Search

The multiple-input-multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) receiver aims to softly decode the transmitted information from the observed received signal. However, soft-output detection requires additional computation and leads to higher overall detection complexity in high-dimensional MIMO and higher-order modulation. Therefore, accurate and low-complexity soft-output detection is a challenging task in such systems. The conventional likelihood ascent search (LAS) detectors perform well in large antenna setups, however, multiple symbol updates and the soft-output estimation using brute force search further boost its complexity. In this paper, we propose a model-based soft-output LAS detector to jointly detect and precisely estimate the soft output in MIMO-OFDM systems. Furthermore, we propose a data-driven deep-LAS (Deep LAS) architecture for MIMO detection which is a multi-layer perception (MLP), and a gated recurrent unit (GRU)-based hybrid trainable learning framework to unfold the proposed two-stage LAS algorithm by directly learning the soft output from the received equalized signals. Numerical results demonstrate that the proposed two-stage soft-output LAS detector precisely computes the log-likelihood ratio (LLR) and provides better performance than the conventional LAS detector. Alternatively, the proposed Deep LAS efficiently estimates the LLR values by achieving a performance gain of 2.55 dB and 3 dB compared to the conventional LAS algorithm. Furthermore, the proposed Deep LAS outperforms the counterpart model-based and standalone data-driven learning schemes and provides a comparable signal-to-noise ratio (SNR) gap of 0.4 dB and 1.2 dB with the optimal soft output sphere decoding (SD) to achieve a BER of $ \text{10}^{-\text{5}}$ for 4-QAM and 16-QAM, respectively.

Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this article investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks.

Symbiotic communication (SC), exploiting the analogy of biological ecosystem to establish communication device ecosystems, can enable cooperative service/resource exchanges across heterogeneous devices thus realizing the complementarity among different communication resources. However, considering unstable wireless links, high network dynamics, and complex electromagnetic interference in such an ecosystem, it is difficult to perform service/resource exchanges without securing a trusted environment. Moreover, multi-dimensional service/resource exchange demanded by massive symbiotic devices (SDs) in the ecosystems exposes additional challenges for exchange decision-making. To deal with the above difficulties, in this article, we propose a blockchain-empowered intelligent coevolution for symbiotic devices (BIO-SD). Specifically, to guarantee the trustworthiness of service/resource exchange and resist malicious attacks, a direct acyclic graph (DAG)-based blockchain architecture is applied to the BIO-SD scheme. Furthermore, a modified multi-agent deep deterministic policy gradient (MADDPG) approach is adopted to make service/resource exchange decisions under this trusted environment. The simulation results show that the proposed BIO-SD scheme outperforms some conventional solutions in terms of transmission rate and transmission latency under both non-attack and malicious attack scenarios.