well as the time scheduling of two-phase process, the throughput of backscatter communication is maximized. Simulation results verify that the proposed scheme achieves a substantially enhanced communication performance.

The power consumption is a critical bottleneck of the unmanned aerial vehicle (UAV) air-to-ground (A2G) link due to the limited on-board battery. Meanwhile, the fast time-varying channel for millimeter-wave (mm-wave) A2G links may lead to the aged channel state information (CSI) under inevitable UAV wobbling. In this paper, we propose two optimization problems to evaluate the energy efficiency performance of the mm-wave UAV A2G link under wobbling. Then, we design two algorithms to maximize the energy efficiency of a mm-wave UAV A2G link by optimizing the transmission time and the UAV hover height subject to bit error probability while considering aged CSI. The numerical results present the spectral-energy efficiency trade-off and the optimum hover height of the mm-wave UAV A2G link under aged CSI.

The authors regret the errors in[1, eqs. (13), (27), (34), and (35)]. The corrections for these equations are given as follows:

For wireless communications using linear large-scale antenna arrays, we define a receiving coordinate system and parameterization strategy to facilitate the study of the impact of three-dimensional position and rotation of the arrays on the achievable spatial degrees of freedom (DoF) in line-of-sight (LOS) channels. An analytical framework based on spatial bandwidth analysis is developed, under which three elementary problems corresponding to three basic orthogonal receiving directions are investigated. For each of them, accurate, simple, and interpretable closed-form approximations for the achievable spatial DoF are derived, and the spatial region where a sufficient amount of spatial DoF is expected available is determined. The expressions can easily be integrated into large-scale system-level simulations. Some interesting and surprising observations are made from simulation studies based on the analytical results. For instance, the spatial bandwidth is shown to be approximately constant in almost the entire spatial multiplexing region. Moreover, in significant parts of this region, the optimal receive array orientation is not parallel with the transmitting array.

Online fountain codes (OFCs) have many advantages, such as low overhead, online feedback and optimal encoding, due to the feedback of the instantaneous decoding state. This paper analyzes the characteristics of underwater acoustic networks (UANs) as well as the issues of existing OFCs applied in UANs. Aiming at these issues, two optimization objectives of OFCs are put forward for UANs. In addition, a recursive OFC with limited feedback (ROFC-LF) is presented for UANs. The ROFC-LF reduces the consumption of bandwidth and energy caused by the transmission of useless coding packets. Through limited feedback, the problem of low channel utilization in UANs with half-duplex communication is solved. Furthermore, a data transmission mechanism based on the ROFC-LF for UANs is presented. The theoretical analysis and simulation results show that the proposed transmission mechanism based on the ROFC-LF scheme outperforms the existing OFC schemes in terms of overhead, computational complexity, coding efficiency and energy consumption. Consequently, the ROFC-LF is suitable for UANs with constrained resources.

With the dense deployment of small cells, the impact of height difference between base stations (BSs) and user equipment (UE) on the performance of heterogeneous networks (HetNets) becomes significant. The traditional two-dimensional models are no longer sufficient to capture the three-dimensional (3D) features of dense HetNets. On the other hand, deploying multiple antennas on BSs is a promising approach to improve network capacity. In this paper, we propose a 3D model for a$K$-tier HetNet with multi-antenna BSs, where different tiers share the same frequency band but may differ in BS height, BS density, number of antennas per BS, BS transmit power, association bias, and path loss exponent. We analytically derive the per-tier association probability under both the strongest received signal and the closest BS cell-association strategies. Based on that, we derive the expressions for the downlink ergodic rate, area spectral efficiency (ASE) and energy efficiency. The numerical results reveal that in the presence of macrocell BSs, for low to medium small-cell BS (SBS) densities, the closest BS cell-association strategy leads to low ergodic rate, ASE and energy efficiency regardless of the SBS height; while at very high SBS densities, under both cell-association strategies, SBSs should be deployed at the same height as UE to achieve high ergodic rate, ASE and energy efficiency. Moreover, we find that for a given SBS height, there exists an optimal combination of SBS density and number of antennas per SBS that maximizes the system energy efficiency.

