Due to the growing diversity of vertical applications, current integrated sensing and communications (ISAC) technologies in wireless networks remain insufficient to support complex services beyond communications. To this end, future networks are evolving toward an integrated heterogeneous service provisioning (IHSP) platform, which aims to integrate a broad range of heterogeneous services beyond the dual-function scope of ISAC. Nevertheless, this trend intensifies the conflicts among concurrent heterogeneous services under constrained resource sharing. In this paper, we overcome this resource constraint by the joint use of two novel elastic design strategies: compromised service value assessment and flexible multi-dimensional resource sharing. Consequently, we propose a value-prioritized elastic multi-dimensional multiple access (MDMA) mechanism for IHSP. First, we define the compromised Value-of-Service (VoS) metric by incorporating elastic parameters to characterize user-specific tolerance and compromise in response to various performance degradations under constrained resources. This VoS metric serves as the foundation for prioritizing resource sharing among IHSP services with fairness among concurrent competing demands. Next, we adapt the MDMA to elastically multiplex services using appropriate multiple access schemes across different resource domains. This protocol leverages user-specific interference tolerances and cancellation capabilities across different domains to reduce resource-demanding conflicts and co-channel interference within the same domain. Then, we maximize the system’s VoS by jointly optimizing MDMA design and power allocation. Since this problem is non-convex, we propose a monotonic optimization-aided dynamic programming (MODP) algorithm to obtain its optimal solution. Additionally, we develop the VoS-prioritized successive convex approximation (SCA) algorithm to efficiently find its suboptimal solution. Finally, simulations are presented to validate the effectiveness of the proposed designs.

In this paper, we propose a novel integrated sensing and backscatter communication (ISABC) system in which each moving target is attached with a backscatter device (BD) to facilitate simultaneous target identification and parameter estimation. When the base station (BS) transmits signals to its desired user, each BD attached to the target transmits the target identification information to the BS via backscatter communication, which concurrently enhances the echo signal strength. The BS needs to detect the BD symbols and to estimate the target parameters using the echoes. This task, however, is challenging due to the coupling between the BD symbols and the target parameters. To address this issue, we propose a novel iterative detection and estimation (IDE) framework, which involves the following two processes alternately: 1) Utilizing a modified maximum likelihood (ML) estimator to perform parameter estimation with the detected BD symbols; 2) Employing the ML detector to detect the BD symbols with the estimated target parameters. Since the inter-carrier interference (ICI) of the OFDM signal is independent of the BD symbols, but contains the delay and Doppler shift information, we develop a target parameter initialization method using such ICI component to improve the performance of the proposed IDE scheme. Moreover, the closed-form Miller-Chang bound is derived to demonstrate the theoretical performance for the target parameter estimation. Finally, simulation results are provided to validate the effectiveness of the proposed designs.

This paper studies an elasticity-enhanced uplink architecture that synergistically integrates the coordinated full-spectrum reuse in multi-cell cellular networks with multicarrier rate-splitting multiple access (RSMA). The uplink multi-layer RSMA granularly partitions each user’s data stream by strategically distributing splitted submessages across subcarriers and employing an elaborate decoding order, thereby fully exploiting the available spatial-spectral degrees of freedom. Particularly, the sum rate maximization for the multi-cell system is formulated through joint optimization of the user association, uplink power allocation, submessage-specific subcarrier assignment, receive beamforming, and decoding order. To tackle the problem’s non-convexity and mitigate the centralized computational burden, a two-stage approach is developed. First, a low-complexity base station (BS) selection method, grounded in matching games, is proposed to partition the user set. Next, a collaborative distributed scheme is proposed to delegate computational process to the corresponding BSs, where each BS independently addresses the remaining local problems using an alternating optimization algorithm. Specifically, the majorization-minimization (MM) and dual decomposition techniques are employed to derive the suboptimal solutions for the power and subcarrier allocation, while the fractional programming and alternating direction method of multipliers (ADMM) are utilized to achieve closed-form updating of the receive beamforming. Moreover, a dynamically optimized decoding order strategy is analytically derived. Simulation results validate the efficacy of the proposed algorithm in sum rate gain and computational complexity, showcasing that the RSMA-aided multi-cell collaborative transmission can attain superior performance than existing schemes.

