Uplink sensing in cell-free integrated sensing and communication (CF-ISAC) systems provides a promising solution by reusing massive communication signals. This approach offers low system overhead and enables wide-area coverage through densely deployed users and distributed access points (APs). However, due to the tight coupling between communication and sensing, accurate extraction of uplink sensing parameters becomes a critical bottleneck: sensing parameter extraction relies on precise demodulation of uplink communication signals, while high-quality channel estimation for data demodulation, in turn, requires accurate sensing results. To address this challenge, we propose an iterative communication-sensing optimization framework under uplink CF-ISAC architecture. This framework establishes dynamic information feedback among the three core modules of data detection, channel reconstruction and target sensing, achieving the collaborative improvement of communication-sensing performance. Specifically, the target sensing module integrates pilot-based sensing and data signal-enhanced sensing to extract target parameters. The channel reconstruction module maps the sensing results to channel state information (CSI). The data detection module recovers data symbols using the reconstructed CSI and feeds back both demodulated symbols and residual errors to the sensing module, enabling iterative correction of target parameter estimation. The simulation results show that the proposed iterative framework achieves simultaneous suppression in communication bit error rate (BER) and enhancement in sensing accuracy through several iterations, thereby effectively breaking through the traditional performance limits.

With the evolution from 5G to 6G, ultra-reliable low-latency communication (URLLC) faces increasingly stringent performance requirements. Lower latency constraints demand shorter channel codeword length, which can severely degrade decoding performance. The massive multiple-input multiple-output (MIMO) system is considered a crucial technology to address this challenge due to its abundant spatial degrees of freedom (DoF). While polar codes are theoretically capacity-achieving in the limit of infinite codeword length, their practical applicability is limited by the latency penalty associated with the long codewords. In this paper, we establish a unified theoretical framework and propose a novel spatiotemporal two-dimensional (2-D) polar coding scheme for massive MIMO systems employing minimum mean square error (MMSE) receivers. The polar transform is jointly applied over both spatial and temporal dimensions to fully exploit the large spatial DoF. By leveraging the near-deterministic signal-to-interference-plus-noise ratio (SINR) property of MMSE detection, the spatial domain is modeled as a set of parallel Gaussian sub-channels. Within this framework, we theoretically analyze the 2-D polarization behavior based on the Gaussian approximation method and show that the proposed scheme asymptotically retains its capacity-achieving property, even under finite blocklength constraints and large spatial DoF. Simulation results further demonstrate that, compared to traditional time-domain polar codes, the proposed 2-D scheme can significantly reduce latency while guaranteeing reliability, or alternatively improve reliability under the same latency constraint—offering a capacity-achieving and latency-efficient channel coding solution for massive MIMO systems in future 6G URLLC scenarios.

Massive ultrareliable and low-latency communications (mURLLCs) have emerged as a dominating 6G-standard service. Fast uplink grant is an effective means to solve the uplink access in mURLLC due to the advantages of low signaling cost and collision-free. However, as two key challenges in fast uplink grant, active set prediction and optimal scheduling still lead to high resource wastage and low access success probability. Based on the special spatial sparsity of cell-free networks, we propose the spatial-separable nonorthogonal multiple access (SSNOMA). Compared with nonorthogonal multiple access (NOMA), SSNOMA allows multiple users to share same 3-D resources composed of time-frequency resources and pilots, so as to reduce the resource wastage caused by prediction errors. Furthermore, we design an intelligent hierarchical fast uplink grant framework. In this framework, the upper controller is responsible for scheduling users from the predicted active user set to ensure the optimal Quality of Service (QoS) and active probability, while the lower controller strictly controls the allocation of uplink grants among the scheduled users to maximize spectral efficiency. In addition, considering the limited ability to collect information in massive user access, the upper confidence bound (UCB)-based multiarmed bandit (MAB) algorithm is used in the upper layer to schedule fast grant users, while the multiagent deep deterministic policy gradient (MADDPG) is used in the lower layer to perform specific grant allocation. Simulation results show that the proposed SSNOMA-based intelligent hierarchical framework can significantly improve the utilization of limited resources, and track long-term scheduling experience as well as QoS, effectively supporting mURLLC.