Three-dimensional (3-D) massive multiple-input-and-multiple-output (MIMO) systems, which explore degrees of freedom in both the vertical and the horizontal dimensions, are a promising technology to enhance spectral efficiency in the next-generation communication systems. As an emerging modulation technology with millimetre wave (mm-wave) communication in massive MIMO systems with limited radio-frequency (RF) chains, the spatial scattering modulation (SSM) makes use of beamspace domain resources to further improve the spectral efficiency. However, most published works on the SSM only focus on two-dimensional (2-D) MIMO systems. In this paper, we generalize the SSM system to 3-D space. First, we design a novel generalized 3-D SSM system by considering both vertical and horizontal angles to determine scattering paths, which are selected to convey information bits. Then, we propose a whitening filter based optimal detection algorithm to detect the received symbols with correlated noise, where the correlation is generated by the combination of the received signals from the large-scale planar receiving antenna array. Next, we design a 2-D fast Fourier transform (FFT) based transceiving approach to improve the hardware friendliness. After that, we propose a low-complexity detector based on the linear minimum mean square error (MMSE) detection algorithm. Moreover, we derive the union upper bound on average bit error probability (ABEP) in a closed form and the asymptotic performance expression for the generalized 3-D SSM system. Finally, we analyse the impact of transmission environment on the ABEP performance. Numerical results show that the generalized 3-D SSM system outperforms the conventional 2-D SSM system, which reduces the ABEP by$10\times $with the same signal-to-noise ratio (SNR) level under the typical indoor environment.

In order to enhance the service provisioning to users in the indoor environment, a hybrid visible light communication (VLC)/radio-frequency (RF) Internet of Things (IoT) system is proposed based on simultaneous lightwave information and power transfer (SLIPT). A mobile user equipment, which serves as an off-the-grid relay, is able to extract information from the light-emitting diode source and then forward the processed information to the destination far away, thus dividing the signal transmission into two hops. Specifically, the optical signal received at the relay is separated into alternating current and direct current components for information decoding and energy harvesting, respectively, in the first hop. Then, the energy harvested is used to forward the processed source information to the destination by using RF in the second hop. Subject to the constraints imposed on both the average and the peak powers of the source, the end-to-end outage probability of the system is analytically derived in a closed form. On this basis, the minimization of the end-to-end outage probability is formulated as an optimization problem. This problem is then solved with a joint design of the peak amplitude and the direct current bias of the source transmitter, by trading-off between the performance of two successive hops. Simulation results demonstrate that by using the proposed optimal solution, the information flow and energy flow can be dynamically balanced, resulting in significant performance gains in terms of outage probability and throughput.

This paper presents a part of our ground-breaking work on evaluation of buildings in terms of wireless friendliness at the building-design stage. The main goal is to devise building design practices that provide for a good performance of wireless networks deployed in buildings. In this paper, the interference gain (IG) and power gain (PG) are defined as two figures of merit (FoM) of the wireless performance of buildings. The FoMs bridge the gap between building design and wireless communications industries. An approach to derive exact closed-form equations for these FoMs is proposed for the first time. The derived analytic expressions facilitate straightforward and more computationally efficient numerical evaluation of the proposed FoMs as compared to Monte Carlo simulations for well-known indoor propagation models. It is shown that the derived closed-form expression can be readily employed to evaluate the impact of building properties, such as the sizes and the aspect ratios (ARs) of rooms, on the wireless performance. The proposed approach sheds light to architects on evaluation and design of wireless-friendly building layouts.

