Chirp spread spectrum (CSS) modulation-based long-range (LoRa) communication has attracted widespread attention owing to its long-range coverage and low-power consumption. However, the inherent trade-off between communication range and data rate in CSS modulation creates a throughput bottleneck, significantly limiting LoRa’s applicability in practical scenarios. To enhance the uplink data rate and reliability in LoRa communications, this paper proposes novel schemes that integrate reconfigurable intelligent surfaces (RIS) and index modulation (IM). Specifically, we develop comprehensive transmission and detection mechanisms for both single-node and multi-node scenarios. To improve the performance of single-node transmission, we introduce an IM scheme based on receiving antenna selection combined with RIS-assisted beamforming, and design a low-complexity sequential detector that exploits unique signal characteristics. To address the multi-node scenario with the co-spreading-factor (co-SF) interference, we propose a power control scheme at transmitters inspired by non-orthogonal access principles, and develop an asymptotically optimal RIS beamforming strategy. We further design an efficient demodulation scheme that leverages statistical features of the received signals across selected antennas. Extensive simulation results demonstrate that our proposed schemes effectively improve both bit error rate and data rate performance compared to conventional LoRa systems and related prior art.

With the advantages of low power consumption, wide range coverage, low cost, and non-coherent detection capabilities, long-range (LoRa) technology exhibits signifblackicant potential for application across a wide range of fields, and has thus attracted considerable attention in recent years. Nevertheless, its real-world performance remains hampered by the low data transmission rate. In this paper, we leverage reconfigurable intelligent surface (RIS) and differential index modulation (DIM) in LoRa uplink systems, and develop a RIS-aided LoRa uplink transmission scheme with DIM to enhance the transmission data rate of the system. In the transmission design, additional information is conveyed by constructing RIS-aided space-time coding blocks and utilizing the activation sequence of receiving antenna array elements, followed by demodulation through a differential encoding-based IM strategy that does not require channel state information (CSI). In the detection design, the orthogonal characteristics of LoRa are leveraged to decouple the detections of LoRa symbols and RIS-aided space-time coding blocks, employing sphere decoding algorithms to reduce the complexity of differential detection. Simulation results demonstrate that the proposed transmission and detection design inherits the non-coherent detection characteristics of traditional LoRa and significantly enhances both the data rate and bit error rate performances of the system.

Mobile edge computing (MEC) enabled artificial intelligence-generated content (AIGC) has garnered considerable attention. To support AIGC metaverse applications within MEC in Internet of Things (IoT) networks, it is effective to offload computation tasks, particularly those involving neural networks generative in AIGC, from mobile devices to edge clouds. Existing solutions typically assume the availability of a dedicated and powerful edge server for each user with single modal data, which can handle the entire AIGC service offloading. However, the practical availability of such dedicated and powerful servers may be limited, necessitating the utilization of less capable alternatives. Thus, we propose the multimodal parallel offloading AIGC framework which partitions multimodal content and offloads partial diffusion tasks to multiple servers. Our proposed scheme accelerates mobile deep vision multimodal metaverse applications through parallel offloading provided by multiple servers. We further utilize the generative AI scheme to solve offloading problems to adapt the dynamic and available communication and computing resource in wireless IoT network. Our framework proposed a multimodal parallel diffusion offloading scheme with integrating the recurrent region proposal prediction algorithm to optimize communication and computing resources while minimizing delay. Simulation results show that our approach can significantly reduce delay compared to conventional algorithms.

With integrated sensing and communications (ISAC) and reconfigurable intelligent surface (RIS) emerging as critical enablers for future mobile communications, their combination has attracted increasing attention lately. To effectively utilize RIS for improving ISAC, we propose two novel designs targeting different scenarios. The first design strikes for a spectral-efficient ISAC by seeking to maximize the weighted sum rate (WSR) of communications and minimize sensing radiation pattern approximation error. The second design aims to achieve an energy-efficient ISAC by optimizing the power allocation between communications and sensing subject to communication quality of service (QoS) constraints. Different optimization problems are formulated for the two designs, with practical constraints of RIS considered, including unit modulus and discrete phase shift. Efficient solutions are developed for the non-convex optimization problems by adeptly employing techniques including weighted minimum mean squared error (WMMSE), fractional programming (FP), second-order cone programming (SOCP), semi-definite relaxing (SDR), and feasibility check. Simulation results demonstrate the non-trivial improvements of WSR, communication energy efficiency and sensing radiation patterns achieved by the proposed designs, also highlighting their superiority over the conventional methods.