This paper investigates the integration of a reconfigurable intelligent surface (RIS) into a secure multiuser massive multiple-input multiple-output (MIMO) system in the presence of transceiver hardware impairments (HWI), imperfect channel state information (CSI), and spatially correlated channels. We first introduce a linear minimum-mean-square error estimation algorithm for the aggregate channel by considering the impact of transceiver HWI and RIS phase-shift errors. Then, we derive a lower bound for the achievable ergodic secrecy rate in the presence of a multi-antenna eavesdropper when artificial noise (AN) is employed at the base station (BS). In addition, the obtained expressions of the ergodic secrecy rate are further simplified in some noteworthy special cases to obtain valuable insights. To counteract the effects of HWI, we present a power allocation optimization strategy between the confidential signals and AN, which admits a fixed-point equation solution. Our analysis reveals that a non-zero ergodic secrecy rate is preserved if the total transmit power decreases no faster than 1/N, whereNis the number of RIS elements. Moreover, the ergodic secrecy rate grows logarithmically with the number of BS antennasMand approaches a certain limit in the asymptotic regimeN→ ∞. Simulation results are provided to verify the derived analytical results. They reveal the impact of key design parameters on the secrecy rate. It is shown that, with the proposed power allocation strategy, the secrecy rate loss due to HWI can be counteracted by increasing the number of low-cost RIS elements.

We investigate the downlink secrecy performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems in the presence of a multi-antenna eavesdropper. We first derive a tight closed-form expression for characterizing the lower bound of the achievable ergodic secrecy rate under spatially correlated channels, taking into account low-resolution digital-to-analog converters (DACs) and RIS phase noise. Subsequently, building upon the derived results, we optimize the power allocation among the information signal and artificial noise in closed form and the RIS phase shifts by developing a projected gradient ascent algorithm, which requires only statistical channel state information of the aggregated channel with low implementational complexity. All theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments. Our results reveal that low-resolution DAC can be beneficial with equal power allocation when the number of RIS elements is small. Besides, the detrimental influence attributed to low-resolution DACs gains prominence as the number of RIS elements grows substantially.

It is supposed that ultra-reliable low latency communication (uRLLC) would continue to evolve in the future sixth generation (6G) network, to provide enhanced capability towards extreme connectivity, with the aid of well established multiple-input multiple-output (MIMO) technology. Since the latency constraint can be represented equivalently by the blocklength of a codeword, channel coding theory at a finite blocklength plays an important role in theoretic analysis of uRLLC. Based on Polyanskiy’s and Yang’s asymptotic results on maximal achievable rate, we first derive the proximate closed-form expressions for the expectation and variance of channel dispersion. Then, the upper bound of average maximal achievable rate is obtained for massive MIMO systems under ideal independent and identically distributed fading channels. Since almost all the fundamental parameters, including the spatial degree-of-freedom (DoF), are considered, this expression can be viewed as a performance bound of the spatiotemporal two-dimension channel coding to some extent. Moreover, it is shown by simulation and analysis, as the DoF goes to infinity, MIMO systems reveal a nature of deterministic transmission, since the average maximal achievable coding rate per antenna can be achieved at each transmission. In this case, the inversely proportional law observed therein implies that the blocklength in the time domain can be further shortened at the expense of spatial DoF. This exchangeability of space and time, to support a given coding rate, paves a solid and feasible road for us to further reduce latency in 6G uRLLC.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Satellite communication is an important technology for the coverage of open country, and plays a vital role of link channel in remote Internet of Things (IoT). However, various kinds of IoT terminals may suffer from the limited battery capacity and computing capability. Therefore, this article proposes a new multiuser IoT system design, in which the satellite link is used to provide communication service for access points (AP), and allow terminals to download and upload data through APs. Then, the AP will exploit simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) technologies to alleviate the deficiencies mentioned above. Moreover, the AP will equip with the full duplex (FD) and multi-input multi-output (MIMO) technologies to further improve the spectrum efficiency. Besides, a hybrid energy storage is assumed in the AP, i.e., the energy may come from power grid or renewable energy. Taking into account all issues above, we optimize the uplink achievable rate through jointly optimizing the CPU frequency, computation tasks, terminal transmitting power and the ratio of MEC task. In order to solve this optimization problem, we first decouple it into two sub-problems. For the first one, we can obtain the closed-form solution of CPU frequency. For the second one, we continue to decompose it into two non-convex problems, and then the iterative active-set method is used to solve these two problems. Numerical simulations demonstrate the effectiveness of the proposed design.

In this paper, we propose an optimal scheme for a distributed antenna system (DAS) to maximize energy efficiency (EE) under a constraint of overall transmit power of each remote access unit (RAU). We exploit the multicriteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multicriteria optimization function, which is extremely complex, into a simpler single objective optimization function. Then an optimal algorithm is developed to allocate the available power to tradeoff EE and SE effectively. Furthermore, we also illustrate the effectiveness of the proposed method and demonstrate there is a tradeoff between energy-efficient and spectral-efficient transmission through computer simulation of a downlink multiuser DAS.