Joint-drift problems could result in failures in executing task or even damage robots in actual applications and different schemes have been presented to deal with such a knotty problem. However, in these existing schemes, there exists the coupling in coefficients for eliminating the drift in the joint space and the equality constraint for completing the given task in the Cartesian space, thereby, theoretically, leading to a paradox in achieving zero joint drift in the joint space and zero position error in the Cartesian space simultaneously. A novel joint-drift-free (JDF) scheme synthesized by a projected zeroing neural network (PZNN) model for the motion generation and control of redundant robot manipulators perturbed by disturbances is proposed and analyzed in this article. Besides, the PZNN model could adopt saturated or even nonconvex projection functions. The proposed scheme completely decouples the interferences of joint errors in the joint space and position errors in the Cartesian space for the first time. Beyond that, theoretical analysis is conducted in order to validate that the PZNN model is of global convergence to the theoretical kinematics solution to the motion generation of robots, and that the joint-drift problems are thus remedied. Moreover, several simulations and physical experiments on the strength of different robot manipulators are carried out to confirm the superiority, efficiency, and accuracy of the proposed JDF scheme synthesized by the PZNN model for remedying joint-drift problems of redundant robot manipulators in noisy environments.

Mobile data traffic has been largely generated indoors. However, indoor cellular networks have been studied either on a two-dimensional (2D) plane or as an intractable optimization problem for a multi-story building. In this paper, we develop a tractable three-dimensional small-cell network model for a multi-story building. On each story, the small-cell base stations (BS) are distributed following a 2D homogeneous Poisson point process. We analytically derive the downlink coverage probability, spectral efficiency (SE) and area spectral efficiency for the indoor network as functions of the story height, the penetration loss of the ceiling and the BS density. Our tractable expressions show that a higher penetration loss of the ceiling leads to a higher coverage probability and a higher SE. Meanwhile, with the increase of the story height or the BS density, the downlink coverage probability first decreases and then increases after reaching a minimum value, indicating that certain values of story height and BS density should be avoided for good indoor wireless coverage.

In this paper, two novel adaptive spatial scattering modulation (ASSM) algorithms, namely unequal transmission probability-based ASSM (UTP-ASSM) and equal transmission probability-based ASSM (ETP-ASSM), are proposed to pursue a better tradeoff between the computational complexity and spectral efficiency. According to available channel state information, the proposed ASSM algorithms are conducted to compute the optimal numbers of scatters and the optimal modulation orders constrained by the maximal tolerable symbol error probability (SEP). In addition, the minimal tolerate value of the minimal tolerant signal-to-noise ratio and SEP threshold are computed with a given data rate requirement by the bisection algorithm. At the receiver, a new type of optimal maximum likelihood detector is proposed to enhance the SEP performance. Furthermore, the union upper bound on the SEP is derived and analyzed. In addition, the computational complexity and system performance of the ASSM schemes are analyzed. Simulation results demonstrate that the proposed ASSM algorithms yield a lower SEP than the conventional spatial scattering modulation at the same value of average data rate.

Millimeter-wave (mmWave) communication plays a significant role in 5G networks. In this paper, we model a mmWave multicell network with three-dimensional (3D) large-scale antenna arrays, including the path loss and inter-cell interference. In general, the antenna downtilt of the base station (BS) determines the beamforming direction in both horizontal and vertical planes. The distance between the BS antenna and a typical user is modeled more accurately in 3D while considering the height of the BS than modeling all network nodes in a 2D plane. We investigate the joint impact of the BS antenna height and antenna downtilt on the performance of the mmWave downlink transmission in terms of the user data rate averaged over all possible locations of the user within the cell coverage area. We present numerical results to show that it is critical to jointly optimize the BS antenna height and downtilt in order to maximize the average downlink data rate across the cell coverage area, and provide useful guidelines into the 3D deployment of mmWave networks.

To better characterize indoor small cell networks (SCN), we consider the blockages caused by interior walls and employ the bounded path loss model to derive the expression for energy efficiency (EE) of a downlink massive multiple-input multiple-output (MIMO) SCN. Our EE expression demonstrates that a higher penetration loss of interior walls leads to a higher EE. For the purpose of maximizing EE, we propose a novel genetic algorithm (GA) based scheme to jointly optimize the number of antennas per base station (BS), the number of users per cell, and the transmission power per antenna. Numerical results show that our proposed scheme can achieve almost the identical EE as the optimal greedy search algorithm, while significantly reducing the computational time.

