In recent years, quasi-complementary sequence sets (QCSSs) have attracted widespread attention as they can support more users in MC-CDMA communications than perfect complementary sequence sets (PCSSs). The objective of this paper is to present three novel constructions of asymptotically optimal or near-optimal periodic QCSSs based on algebraic methods. Firstly, we propose a generic constriction of QCSSs with small alphabet sizepfrom polynomials over finite fields. Using the quadratic and cubic polynomials, we then respectively derive an infinite family of asymptotically optimal QCSSs and an infinite family of asymptotically near-optimal periodic QCSSs with large set sizes. Secondly, we give a construction of periodic QCSSs based on Gaussian sums which have smaller periodic tolerance than that of a known family of QCSSs. Thirdly, we present a construction of periodic QCSSs from permutation polynomials and complementary sets, yielding an infinite family of QCSSs with large set size, small periodic tolerance and low column sequence peak-to-average power ratio (PAPR).

The bit error rate (BER) directly determines the quality of wireless communication transmission. Traditional demodulators are limited in operating on burst signals and exhibit poor BER performance in low signal-to-noise ratio (SNR) conditions. For real-world burst signals, symbol-by-symbol approaches fail to capture inter-symbol dependencies, and existing end-to-end frameworks cannot handle the variable output lengths required for burst signals. To address this issue, we propose an end-to-end demodulation framework based on deep learning (DL), in which detection, recognition, channel compensation, and demodulation stages were trained as a unified system, enabling the entire signal burst to be demodulated in a single operation during inference. The framework's generalization and robustness are enhanced by a proposed masking mechanism and a denoising autoencoder (DAE), respectively. The former dynamically adjusts the output bitstream length while preventing gradient flow from redundant components, and the latter compensates for channel fading effects. We further introduce a dedicated end-to-end training strategy to optimize the adaptation between these modules. Experimental results on real-world Frequency Shift Keying (FSK), Minimum Shift Keying (MSK), Phase-Shift Keying (PSK), and Quadrature Amplitude Modulation (QAM) signals demonstrate that the proposed framework achieves superior demodulation accuracy for long-sequence burst signals. Compared to existing methods, the proposed framework enables parallel demodulation, and dynamically adapts the output bit stream in terms of varying message types and lengths.

In this letter, we first propose a construction of aperiodic binary Z-complementary sequence sets (ZCSSs) with length 3·2memploying Boolean functions. Next we present a construction of binary ZCSSs with large set sizes by using orthogonal sequences. At last, we provide a large class of optimal binary ZCSSs of length 2m·L with large zero correlation zone width, where L is the length of Z-complementary pairs. The lengths of the ZCSSs are more flexible than the known methods. In this letter, these systematic constructions can generate more ZCSSs of the new sequence lengths and set sizes, which have not been reported before.

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The lengths of binary Golay complementary pairs are limited to 2α10β26γ, where α, ß, γ ∈ {0, 1, 2, . . .}. This weakness is repaired by the Z-complementary pairs (ZCPs). In this letter, by concatenating several different complementary pairs of length 2m, we present a construction of even-period binary ZCPs of length N = 2m+ 3+ 2m+ 2+ 2m+ 1with zero-correlation zone (ZCZ) width Z = 2m+ 3. We also propose another construction of ZCPs of length N' with larger ZCZ width Z = 6N'/7.

In this paper, we employ the so-called semi-bent functions to achieve significant improvements over currently known methods, regarding the number of orthogonal sequences per cell that can be assigned to a regular tessellation of hexagonal cells, typical for certain code-division multiple-access systems. Our initial design method generates a large family of orthogonal sets of sequences derived from vectorial semi-bent functions. A modification of the original approach is proposed to avoid a hard combinatorial problem of allocating several such orthogonal sets to a single cell of a regular hexagonal network, while preserving the orthogonality to adjacent cells. This modification increases the number of users per cell by starting from shorter codewords and then extending the length of these codewords to the desired length. The specification and assignment of these orthogonal sets to a regular tessellation of hexagonal cells have been solved, regardless of the parity and size of m (where 2mis the length of the codewords). In particular, when the re-use distance is D = 4, the number of users per cell is 2m-2for almost all m, which is twice as many as can be obtained by the best known methods.

Mobile video service consumes more energy than other services from the perspective of data transmitting and user-side device energy consumption. Research on energy efficiency of mobile video service is of vital significance in achieving a green communication system. This paper aims to increase energy efficiency for mobile video systems based on the characteristics of QoE. Considering that user's QoE is constrained by factors like devices and ambient environment, a QoE-driven adaptive streaming scheme is proposed in order to help save radio resources and reduce power consumption. After that, a simple QoE model and several power models relevant to mobile video service are formulated. Based on these models, a QoE-driven energy efficient resource allocation algorithm is introduced. Simulation results demonstrate that the proposed algorithm can achieve superior performance in both QoE and energy efficiency, especially for cell-edge users.