In this study, we design novel approximately mutually unbiased bases (AMUBs) sequences with arbitrary lengths and large family sizes for massive connection systems. Sequences with low correlations are highly demanded for many wireless communications systems, including Industrial Internet of Things (IIoT) systems for various applications. While many sets of sequences have been designed in the past decades, the requirement of large family size, i.e., the number of available sequences for massive connection systems has not yet been addressed. It is well known that mutually unbiased-based (MUB) sequences process desired correlation properties with large family sizes. However, the family size based on the current construction methods is limited by the length of the MUB sequences. In real applications, the longer length may lead to higher overhead and affect the overall transmission rate. This drawback makes MUB sequences have limited applications for industrial massive connection systems. In this article, we modified the original sequences generator of MUB from a quadratic polynomial to a cubic polynomial to further increase the family size. To generate AMUB sequences with arbitrary lengths, we then proposed a construction method based on the exponential sums over finite fields, and optimized the continuous peak-to-average power ratio (PAPR) of the proposed AMUB sequences for real applications. Given the dimension of MUB M, the proposed method can increase M times the number of available sequences. Meanwhile, the length restrictions in MUB sequence construction are removed. Theoretical cross-correlation (CC) bounds are provided and show low correlations of the proposed sequences. The low CC, PAPR, and increased family size of the proposed sequences are then verified by numerical results.

We analyze the uplink performance of a distributed massive multiple-input multiple-output (MIMO) architecture in which the remotely located access points (APs) are connected to a central processing unit via a fiber-optical fronthaul carrying a dithered and 1-bit quantized version of the received radio-frequency (RF) signal. The innovative feature of the proposed architecture is that no down-conversion is performed at the APs. This eliminates the need to equip the APs with local oscillators, which may be difficult to synchronize. Under the assumption that a constraint is imposed on the amount of data that can be exchanged across the fiber-optical fronthaul, we investigate the tradeoff between spatial oversampling, defined in terms of the total number of APs, and temporal oversampling, defined in terms of the oversampling factor selected at the central processing unit, to facilitate the recovery of the transmitted signal from 1-bit samples of the RF received signal. Using the so-called error-vector magnitude (EVM) as performance metric, we shed light on the optimal design of the dither signal, and quantify, for a given number of APs, the minimum fronthaul rate required for our proposed distributed massive MIMO architecture to outperform a standard co-located massive MIMO architecture in terms of EVM.

The performance of multi‐cell massive multiple‐input multiple‐output systems is constrained by inter‐cell interference. In this study, a pilot allocation approach is proposed to mitigate interference in such systems. The asymptotic spectral efficiency in the limit of the number of antennas is derived first. By analysing the derivatives of the spectral efficiency with the pilot allocation, a single branch search algorithm for pilot allocation is formed. In contrast, the existing approaches allocate pilots for each cell separately or with brute‐force search. The analysis and numerical results show that the proposed approach is of better compromise in mitigating the interference and reducing the computational complexity.

Precise ultra-wideband (UWB) ranging requires accurate estimation of time of arrival (TOA). In this paper,a novel TOA estimation approach using checking window is proposed for energy detection (ED) receivers in dense multipath channels. Unlike the traditional methods that treat the multipath components (MPCs) as interference sources, the proposed scheme exploits the MPCs to consolidate the detection of the first path (FP). By detecting dense threshold-crossing (TC) events over successive energy samples with the checking window, the MPCs are collected, and the false TC events that sparsely distributed in the noise region can be recognized and neglected for the FP detection. As a result, the early false detection is reduced and the leading edge detection is improved. Simulations demonstrate the effectiveness and robustness of the proposed scheme in IEEE 802.15.4a channels.

In this paper, two near-optimal detectors based on the convex optimization algorithm are proposed for multiuser detection (MUD) in the ultra-wide bandwidth (UWB) systems. The first detector performs semidefinite relaxation (SDR) to approximate the optimum multiuser detection (OMD) which is a nondeterministic polynomial time hard (NP-hard) problem. When the cutting planes generation algorithm is employed to strengthen the relaxation of the SDR, a tight MUD detector for combinatorial optimization is obtained by adding triangle inequalities to the well-known SDR in strict feasible region. Simulations demonstrate that the semidefinite programming (SDP) approaches can provide bit error rate (BER) performance close to the OMD efficiently using the interior point method, and the tight detector provides a better BER performance than the previous detector with a slightly higher complexity.

The key of time of arrival (TOA) estimation in ultra wideband (UWB) ranging is to detect the first path (FP). In this paper, we propose an optimized FP detector with the adaptive threshold in the absence of prior channel state information (CSI). In particular, the error information (EI) set is introduced to guide the threshold adjustment and determine the TOA estimate with a novel iterative algorithm. The EI set captures the characteristics of major errors regarding the inappropriate threshold. After the iterative process, the proposed scheme is shown to achieve the asymptotic optimal threshold without large number of repeated pulses. Therefore, the proposed scheme efficiently improves the TOA estimation accuracy as compare to the traditional schemes. Simulation results validate the effectiveness and superiority of the proposed scheme.

In this paper, semidefinite relaxation (SDR) technology is exploited for the multiple-symbol detection (MSD) over the multiple-input multiple-output (MIMO) ultra-wideband (UWB) systems. The existing scheme generalized likelihood ratio test (GLRT) MSD jointly detect multiple symbols, however, it entails a complexity of O(2M), where M is the observation window size. To this end, SDR is employed to reformulate the GLRT detection into a semidefinite programming (SDP) model, and two detectors, randomization-SDR (RSDR) and eigenvector-SDR (ESDR) are proposed on the order of O(M3.5) and O(M3), respectively. Complexity analysis validates that the SDR-MSD strategy is desirable owing to its reduced complexity, compared with the exponential-complexity sphere decoding (SD) MSD. Furthermore, Monte-Carlo simulations demonstrate that the proposed SDR detectors provide the bit error rate (BER) performance almost the same with that of the SD method, and the RSDR outperforms the ESDR at the price of slightly higher complexity.

Large-scale multi-cell multiuser multiple-input multiple-output (LS-MIMO) systems can greatly increase the spectral efficiency. But the performance of these systems is deteriorated by pilot contamination. In this paper, first, a pilot design criterion is proposed by exploiting the orthogonality of channel vectors of LS-MIMO systems. Second, following this criterion, Chu sequences based pilots are designed. Because of the proposed pilots, the channel estimate of most terminals of a cell is only interfered by the partial cells rather than all the other cells, where the latter is caused by traditional pilots. As a result, pilot contamination is mitigated. Numerical results verify the effectiveness of the proposed pilots.

Large-scale multi-cell multiuser multiple-input multiple-output (LS-MIMO) systems have received much attention recently. But the performance of these systems is deteriorated by imperfect channel state information (CSI). Hence, in this paper, a subspace-based semi-blind channel estimator is proposed. Based on the approximate orthogonality of the channel vectors of LS-MIMO systems, singular value decomposition (SVD) is employed on the received signals to determine the channel matrix up to an ambiguity matrix. Then matrix inversion is avoided in resolving the ambiguity matrix, which is essential to traditional subspace-based estimators and loses the partial CSI. The properties of the estimators are analyzed, and the analysis shows that the estimation accuracy of the proposed estimator is improved. Simulations comparing the proposed approach with others illustrate improvement of the performance of the proposed approach.