The tremendous popularity of Internet-of-Things (IoT) applications and wireless devices has prompted a massive increase of indoor wireless traffic. To further explore the potential of indoor IoT wireless networks, creating constructive interactions between indoor wireless transmissions and the built environments becomes necessary. The electromagnetic (EM) wave propagation indoors would be affected by the EM and physical properties of the building material, e.g., its relative permittivity and thickness. In this article, we construct a new multipath channel model by characterizing wall reflection (WR) for an indoor Line-of-Sight (LOS) single-user multiple-input-multiple-output (MIMO) system and derive its ergodic capacity in the closed-form. Based on the analytical results, we define the wireless friendliness of building material based on the spatially averaged indoor capacity and propose a scheme for evaluating the wireless friendliness of building materials. Monte Carlo simulations validate our analytical results and manifest the significant impact of the relative permittivity and thickness of building material on indoor capacity, indicating that the wireless friendliness of building materials should be considered in the planning and optimization of indoor wireless networks. The outcomes of this article would enable appropriate selection of wall materials during building design, thus enhancing the capacity of indoor LOS MIMO communications.

A novel generalized polarization-space modulation (GPSM) is proposed for polarized multiple-input multiple-output (MIMO) systems with a limit number of radio frequency (RF) chains. In the spatial domain, multiple dual-polarized (DP) transmit antennas are activated, and then combinations of those indices are used to convey information. While in the polarization domain, depending on the random input bits, only one polarization state is selected for each active DP transmit antenna to transmit information following the rule of the polarized shift keying. At the receiver, the maximum likelihood detector is employed as a benchmark to detect information bits being used to select the polarization state and activated DP antennas. In the detector, imperfect channel state information (CSI) is taken into account. Two less computationally complex detectors, i.e., a linear detector and a sphere decoding (SD) detector are proposed to relieve the computational burden. Sacrificing the average bit error probability (ABEP) performance, the proposed linear detector can reduce the computational complexity significantly. The proposed SD detector can achieve the optimum ABEP performance, while reducing computational complexity by reducing the search space. A closed-form union upper bound (UUB) on the ABEP of the GPSM system with imperfect CSI at the receiver is analytically derived and validated through simulations. From the UUB, a loose asymptotic bound on the ABEP, which sheds light on deriving the diversity gain and the coding gain, is derived. Numerical results show that the signal-to-noise ratio loss caused by increasing the number of transmit antennas is less than 3 dB while the spectral efficiency is increased by 7 b/z/Hz. Therefore, the GPSM can be a promising candidate of down link massive MIMO systems to achieve a high spectral efficiency with a limit number of RF chains.

It has been reported that some specially designed recurrent neural networks and their related neural dynamics are efficient for solving quadratic programming (QP) problems in the real domain. A complex-valued QP problem is generated if its variable vector is composed of the magnitude and phase information, which is often depicted in a time-dependent form. Given the important role that complex-valued problems play in cybernetics and engineering, computational models with high accuracy and strong robustness are urgently needed, especially for time-dependent problems. However, the research on the online solution of time-dependent complex-valued problems has been much less investigated compared to time-dependent real-valued problems. In this article, to solve the online time-dependent complex-valued QP problems subject to linear constraints, two new discrete-time neural dynamics models, which can achieve global convergence performance in the presence of perturbations with the provided theoretical analyses, are proposed and investigated. In addition, the second proposed model is developed to eliminate the operation of explicit matrix inversion by introducing the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Moreover, computer simulation results and applications in robotics and filters are provided to illustrate the feasibility and superiority of the proposed models in comparison with the existing solutions.

