Sparse code multiple access (SCMA) building upon orthogonal frequency division multiplexing (OFDM) is a promising wireless technology for supporting massive connectivity in future machine-type communication networks. However, the sensitivity of OFDM to carrier frequency offset (CFO) poses a major challenge because it leads to orthogonality loss and incurs intercarrier interference (ICI). In this paper, we investigate the bit error rate (BER) performance of SCMA-OFDM systems in the presence of CFO over both Gaussian and multipath Rayleigh fading channels. We first model the ICI in SCMA-OFDM as Gaussian variables conditioned on a single channel realization for fading channels. The BER is then evaluated by averaging over all codeword pairs considering the fading statistics. Through simulations, we validate the accuracy of our BER analysis and reveal that there is a significant BER degradation for SCMA-OFDM systems when the normalized CFO exceeds 0.02.
In this letter, we incorporate index modulation (IM) into affine frequency division multiplexing (AFDM), called AFDM-IM, to enhance the bit error rate (BER) and energy efficiency (EE) performance. In this scheme, the information bits are conveyed not only by $M$-ary constellation symbols, but also by the activation of the chirp subcarriers (SCs) indices, which are determined based on the incoming bit streams. Then, two power allocation strategies, namely power reallocation (PR) strategy and power saving (PS) strategy, are proposed to enhance BER and EE performance, respectively. Furthermore, the average bit error probability (ABEP) is theoretically analyzed. Simulation results demonstrate that the proposed AFDM-IM scheme achieves better BER performance than the conventional AFDM scheme.
Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for low-power wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming and uplink multi-user detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penalty-based optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.
Sparse code multiple access (SCMA) is a promising technique for the enabling of massive connectivity in future machine-type communication networks, but it suffers from a limited diversity order which is a bottleneck for significant improvement of error performance. This paper aims for enhancing the signal space diversity of sparse code multiple access (SCMA) by introducing quadrature component delay to the transmitted codeword of a downlink SCMA system in Rayleigh fading channels. Such a system is called SSD-SCMA throughout this work. By looking into the average mutual information (AMI) and the pairwise error probability (PEP) of the proposed SSD-SCMA, we develop novel codebooks by maximizing the derived AMI lower bound and a modified minimum product distance (MMPD), respectively. The intrinsic asymptotic relationship between the AMI lower bound and proposed MMPD based codebook designs is revealed. Numerical results show significant error performance improvement in the both uncoded and coded SSD-SCMA systems.
This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.
Intelligent reflecting surface (IRS) is a promising and disruptive technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted Terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding in the presence of eavesdropper. Two types of sparse RF chain antenna structures are adopted, i.e., sub-connected structure and fully connected structure. Cluster heads are firstly selected for transmissions, and discrete phase-based analog precoding is designed for the transmit beamforming. Subsequently, based on the channel conditions, the users are grouped into multiple clusters, and each cluster is transmitted by using the NOMA technique. In addition, a low complexity zero-forcing method is employed to design digital precoding so as to eliminate interference between clusters. On this basis, we propose a secure transmission scheme to maximize the sum secrecy rate by jointly optimizing the power allocation and phase shifts of IRS under the constraints of system transmission power, achievable rate requirement of each user, and IRS phase shifts. Due to multiple coupled variables, the formulated problem leads to a non-convex issue. We apply the Taylor series expansion and semidefinite programming to convert the original non-convex problem into a convex one. Then, an alternating optimization algorithm is developed to obtain a feasible solution of the original problem. Simulation results are demonstrated to validate the convergence of the proposed algorithm, and confirm that the deployment of IRS can significantly improve the secrecy performance.