The unmanned aerial vehicle (UAV) with intelligent reflecting surface (IRS) mounted, namely, aerial IRS, has the potential in improving the information freshness (IF) and transmission data rate for wireless networks, where age of information (AoI) is generally utilized to characterize the IF. In this article, we optimize both the AoI and transmission data rate in aerial IRS-assisted Internet of Things (IoT) networks through the joint transmission scheduling, UAV location, and IRS phase shift matrix design. We formulate a multiobjective optimization problem, which simultaneously minimizes the system average AoI and maximizes the overall transmission data rate. The optimal solutions for the two objectives in the formulated problem are not always consistent with each other. Besides, the optimization problem with either objective is nonconvex and difficult to tackle directly. An effective three-step scheme is developed in this article to solve the formulated problem. To be more specific, firstly the UAV locations are optimized through a$Q$-learning-based scheme to maximize the overall data rate while guaranteeing the signal-to-noise ratio (SNR) constraint of each IoT device; then the IRS phase shift matrices are determined through a low-complexity Tabu-search-based scheme to further improve the overall data rate given the SNR constraint of each device; finally, the transmission scheduling is performed to optimize the system AoI based on a deep$Q$-network algorithm. Simulation evaluation demonstrates that the proposed scheme outperforms existing ones.

Heterogeneous networks(HetNets) have been widely used in the development of 5G because a large number ofsmall base stations(SBSs) can be deployed in hot spots to alleviate uneven traffic distribution. However, the performance of HetNet with wireless backhaul is limited by its backhaul capacity and the severe wireless interference. To solve this problem, we introducereconfigurable intelligent surface(RIS) into the wireless backhaul HetNets and employ reversedtime division duplex(TDD) in the time domain and dynamicsoft frequency reuse(SFR) in the frequency domain. In the proposed RIS-aided dual-layer HetNets system, we formulate a joint optimization problem of bandwidth allocation, RIS passive beamforming, and power allocation to maximize the overall data rate. Two schemes are considered to adapt to different situations, i.e.,unified dynamic SFR(U-SFR) andindividual dynamic SFR(I-SFR). We solve the different sub-problems in U-SFR and I-SFR modes with the alternate optimization method, and obtain closed-form expressions for bandwidth allocation and power allocation. The convergence, feasibility, and complexity of the proposed schemes are also analyzed. In numerical results, the overall data rate is shown to be significantly increased with the help of RIS.

In this letter, considering the inverse synthetic aperture radar (ISAR) imaging of nonuniformly rotating targets under a low signal-to-noise ratio (SNR) environment, an effective ISAR imaging algorithm based on the coherently integrated nonuniform trilinear autocorrelation function (CINTAF) is proposed. Because the definition of a nonuniform trilinear autocorrelation function (NTAF) which enables a coherent accumulation of the signal energy in both the time and lag-time domains, the proposed method has a significant antinoise performance improvement in comparison with other algorithms, while the computational complexity remains similar. The effectiveness and superiority of this new method have been demonstrated through several simulation results.

The adaptive enhanced inter-cell interference coordination (eICIC) configuration is critical for interference management. This problem is challenging especially from energy efficiency perspective and taking individual user fairness into account. Therefore, we formulate a max-min energy efficiency eICIC configuration problem, i.e., determining the number of almost blank subframes (ABS) and user associates with macro or pico while considering fairness jointly. Since the mixed combinatorial and non-smooth features of the problem, an iterative- distributed algorithm is proposed with using fractional programming and Lagrangian dual theory. Numerical results demonstrate the effectiveness of the proposed algorithm and verify fairness achieved among users, and validate the tradeoff between energy efficiency and fairness for eICIC in HetNets comparing with the existing algorithms.

Active antenna system (AAS) is a promising technology to boost the capacity of next generation wireless communication systems. As a key feature of AAS, vertical sectorization can help form new sub-sectors vertically in a conventional macro cell, facilitates reusing the frequency resources for multiple users, and thus has the potential to improve the system peroformance. In this paper, we investigate the performance of vertical sectorization by optimizing the antenna downtilt and transmit power in LTE-A downlink systems. We first derive the achievable data rate of a downlink wireless communication system considering vertical sectorization and then formulate the problem based on the derived data rate. Finally, antenna downtilt and transmit power are optimized to improve the performance of vertical sectorization. The simulation results demonstrate the effectiveness of the proposed algorithm.