The 1000 fold capacity enhancement is one of the key requirements in the future 5G networks, stimulating the interest in jointly adopting several advanced techniques (e.g. multiple antennas and heterogeneous networks (HetNets)). Analysis of the performance of the HetNets jointly with multiple antennas becomes crucial. In this paper, we analyse the K-tier multi-antenna HetNets from a downlink coverage perspective. The coverage probability is derived using the Gil-Pelaez inversion theorem under the stochastic geometry framework. Moreover, a closed form approximated result is obtained for observing the influence of the normalized range bias (NRB). The result shows that our proposed result closely match the Monte Carlo simulation, and the approximation result is effective for searching the optimal NRB.

As a state of the art coding technology, fountain codes are promising to drive the outage probability to zero in non-orthogonal multiple access (NOMA) system suffering from severely fading channels. In this paper, we study cross-layer design for a downlink fountain-coded NOMA system with a base station and two users. In order to achieve successful decoding for both users over block Rayleigh fading channels, an optimization problem, taking optimal allocation of redundancy between packet-level fountain coding and physical-layer channel coding into account, is formulated. The sum of the overall code rates is maximized subjected to a minimum physical-layer code rate constraint of the weak user for the single-input single-output scenario. Numerical analysis shows that it is vital to rapidly increase the overall code rate for the weaker user to maximize the sum of the overall code rates and very little redundancy is required for packet-level fountain coding at the weak user, when the total transmit power is large and the channel gain difference between two users becomes small.

The Long Term Evolution-Unlicensed (LTE-U) scheme has been proposed to exploit the unlicensed spectrum, especially the 5 GHz band, for cellular networks to further increase capacity. Since the 5 GHz band has already been used by WiFi networks, the carrier sense adaptive transmission (CSAT) scheme has been proposed LTE-U access points (LAPs) to harmoniously coexist with WiFi access points (WAPs), where duty cycles are used by LAPs to leave certain time slots that only allow WAPs to access the unlicensed band. However, the performance of the CSAT scheme has not been sufficiently analyzed for multiple unlicensed channels (UCs) in a large-scale network. In this work, we derive the explicit expressions of downlink successful transmission probabilities (STPs) of LAP users and WAP users for a large-scale multi-UC network using stochastic geometry tools. Based on the derived STPs, the fairness between the LTE-U network and the WiFi network, which is defined as the minimum throughput of LTE-U and WiFi users, are analysed versus the duty cycle and the LAP density. Furthermore, the optimal duty cycle is obtained based on the derived STPs in the duty-cycle and non-duty-cycle durations. Our results show that for a given number of UCs and WAP density, and with the optimal duty cycle, the optimal LAP density increases with the increasing number of UCs for maximizing the fairness, but leads to a poorer minimum throughput performance.

In indoor environments, the performance of ultra-dense cellular networks is significantly affected by blockages, especially for ultra-high frequencies. However, previous works either ignore or simplify such effects for analysing the networks. On the basis of stochastic geometry, this paper proposes a mathematically tractable approach to analyse ultra-dense networks in indoors, which considers both the effects of wall blockages and the distance-based path loss. The effects of wall blockages are firstly investigated by modelling the walls as a Boolean scheme of straight lines on a finite plane. Then a path loss model incorporating both the blockage-based and distance-based path loss is applied to analyse the performance of indoor networks. Finally, the analytical result is validated by comparing it with Monte Carlo simulations. The simulation results also show that the optimum transmitter density is finite for indoor ultra-dense networks with blockages, although the coverage probability benefits from the increase of transmitter density.

The Block Korkine-Zolotareff (BKZ) reduction can trade off between performance and complexity by adjusting the size of the local reduction blocks, and therefore, offers the opportunity to approach the optimal performance achieved by the KZ reduction at a lower cost, when adopted by the lattice reduction (LR) aided successive interference cancellation (SIC) receiver for multiple-input multiple-output (MIMO) systems. In this paper, a novel complex-domain BKZ (CBKZ) reduction algorithm is developed by employing an on-demand complex-domain Schnorr-Euchner (SE) enumeration strategy for local shortest lattice vector searching, and a tailored unimodular transform scheme for basis updating. The sorted-QR decomposition coupled with size reduction is employed for pre-processing, rather than the widely adopted Lenstra-Lenstra-Lovász (LLL) reduction. Simulation results show that the proposed CBKZ reduction achieves close-to-optimal performance by using relatively small block sizes, and at the same time, causes only a small proportion of the overall computations required by its competitors: the complex LLL (CLLL) reduction and CLLL-deep reduction. In addition, a unified mathematical framework is provided to encompass the traditional SIC detection with ordering, the LR-aided SIC detection, and the integer-forcing (IF) SIC detection, under the same umbrella.