To make indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks free from wired fronthaul, this paper studies a multicarrier-division duplex (MDD)-enabled two-tier terahertz (THz) fronthaul scheme. More specifically, two layers of fronthaul links rely on the mutually orthogonal subcarreir sets in the same THz band, while access links are implemented over sub-6G band. The proposed scheme leads to a complicated mixed-integer nonconvex optimization problem incorporating access point (AP) clustering, device selection, the assignment of subcarrier sets between two fronthaul links and the resource allocation at both the central processing unit (CPU) and APs. In order to address the formulated problem, we first resort to the low-complexity but efficient heuristic methods thereby relaxing the binary variables. Then, the overall end-to-end rate is obtained by iteratively optimizing the assignment of subcarrier sets and the number of AP clusters. Furthermore, an advanced MDD frame structure consisting of three parallel data streams is tailored for the proposed scheme. Simulation results demonstrate the effectiveness of the proposed dynamic AP clustering approach in dealing with the varying sizes of networks. Moreover, benefiting from the well-designed frame structure, MDD is capable of outperforming TDD in the two-tier fronthaul networks. Additionally, the effect of the THz bandwidth on system performance is analyzed, and it is shown that with sufficient frequency resources, our proposed two-tier fully-wireless fronthaul scheme can achieve a comparable performance to the fiber-optic based systems. Finally, the superiority of the proposed MDD-enabled fronthaul scheme is verified in a practical scenario with realistic ray-tracing simulations.
Terahertz (THz) systems are capable of supporting ultra-high data rates thanks to large bandwidth, and the potential to harness high-gain beamforming to combat high pathloss. In this paper, a novel quantum sensing (Ghost Imaging (GI)) based beam training is proposed for Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR RIS) aided THz multi-user massive MIMO systems. We first conduct GI by surrounding 5G downlink signals to obtain 3D images of the environment including users and obstacles. Based on the information, we calculate the optimal position of the UAV-mounted STAR by the proposed algorithm. Thus the position-based beam training can be performed. To enhance the beam-forming gain, we further combine with channel estimation and propose a semi-passive structure of the STAR and ambiguity elimination scheme for separated channel estimation. Thus the ambiguity in cascaded channel estimation, which may affect optimal passive beamforming, is avoided. The optimal active and passive beamforming are then carried out and data transmission is initiated. The proposed BS sub-array and sub-STAR spatial multiplexing architecture, optimal active and passive beamforming, digital precoding, and optimal position of the UAV- mounted STAR are investigated jointly to maximize the average achievable sum rate of the users. Moreover, the cloud radio access networks (CRAN) structured 5G downlink signal is proposed for GI with enhanced resolution. The simulation results show that the proposed scheme achieves beam training and separated channel estimation efficiently, and increases the spectral efficiency dramatically compared to the case when the STAR operates with random phase.
With the blooming of Internet-of-Things (IoT), we are witnessing an explosion in the number of IoT terminals, triggering an unprecedented demand for ubiquitous wireless access globally. In this context, the emerging low-Earth-orbit satellites (LEO-SATs) have been regarded as a promising enabler to complement terrestrial wireless networks in providing ubiquitous connectivity and bridging the ever-growing digital divide in the expected next-generation wireless communications. Nevertheless, the stringent requirements posed by LEO-SATs have imposed significant challenges to the current multiple access schemes and led to an emerging paradigm shift in system design. In this article, we first provide a comprehensive overview of the state-of-the-art multiple access schemes and investigate their limitations in the context of LEO-SATs. To this end, we propose the amalgamation of the grant-free non-orthogonal multiple access (GF-NOMA) paradigm and the orthogonal time frequency space (OTFS) waveform, for simplifying the connection procedure with reduced access latency and enhanced Doppler-robustness. Critical open challenging issues and future directions are finally presented for further technical development.
Terahertz (THz) and intelligent reflecting surface (IRS) have been regarded as two promising technologies to improve the capacity and coverage for future 6G networks. Generally, IRS is usually equipped with large-scale elements when implemented at THz frequency. In this case, the near-field model and beam squint should be considered. Therefore, in this paper, we investigate the far-field and near-field beam squint problems in THz IRS communications for the first time. The far-field and near-field channel models are constructed based on the different electromagnetic radiation characteristics. Next, we first analyze the far-field beam squint and its effect for the beam gain based on the cascaded base station (BS)-IRS-user channel model, and then the near-field case is studied. To overcome the far-field and near-field beam squint effects, we propose to apply delay adjustable metasurface (DAM) to IRS, and develop a scheme of optimizing the reflecting phase shifts and time delays of IRS elements, which effectively eliminates the beam gain loss caused by beam squint. Finally, simulations are conducted to demonstrate the effectiveness of our proposed schemes in combating the near and far field beam squint.