Three-dimensional (3D) beamforming and small cells are two effective techniques to meet the demand of explosive data rate in 5G wireless networks nowadays. The cooperation of these two schemes can help small cell involved heterogeneous networks (HetNets) to achieve high performance. In this paper, we investigate the performance of small cell in-band wireless backhaul in a downlink HetNet considering 3D beamforming. We first analyze the received signals of small cells and users for in-band small cell backhauling and then derive the closed-form achievable data rates for users and the overall system based on gamma distribution. Finally, we formulate the problem based on the derived achievable data rate, followed by analysis of the problem. Simulation results demonstrate that combining 3D beamforming with HetNets can significantly improve the system performance.

The densification and expansion of heterogeneous cellular networks (HetNets) pose new challenges on interference management and reduction of energy consumption. The 3GPP has proposed enhanced intercell interference coordination (eICIC) by making a macrocell silent in almost blank subframes (ABSs) to mitigate interference for low power base stations (BSs) in HetNets. However, energy efficiency (EE) is very crucial for the deployment of a large number of low power nodes as they consume a lot of energy. In this work, we develop a novel EE-eICIC algorithm to determine the amount of ABSs and user equipment (UE) that should associate with picocells or macrocells from energy efficiency perspective. Due to the nonsmooth and mixed combinatorial features of this formulation, we focus on a suboptimal algorithm design. Using generalized fractional programming and the convex programming theory, we propose an iterative and relaxed-rounding algorithm to solve the problem. Numerical results illustrate that the proposed EE-eICIC algorithm achieves superior performance in comparison with state-of-the-art methods in terms of energy efficiency of both system and user.

In LTE uplink systems, single-carrier frequency-division multiple access (SC-FDMA) has been employed. In SC-FDMA, orthogonal frequency resources are assigned to different users to avoid intra-cell interference. However, inter-cell interference (ICI) caused by the users in neighboring cells significantly deteriorates the performance. Cooperation among base stations must be used to deal with ICI for multi-cell systems. In this paper, we investigate multi-cell scheduling in SC-FDMA for LTE uplink. We propose a novel cooperative scheduling algorithm that takes inter-cluster and intra-cluster interference into account. We first perform coordinated scheduling and then link adaptation to select modulation and coding scheme (MCS). Simulation results show that the proposed algorithm has significant gains over the single-cell proportional fair (PF) scheduling algorithm on both cell-edge and average throughput. It also outperforms the existing cooperative algorithm under full path loss compensation and fractional open-loop power control (OLPC).

Scheduling can obtain multi-user diversity if channel state information (CSI) is known, such as for low-mobility users and can exploit frequency diversity if CSI is not available at the transmitter, such as for high-mobility users. In this paper, we investigate resource allocation exploiting frequency and multiuser diversity for LTE downlink systems with users of different mobilities. To facilitate resource allocation, we first develop a user classification algorithm to identify high- and low-mobility users. Based on user mobility classification, we then propose a scheduling algorithm to simultaneously obtain multi-user diversity for those low-mobility users and frequency diversity for those high-mobility users. It is demonstrated by computer simulation that the performance of the proposed scheduling algorithm provides 6% and 23% gain of overall cell throughput, and 5.6% and 18% gain of 10th percentile throughput over proportional fairness based frequency-selective and frequency-diversity scheduling algorithms, respectively. Furthermore, the proposed scheduling algorithm has the same order of computational complexity as the frequency-selective scheduling algorithm.

In LTE systems with heterogeneous user mobilities, low-mobility users favor frequency selective scheduling while high-mobility users benefit from frequency diversity scheduling. To benefit both low- and high-mobility users simultaneously, scheduling exploiting frequency selectivity and diversity is desired. To enable the scheduling, low-complexity user mobility classification to distinguish these two types of users is required. In this paper, we first propose a user mobility classification algorithm, which is robust to different channel delay profiles (CDPs), for single-transmit-antenna systems. Then, we extend it to multiple-input multiple-output (MIMO) systems. A low-complexity scheduling algorithm, exploiting both frequency-selectivity and diversity for low- and high-mobility users simultaneously, is also developed. As demonstrated by the simulation results, the proposed user classification algorithm is robust to different CDPs and the proposed scheduling algorithm is effective.

In this paper, we develop a scheduling algorithm to obtain multi-user diversity for those low-mobility users and frequency diversity for those high-mobility users. Computer simulation demonstrates that the proposed scheduling algorithm provides 10% and 16% overall cell throughput gain over proportional fairness based frequency-selective and frequency-diversity scheduling algorithm, respectively. In addition, the proposed scheduling algorithm is shown to have the same order of computational complexity as the frequency-selective scheduling algorithm, and can be easily implemented in the LTE downlink systems.