Channel-width adaptation can significantly improve the connectivity, capacity and reduce the power consumption in wireless networks. The OFDMA-based channel-width adaptation based on traffic demand has been studied in wireless networks with sufficient spectral resources. However, the traffic demand may not be fully satisfied in resource-limited scenarios. In this paper, we study the channel-width adaptation problem in wireless mesh networks (WMNs) with limited spectral resources. More specifically, considering diverse quality-of-service (QoS) requirements, a QoS-aware channel-width adaptation scheme is proposed. First, resource allocation with QoS-aware channel-width adaptation is modelled as an optimization problem. Genetic algorithm is employed to get a near-optimal solution. Then, in order to reduce the computational complexity, a greedy algorithm is developed to suit the highly dynamic traffic demand. Simulation results show that the proposed low-complexity algorithm can guarantee the QoS support in resource-limited scenarios.

The Reduced Power Subframes (RPS) are encouraged to be applied in the LTE-Advanced Heterogeneous Networks (HetNets), to reduce the capacity loss caused by the Almost Blank Subframes (ABS). However, the RPS are supposed to be used in the macrocells only. In fact, the RPS can also be used in femtocells to mitigate the interference that the macrocell edge users suffers, but its performance is not investigated yet. In this paper, we introduce the RPS both in the macrocells and the femtocells. The results of the Signal to Interference Ratio (SIR) coverage probability under the stochastic geometry framework are derived in a closed-form which is verified through Monte Carlo simulation. Based on these results, the macrocell edge users' SIR coverage and the average rate coverage of the network (the average fraction of users achieving a target rate) are analysed numerically. Our proposed scheme enhanced both the SIR of the macro edge users and the average rate coverage probabilities.

In this paper, the spatial modulation (SM) technique is employed at the source and relay nodes in a full-duplex two-way relay channel (FD-TWRC) to support spectral-efficient bi-directional communications while guaranteeing a low cost implementation. Maximum likelihood detectors are employed at each node that is subject to an intrinsic self-loop interference. We first propose a tight upper bound on the average bit error probability (ABEP). Then, based on the ABEP upper bound, an asymptotic ABEP expression is derived in the high signal-to-noise ratio (SNR) regime. Exploiting the asymptotic ABEP, an exact SNR threshold for the selection between FD-TWRC-SM and half-duplex (HD)-TWRC-SM is derived in a closed form, which sheds light on when it is beneficial to select the FD (or HD) mode. In addition, the power allocation (PA) among sources and relay is investigated, through which an optimal PA factor in terms of ABEP is obtained. All analytical results derived in this paper are verified by Monte Carlo simulations, from which some new insights are obtained on the performance of FD-TWRC-SM.

We consider a cooperative multi-path relay channel (MPRC) where multiple half-duplex relays assist in the packet transmissions from a source to its destination. A virtual full-duplex (FD) relaying scheme is proposed that allows the source to transmit a new packet simultaneously with the selected best relay, with the rest of the relays attempting to decode this new packet. Thus, a new source packet can be served in each time slot, as in FD relay systems. Taking into account the effect of inter-relay interference (IRI) that is caused by simultaneous relay and source transmissions, a Markov chain analytical model is used to characterize the decoding performance at the relays, based on which the overall outage probability of MPRC is obtained in closed-form expressions. The asymptotic performance analysis reveals that in low rate scenarios, a close-to-full diversity order is achieved by the proposed scheme while substantially improving the spectrum efficiency. In high rate scenarios, the decoding performance of relays is limited by IRI and the system outage performance experiences an error floor. Simulation results demonstrate the performance gains of the proposed scheme by comparisons with existing half-duplex and FD relay systems in the literature.

Previous work shows that Average Bit Error Probability (ABEP) of SM systems under Non Line of Sight (NLOS) scenario is lower than that under Line of Sight (LOS) scenario because of a lower spatial correlation with a given Signal to Noise Ratio (SNR). However, when transmit power is constrained as a constant, the SNR under LOS scenario is larger than that under NLOS scenario benefiting from the strong LOS ray. Spatial correlation and SNR are two contradictory factors which both impact the performance of SM systems. In order to analyze performances of SM systems with a constant power under LOS and NLOS indoor scenarios, 15150 complex MIMO channel matrices are measured in a typical building of Shenzhen Graduate School, Harbin Institute of Technology. Based on measured data and motivated by the Minimum Distance Lower Bound (MDLB) approach, Euclid distances between received symbols of SM systems, as a monotonic function of ABEP, is analyzed. It is found that the effect of the SNR is much more important than the spatial correlation in the indoor environment so that SM system under LOS scenario shows a better performance than NLOS when the transmitting power is a constant. Furthermore, it is observed that bottlenecks of ABEP under LOS and NLOS scenarios are different. Under the LOS scenario, the bottleneck of ABEP is the antenna index error. On the contrary, under the NLOS scenario, the bottleneck of ABEP is the joint error. Using two extreme cases, such phenomena is analytically explained in the paper.

To obtain the optimal coefficient matrix for the integer-forcing (IF) linear receiver, the successive minima problem (SMP) on lattices needs to be solved. By decomposing SMP into a series of subspace avoiding problems (SAP) and developing a modified sphere-decoding (SD) algorithm based on the Schnorr-Euchner (SE) enumeration strategy to solve each instance of SAP, two practical algorithms are constructed to solve SMP exactly for real and complex lattices respectively. The initial radius and the starting position on the search-tree of the SAP algorithm are optimized by exploiting the intermediate results obtained in previous rounds. As compared to the Minkowski reduction algorithm, the proposed complex SMP algorithm brings not only more freedom in lattice code design, but also computation reduction in finding the coefficient matrix. Moreover, benefiting from the tree-search initialization optimization, the proposed real SMP algorithm can bring even more computation reduction when the system size is large. However, the proposed algorithms achieve practically the same performance as Minkowski reduction for the IF linear receiver, as the Minkowski-reduced basis approximates successive minima very closely.

The Spatial Modulation (SM) transmission scheme boosts the spectral efficiency and achieves the multiplexing gain by activating a single transmit antenna in each time slot. Radio wave propagation characteristics determined by the environment is a decisive factor for the SM system. In this paper, we investigate the performance of the SM scheme over real-world 4x4 Multi-Input Multi-Output (MIMO) channels measured in typical indoor scenarios. Firstly, a MIMO channel sounder is established. Based on the sounder, 15150 complex 4x4 MIMO channel matrices are measured inside a typical teaching building under both Line of Sight (LOS) and None Line of Sight (NLOS) scenarios. Secondly, by comparing the SM system over the measured channel and commonly-used channel models, we prove that both the Independently and Identically Distribute (i.i.d.) Rayleigh and the Spatial Correlation (SC) channel model are oversimplified, and that only practical experiences can yield definitive answers to the achievable real-world system performance. Thirdly, the Average Bit Error Probability (ABEP) performance of the SM system is studied based on the measured data under a variety of system configurations. The study of different receive antenna array settings (4x4, 4x2 and 4x1 MIMO setups) approves the significance of combining scheme at the receiver. SM systems employing different signal constellations (Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (16QAM)) are also investigated and some interesting results are revealed. Lastly, performance assessment of SM against State-Of-The-Art (SOTA) MIMO schemes (Space-Time Block Code (STBC) and Vertical Bell Labs Layered Space-Time (V-BLAST) code) is conducted. Results show that for a 4x4 MIMO, the low-complexity SM scheme outperforms both STBC and V-BLAST.