Conventional wireless communication techniques and performance indicators are becoming inadequate for the designs of integrated sensing, positioning, and communication (ISPAC) systems, due to their inability to balance diverse competing demands from concurrent heterogeneous services under constrained resources. In this paper, we overcome these challenges with two new elastic design strategies: compromised service value assessment and flexible multi-dimensional service multiplexing. Accordingly, we propose an elastic value-prioritized service provisioning based on multi-dimensional multiple access (MDMA) for ISPAC systems. First, we modify our previous value-of-service (VoS) metric by incorporating elastic parameters to capture user-specific tolerance and compromise in response to various performance degradations under constrained resources. The modified VoS metric can serve as a foundation for prioritizing service and enabling effective service provisioning among competing services. Then, we adapt the MDMA to elastically multiplex services using appropriate multiple access schemes across various resource domains. This protocol leverages user-specific interference tolerances and cancellation capabilities across different resource domains to reduce resource-demanding conflicts and co-channel interference within the same domain. Finally, we formulate a system VoS maximization problem by jointly optimizing the MDMA design and power allocation, and then propose a sub-optimal algorithm to solve it efficiently.

By harnessing the delay-Doppler (DD) resource domain, orthogonal time-frequency space (OTFS) substantially improves the communication performance under high-mobility scenarios by maintaining quasi-time-invariant channel characteristics. However, conventional multiple access (MA) techniques fail to efficiently support OTFS in the face of diverse communication requirements. Recently, multi-dimensional MA (MDMA) has emerged as a flexible channel access technique by elastically exploiting multi-domain resources for tailored service provision. Therefore, we conceive an elastic multi-domain resource utilization mechanism for a novel multi-user OTFS-MDMA system by leveraging user-specific channel characteristics across the DD, power, and spatial resource domains. Specifically, we divide all DD resource bins into separate subregions called DD resource slots (RSs), each of which supports a fraction of users, thus reducing the multi-user interference. Then, the most suitable MA, including orthogonal, non-orthogonal, or spatial division MA (OMA/ NOMA/ SDMA), will be selected with each RS based on the interference levels in the power and spatial domains, thus enhancing the spectrum efficiency. Then, we jointly optimize the user assignment, MA scheme selection, and power allocation in all DD RSs to maximize the weighted sum-rate subject to their minimum rate and various practical constraints. Since this results in a non-convex problem, we develop a dynamic programming and monotonic optimization (DPMO) method to find the globally optimal solution in the special case of disregarding rate constraints. Subsequently, we apply a low-complexity algorithm to find sub-optimal solutions in general cases.

Coordinated beamforming across distributed base stations (BSs) in cell-free wireless infrastructure can efficiently support integrated sensing and communication (ISAC) users by enhancing resource sharing and suppressing interference in the spatial domain. However, intensive coordination among distributed BSs within the ISAC-enabled network poses risks of generating substantial interference to other coexisting networks sharing the same spectrum, while also incurring elevated costs from energy consumption and signaling exchange. To address these challenges, this paper develops an interference-suppressed and cost-efficient cell-free ISAC network, which opportunistically and cooperatively orchestrates distributed radio resources to accommodate the competing demands of sensing and communication (S&C) services. Specifically, we conceive a radiation footprint control mechanism that autonomously suppresses interference across the entire signal propagation space to safeguard other networks without exchanging channel knowledge signaling. Then, we propose joint BS activation and beamforming coordination to dynamically activate appropriate BSs and orchestrate their spatial beams for service provisioning. Building upon this framework, we formulate a cost-efficient utility maximization problem that considers individual S&C demands and location-dependent radiation footprint constraints. Since this results in a non-convex optimization problem, we develop a monotonic optimization embedded branch-and-bound (MO-BRB) algorithm to find the optimal solution. Additionally, we apply a low-complexity iterative method to obtain near-optimal solutions. Finally, simulation results validate the effectiveness of the proposed algorithms.

In this paper, we investigate the integrated sensing and backscatter communication (ISABC) system in mobility scenarios. Specifically, the backscatter devices (BDs) are attached to the moving targets, thus enhancing the signal strength of reflected echoes and concurrently passively transmitting supplementary information, such as identification details, to the ISABC terminal through backscatter communication. The ISABC terminal aims to detect signals from the BDs while concurrently estimating target parameters, such as delays and Doppler shifts, from the backscattered signals. However, it is quite challenging to concurrently achieve parameter estimation and signal detection from the received superposition of two disparate signals emanating from the structural and antenna components of the target equipped with BD. The challenge is exacerbated coupling between the symbols of BD and the estimated parameters, alongside the intercarrier interference (ICI) induced by the Doppler shift. To address these issues, we propose a novel joint parameter estimation and signal detection scheme by alternatively performing the following two processes: 1) Utilizing a modified maximum likelihood (ML) estimation algorithm to perform off-grid ICI-aware sensing with superimposed signals. 2) Employing the generalized likelihood ratio test (GLRT) detector for demodulating the symbols of the BD. Finally, simulation results are provided to demonstrate the performance of the proposed algorithm and validate that the estimation performance can be improved in the high SNR regime.

