The rising demand for diverse service requirements under limited resources necessitates designing codeword indicators with variable distance metrics to exploit prior information, such as data rate, coding schemes, and Signal-to-Noise Ratio (SNR). Traditional codebook designs that maximize the minimum Hamming distance provide a global lower bound but often overlook the specific distance requirements of individual codewords. To address this limitation, the weighted Hamming distance metric is introduced, prioritizing codewords with varying distance attributes. Maximizing the weighted Hamming distance leads to the development of hierarchical codebooks, enabling the selection of codewords tailored to specific distance needs. Building on the classical Plotkin bound, an upper bound is derived, revealing trade-offs among the number of codewords, distance layers, and lengths. Optimal hierarchical codebooks are designed using a cyclotomy structure to maintain the desired Hamming distance properties and incorporate a heuristic algorithm to allow flexibility in codebook length and multi-layer attributes. In ultra-wideband multi-service scenarios, allocating higher-distance codewords to lower-SNR services enhances overall performance by matching codeword distances to service demands. Simulations demonstrate that, informed by deep learning-based SNR predictions, this approach reduces packet error rates and improves efficiency compared to existing IEEE 802.15.4z convolutional schemes and classical Hadamard codebooks.

In an increasing number of area inspection applications, such as powerline inspection and sewage disposal monitoring, Unmanned Aerial Vehicles (UAVs) are used for capturing and transmitting on-site videos. Existing UAV video compressions employ Advanced Video Coding (AVC) or High Efficiency Video Coding (HEVC) encoders to eliminate intra-frame and short-term inter-frame redundancy, while these methods still face challenges in achieving high compression efficiency due to the high captured video bitrate and limited transmission capacity. In this paper, we further consider that UAVs revisit the same area and capture videos from different viewpoints, hence the Long-term Historical Background Redundancy (LHBR) exists among revisited video clips. Thus, we leverage the LHBR caused by UAV revisits, and propose a high-efficiency aerial video compression for UAVs. Our method comprises three steps: Firstly, we propose a lightweight method based on a spatial correlation model to select the most correlated reference frames from historical video database. Then, we design a Historical Reference Background Frame (HBRF) generation algorithm by alternately using the keypoint-based and telemetry-assisted alignments to align the selected frames with current frame. Finally, we use the generated HBRF as a reference frame to eliminate the LHBR within I-frames. Our proposed method has been experimentally proven to reduce Bjøntegaard-Delta bitrate (BD-bitrate) by 42.83% or enhance Bjøntegaard-Delta Peak Signal-to-Noise Ratio (BD-PSNR) by 2.98 dB over original HEVC, and take 29.3% of the encoding time needed for existing LHBR based compressions.

In this work, we consider an Unmanned Aerial Vehicle (UAV)-aided covert transmission network, which adopts the uplink transmission of Communication Nodes (CNs) as a cover to facilitate covert transmission to a Primary Communication Node (PCN). Specifically, all nodes transmit to the UAV exploiting uplink non-Orthogonal Multiple Access (NOMA), while the UAV performs covert transmission to the PCN at the same frequency. To minimize the average age of covert information, we formulate a joint optimization problem of UAV trajectory and power allocation designing subject to multi-dimensional constraints including covertness demand, communication quality requirement, maximum flying speed, and the maximum available resources. To address this problem, we embed Signomial Programming (SP) into Deep Reinforcement Learning (DRL) and propose a DRL framework capable of handling the constrained Markov decision processes, named SP embedded Soft Actor-Critic (SSAC). By adopting SSAC, we achieve the joint optimization of UAV trajectory and power allocation. Our simulations show the optimized UAV trajectory and verify the superiority of SSAC compared with various existing baseline schemes. The results of this study suggest that by maintaining appropriate distances from both the PCN and CNs, one can effectively enhance the performance of covert communication by reducing the detection probability of the CNs.

When implementing helicopter $\mathrm{-}$satellite communications, periodical interruption of the received signal is a challenging problem because the communication antenna is intermittently blocked by the rotating blades of the helicopter. The helicopter $\mathrm{-}$satellite channel model and the Forward Error Control (FEC) coding countermeasure are presented in this paper. On the basis of this model, Check-Hybrid (CH) Low-Density Parity-Check (LDPC) codes are designed to mitigate the periodical blockage over the helicopter $\mathrm{-}$satellite channels. The CH-LDPC code is derived by replacing part of single parity-check code constraints in a Quasi-Cyclic LDPC (QC-LDPC) code by using more powerful linear block code constraints. In particular, a method of optimizing the CH-LDPC code ensemble by searching the best matching component code among a variety of linear block codes using extrinsic information transfer charts is proposed. Simulation results show that, the CH-LDPC coding scheme designed for the helicopter $\mathrm{-}$satellite channels in this paper achieves more than 25% bandwidth efficiency improvement, compared with the FEC scheme that uses QC-LDPC codes.

The design of a high-speed decoder using traditional partly parallel architecture for Non-Quasi-Cyclic (NQC) Low-Density Parity-Check (LDPC) codes is a challenging problem due to its high memory-block cost and low hardware utilization efficiency. In this paper, we present efficient hardware implementation schemes for NQC-LDPC codes. First, we propose an implementation-oriented construction scheme for NQC-LDPC codes to avoid memory-access conflict in the partly parallel decoder. Then, we propose a Modified Overlapped Message-Passing (MOMP) algorithm for the hardware implementation of NQC-LDPC codes. This algorithm doubles the hardware utilization efficiency and supports a higher degree of parallelism than that used in the Overlapped Message Passing (OMP) technique proposed in previous works. We also present single-core and multi-core decoder architectures in the proposed MOMP algorithm to reduce memory cost and improve circuit efficiency. Moreover, we introduce a technique called the cycle bus to further reduce the number of block RAMs in multi-core decoders. Using numerical examples, we show that, for a rate-2/3, length-15360 NQC-LDPC code with 8.43-dB coding gain for Binary Phase-Shift Keying (BPSK) in an Additive White Gaussian Noise (AWGN) channel, the decoder with the proposed scheme achieves a 23.8%–52.6% reduction in logic utilization per Mbps and a 29.0%–90.0% reduction in message-memory bits per Mbps.

In this study, a class of Generalized Low-Density Parity-Check (GLDPC) codes is designed for data transmission over a Partial-Band Jamming (PBJ) environment. The GLDPC codes are constructed by replacing parity-check code constraints with those of nonsystematic Bose-Chaudhuri-Hocquenghem (BCH), referred to as Low-Density Parity-Check (LDPC)-BCH codes. The rate of an LDPC-BCH code is adjusted by selecting the transmission length of the nonsystematic BCH code, and a low-complexity decoding algorithm based on message-passing is presented that employs A Posteriori Probability (APP) fast BCH transform for decoding the BCH check nodes at each decoding iteration. Simulation results show that the LDPC-BCH codes with a code rate of 1/8.5 have a bit error rate performance of 1 $\mathrm{\times }$10 ${}^{\mathrm{-}6}$ at signal-noise-ratios of $\mathrm{-}$6.97 dB, $\mathrm{-}$4.63 dB, and 2.48 dB when the fractions of the band jammed are 30%, 50%, and 70%, respectively.