Coordinated multipoint has been widely introduced to enhance the capacity, especially that of users at cell edge. Recently energy efficient transmission techniques are increasingly important due to the increase of energy consumption in wireless communication systems. In this paper, an energy efficient cooperative transmission algorithm using coordinated beamforming is proposed for multi-cell MIMO networks. We formulate the problem as a non-convex optimization problem in a fractional form. Then we transform the problem into an equivalent form and propose an iterative algorithm to solve it. In each iteration, a simplified zero-forcing coordinated beamforming using beam tracing and an optimal power allocation algorithm for maximizing energy efficiency are derived. Simulation results demonstrate that the proposed algorithm has a better energy efficiency performance than the traditional capacity maximizing method. With the lower complexity, the proposed method using beam tracing approaches the energy efficiency performance of the subspace decomposition method in slowly varying channels.

Energy efficiency(EE) has caught more and more attention in future wireless communications due to steadily rising energy costs and environmental concerns. In this paper, we propose an EE scheme with proportional fairness for the downlink multiuser distributed antenna systems (DAS). Our aim is to maximize EE, subject to constraints on overall transmit power of each remote access unit (RAU), bit-error rate (BER), and proportional data rates. We exploit multi-criteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multi-criteria optimization problem, which is extremely complex, into a simpler single objective optimization problem. Then an optimal algorithm is developed to allocate the available power to balance the tradeoff between EE and SE. We also demonstrate the effectiveness of the proposed scheme and illustrate the fundamental tradeoff between energy- and spectral-efficient transmission through computer simulation.

In this paper, we compare EE of the distributed MIMO (D-MIMO) and co-located MIMO (C-MIMO) in the uplink cellular systems since mobile stations are battery powered. The total energy consumption includes both the circuit energy consumption and the transmission energy. We get the closed-form expression for EE of D-MIMO and C-MIMO systems. What's more, an optimization algorithm is proposed to get the optimal EE values while satisfying given spectral efficiency (SE) requirement for both D-MIMO and C-MIMO systems. Simulation results show that the D-MIMO systems are more energy efficient than C-MIMO systems in composite fading channel, and the optimal EE value can be obtained by the proposed algorithm while satisfying given SE requirement.

Transmit beamforming and receive combining, which is used in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, is investigated in this paper. The complexity of linear spherical interpolation algorithm is too large and it can only be applied to unquantized beamforming, so we propose a general spherical interpolation method. This method feeds back pilot subcarrier beamforming vectors to the transmitter, then using the channel correlation in frequency domain, the beamforming vectors for non-pilot subcarriers are reconstructed according to that of the two neighboring pilot subcarriers using the general spherical interpolation algorithm. Simulation results show that this algorithm is applicable not only for unquantized beamforming, but also for quantized beamforming. Considering the realization of the actual system, we propose a phase quantized method. Simulations demonstrate that the performance of the phase quantization method outperforms existing MIMO-OFDM beamforming interpolation algorithms, and is easy to realize in actual system.

Cell edge effect has been recently paid much attention in the development of new generation mobile communications systems because it can cause serious performance degradation in cell edge. In this paper, the cell edge effects of traditional cellular systems and distributed cellular systems are evaluated and compared in environments with or without inter-cell interference (ICI). Three performance metrics are proposed to quantify the cell edge effect of cellular systems. A lower bound is derived on the location-specific spectral efficiency of the collocated antenna system (CAS). Both an approximate expression and an iterative method to quantify the location-specific spectral efficiency of the distributed antenna system (DAS) are presented. Moreover, based on these results, the proposed performance metrics are analyzed and discussed. Finally, numerical results for typical configurations of the cellular systems are presented to validate the theoretical results, and it is also shown that the cell edge performance of the DAS is better than that of the CAS.

In this paper, an efficient digital implementation of multicarrier transmission scheme based on generalized discrete Fourier transform (DFT) filter banks is presented for multicarrier code-division multiple-access (MC-CDMA) systems. Generalized DFT filter banks has been traditionally discussed for very high-speed digital subscriber lines (VDSL) wireline systems, received interest also for wireless applications. The design of the generalized DFT modulated filter banks is investigated and its fast implementation is derived in the time-domain for arbitrary integer sampling rate. Through the utilization of the generalized DFT modulated filter banks, one or more subcarriers, even noncontiguous subcarriers can be easily supported by system in both uplink and downlink, which facilitates the users to access the network in a frequency-division multiple-access manner. Simulation results show that the overall bit-error-rate performance of the proposed multicarrier transmission scheme well approaches that of a single-carrier system due to the negligible intercarrier interference introduced by an appropriate design of the generalized DFT modulated filter banks.