In conventional dual-function radar-communication (DFRC) systems, the radar and communication channels are routinely estimated at fixed time intervals based on their worst-case operation scenarios. Such situation-agnostic repeated estimations cause significant training overhead and dramatically degrade the system performance, especially for applications with dynamic sensing/communication demands and limited radio resources. In this paper, we leverage the channel aging characteristics to reduce training overhead and to design a situation-dependent channel re-estimation interval optimization-based resource allocation in a multi-target tracking DFRC system. Specifically, we exploit the channel temporal correlation to predict radar and communication channels for reducing the need for training preamble retransmission. Then, we characterize the channel aging effects on the Cramer-Rao lower bounds (CRLBs) for radar tracking performance analysis and achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) transmit beamforming for communication performance analysis. In particular, the aged CRLBs and achievable rates are derived as closed-form expressions with respect to the channel aging time, bandwidth, and power. Based on the analyzed results, we optimize these factors to maximize the average total aged achievable rate subject to individual target tracking precision demand, communication rate requirement, and other practical constraints. Since the formulated problem belongs to a non-convex problem, we develop an efficient one-dimensional search based optimization algorithm to obtain its suboptimal solutions. Finally, simulation results are presented to validate the correctness of the derived theoretical results and the effectiveness of the proposed allocation scheme.

Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication systems. This is because an RIS has a large number of reflective elements, which are passive devices with no active transmitting/receiving abilities. In this paper, we study the channel estimation problem for the RIS aided multi-user millimeter-wave (mmWave) multi-input multi-output (MIMO) system. Specifically, we propose a novel channel estimation protocol for the above system to estimate the cascaded channels, which are the products of the channels from the base station (BS) to the RIS and from the RIS to the users. Further, since the cascaded channels are typically sparse, this allows us to formulate the channel estimation problem as a sparse recovery problem using compressive sensing (CS) techniques, thereby allowing the channels to be estimated with less training overhead. Moreover, the sparse channel matrices of the cascaded channels of all users have a common block sparsity structure due to the common channel between the BS and the RIS. To take advantage of the common sparsity pattern, we propose a two-step multi-user joint channel estimation procedure. In the first step, we make use of the common column-block sparsity and project the received signals onto the common column subspace. In the second step, we make use of the row-block sparsity of the projected signals and propose a multi-user joint sparse matrix recovery algorithm that takes into account the common channel between the BS and the RIS.

Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%.

In this paper, we study the resource allocation design for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long- packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and a significant decoding error rate. Thus conventional resource allocation design based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we employ the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Then, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to various practical constraints. Since the problem formulated belongs to a non-convex mixed integer non-linear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. In addition, we analyze the complexity of the proposed algorithm theoretically. Finally, simulation results show that the proposed optimal algorithm outperforms the suboptimal baseline schemes significantly.

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. This work focuses on the downlink of an IRS-aided multiuser multiple-input single-output (MISO) system. A practical IRS assumption is considered, in which the incident signal can only be shifted with discrete phase levels. Then, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. This non-convex problem is firstly decomposed via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. In addition, an efficient algorithm with closed-form solutions is proposed for the passive beamforming, which is applicable to both the discrete phase- shift IRS and the continuous phaseshift IRS. Simulation results have verified the effectiveness of the proposed algorithm as compared to different benchmark schemes.

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator.

Internet-of-Things (IoT) is a promising technology to connect massive machines and devices in the future communication networks. In this paper, we study a wireless-powered IoT network (WPIN) with short packet communication (SPC), in which a hybrid access point (HAP) first transmits power to the IoT devices wirelessly, then the devices in turn transmit their short data packets achieved by finite blocklength codes to the HAP using the harvested energy. Different from the long packet communication in conventional wireless network, SPC suffers from transmission rate degradation and a significant packet error rate. Thus, conventional resource allocation in the existing literature based on Shannon capacity achieved by the infinite blocklength codes is no longer optimal. In this paper, to enhance the transmission efficiency and reliability, we first define effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users' individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes.