| Parameter | Value |
| Number of users, N | 5 |
| Number of channels, M | 5 |
| Channel bandwidth, W | 180 KHz |
| Noise power spectral density, N0 | -174 dBm/Hz |
| Pathloss model | 128.1+37.6lg[d(km)] dB |
| Shadow effect factor | 6 dB |
| Transmit power, pn | 10 dBm |
| Maximum number of symbols per word, K | 20 symbols/word |
| Semantic similarity threshold, ξth | 0.9 |
| S-SE threshold, Φth | 0.025(I/L) sut/s/Hz |
| Transforming factor, μ | 40 bits/word |
+
+5G system: Similar to 4G, the BS gets CQI based on the measured SNR [18], and then obtains the achievable SE $R _ { n , m }$ according to Table 5.2.2.1-2 in 3GPP TS 38.214.
+
+RNote that no scheme could achieve a higher bit transmission rate than the ideal system, but we focus on the S-SE to evaluate the performance in this letter. By adopting the developed transform method, the S-SE optimization problem of the above three benchmarks can be formulated as
+
+$$
+(\mathbf {P 4}) \max _ {\boldsymbol {\alpha} _ {n}} \sum_ {n = 1} ^ {N} \sum_ {m = 1} ^ {M} \alpha_ {n, m} \Phi_ {n, m} ^ {\prime \Delta} \tag {13}
+$$
+
+s.t. $\mathrm { C _ { 1 } , C _ { 2 } , C _ { 3 } }$
+
+$$
+\mathrm {C} _ {7}: \Phi_ {n, m} ^ {\prime \Delta} \geq \Phi_ {\mathrm {t h}}, \tag {13a}
+$$
+
+where Φ-Δ $\Phi _ { n , m } ^ { \prime \Delta }$ is the S-SE of the $n$ -th user over the $m$ -th channel in system $\Delta$ , $\Delta \in \{ \mathrm { I d e a l } , 4 \mathrm { G } , 5 \mathrm { G } \}$ . (P4) can be solved by the method introduced in Section III-C.
+
+# C. Simulation Results
+
+In our simulation, a circular network with radius $r = 5 0 0 \mathrm { m }$ is considered where $N$ users are distributed uniformly. Unless specifically stated, the relevant parameters are listed in Table I.
+
+We first examine the conventional resource allocation model in semantic-aware networks. In this simulation, the optimal channel assignment results of the conventional model in the ideal system is applied in the network, along with different values of $k _ { n }$ . Then the obtained S-SE is compared with that kof the proposed model. As shown in Fig. 3, the S-SE of the conventional model is smaller than that of the proposed model regardless of the value of $k _ { n }$ , which implies that the convenktional model is not suitable in semantic-aware networks. In addition, the S-SE of the conventional model with $k _ { n } ~ = ~ 3$ kis equal to 0 because the semantic similarity is less than the threshold in this case.
+
+
+
+
+
+(a) The S-SE versus the number of channels
+
+
+
+
+
+
+(b) The S-SE versus the transmit power.
+
+
+
+
+
+
+(c) The S-SE versus the transforming factor.
+
+
+In the following, we compare the different communication systems with the corresponding resource allocation model. Fig. 4(a) shows the S-SE of different systems versus the number of channels. When M is increased from 1 to 5, the S-SE of all systems increases rapidly because more users are served. Then when M keeps on increasing from 5 to 10, the S-SE grows slowly instead of remaining stable because more channels are available and users can choose the channel with higher SNR. Moreover, the semantic communication system outperforms all conventional communication systems.
+
+Fig. 4(b) illustrates the S-SE versus the transmit power. As $p _ { n }$ increases, the S-SE of the ideal system increases rapidly pwhile that of the semantic communication system, 4G system, and 5G system increase first and then tend to be a constant, implying that all practical systems have an upper bound with increasing SNR. Moreover, the semantic communication system shows a larger upper bound than 4G and 5G due to its stronger ability in compressing data.
+
+Fig. 4(c) shows the S-SE versus the transforming factor. The performance of the semantic communication system remains stable since the transforming factor is irrelevant to it. For the conventional systems, the S-SE decreases with increasing $\mu$ because the S-SE is the ratio of the SE to $\mu$ , and the maximum SE is a fixed value with different $\mu$ . Additionally, the semantic communication system yields better performance than both 4G and 5G when $\mu$ is larger than 19 bits/word. Nevertheless, when $\mu$ is smaller than approximately 27 bits/word, i.e., a word can be encoded to less than 27 bits, the semantic communication system performs worse than the ideal system. This figure demonstrates that whether semantic communication systems outperforms conventional ones to a great extent depends on the source coding scheme adopted in conventional systems.
+
+# V. CONCLUSION
+
+In this letter, we have studied the SE issue in the semantic domain and explored the resource allocation for semantic communications. Specifically, S-R and S-SE have been defined first to make it possible to measure the communication efficiency of the semantic communication system based on the DeepSC model. Aiming at maximizing the overall S-SE of all users, the semantic-aware resource allocation has been formulated as an optimization problem and the optimal solution has been obtained. Extensive simulation has been conducted to evaluate the performance of the proposed scheme. An insightful conclusion is that, for text transmission, semantic communication systems achieve a higher S-SE than both 4G and 5G systems when a word is mapped to more than 19 bits on average through conventional source coding techniques. Further, if
+
+the required bits for encoding a word is increased to more than 27 bits with 10 dBm transmit power, semantic communication systems even outperforms the ideal system. In the future, how to design resource allocation method to satisfy the requirements of multiple intelligence tasks including single modal and multimodal tasks should be further investigated.
+
+# REFERENCES
+
+
+
+[1] W. Tong and G. Y. Li, “Nine challenges in artificial intelligence and wireless communications for 6G,” Sep. 2021, arXiv: 2109.11320.
+
+
+
+
+
+[2] Z. Qin, X. Tao, J. Lu, and G. Y. Li, “Semantic communications: Principles and challenges,” Dec. 2021, arXiv: 2201.01389.
+
+
+
+
+
+[3] H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Trans. Signal Process., vol. 69, no. 1, pp. 2663–2675, Apr. 2021.
+
+
+
+
+
+[4] M. Sana and E. C. Strinati, “Learning semantics: An opportunity for effective 6G communications,” in Proc. IEEE 19th Annu. Consum. Commun. Netw. Conf. (CCNC), Las Vegas, NV, USA, Jan. 2022, pp. 631–636.
+
+
+
+
+
+[5] C.-H. Lee, J.-W. Lin, P.-H. Chen, and Y.-C. Chang, “Deep learningconstructed joint transmission-recognition for Internet of Things,” IEEE Access, vol. 7, pp. 76547–76561, 2019.
+
+
+
+
+
+[6] E. Bourtsoulatze, D. B. Kurka, and D. Gündüz, “Deep joint sourcechannel coding for wireless image transmission,” IEEE Trans. Cogn. Commun. Netw., vol. 5, no. 3, pp. 567–579, Sep. 2019.
+
+
+
+
+
+[7] Z. Weng and Z. Qin, “Semantic communication systems for speech transmission,” IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2434–2444, Aug. 2021.
+
+
+
+
+
+[8] T.-Y. Tung and D. Gündüz, “DeepWiVe: Deep-learning-aided wireless video transmission,” Nov. 2021, arXiv: 2111.13034.
+
+
+
+
+
+[9] M. Kountouris and N. Pappas, “Semantics-empowered communication for networked intelligent systems,” IEEE Commun. Mag., vol. 59, no. 6, pp. 96–102, Jun. 2021.
+
+
+
+
+
+[10] R. Carnap and Y. Bar-Hillel, “An outline of a theory of semantic information,” Res. Lab. Electron., Massachusetts Inst. Technol., Cambridge, MA, USA, RLE Rep. 247, Oct. 1952.
+
+
+
+
+
+[11] J. Bao et al., “Towards a theory of semantic communication,” in Proc. IEEE Netw. Sci. Workshop, West Point, NY, USA, Jun. 2011, pp. 110– 117.
+
+
+
+
+
+[12] F. M. J. Willems and T. Kalker, “Semantic compaction, transmission, and compression codes,” in Proc. Int. Symp. Inf. Theory (ISIT), Adelaide, SA, Australia, Sep. 2005, pp. 214–218.
+
+
+
+
+
+[13] H. Xie, Z. Qin, and G. Y. Li, “Task-oriented multi-user semantic communications for VQA,” IEEE Wireless Commun. Lett., vol. 11, no. 3, pp. 553–557, Mar. 2022.
+
+
+
+
+
+[14] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using siamese bert-networks,” in Proc. Empr. Methods Nat. Lang. Process. (EMNLP), Nov. 2019, pp. 3982–3992.
+
+
+
+
+
+[15] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “BLEU: A method for automatic evaluation of machine translation,” in Proc. Annu. Meeting Assoc. Comput. Linguist. (ACL), Philadelphia, PA, USA, Jul. 2002, pp. 311–318.
+
+
+
+
+
+[16] H. W. Kuhn, “The Hungarian method for the assignment problem,” Naval Res. Logist. Quart., vol. 2, pp. 83–97, Mar. 1955.
+
+
+
+
+
+[17] A. Ghosh and R. Ratasuk, Essentials of LTE and LTE-A. Cambridge, U.K.: Cambridge Univ. Press, 2011.
+
+
+
+
+
+[18] E. Chu, J. Yoon, and B. C. Jung, “A novel link-to-system mapping technique based on machine learning for 5G/IoT wireless networks,” Sensors, vol. 19, no. 5, p. 1196, Mar. 2019.
+
diff --git a/workspace/resource_allocation/analysis/image_understanding.md b/workspace/resource_allocation/analysis/image_understanding.md
new file mode 100644
index 0000000..eb644cc
--- /dev/null
+++ b/workspace/resource_allocation/analysis/image_understanding.md
@@ -0,0 +1,39 @@
+# Image Understanding
+
+## Summary
+- Total images: 6
+- Architecture diagrams: 1
+- Experiment figures: 5
+- Other: 0
+
+---
+
+## Figure 1: The structure of semantic-aware networks
+**Type**: Architecture
+**Priority**: LOW
+**Key insight**: Shows a base station communicating with multiple users. Each user generates semantic symbols via a neural network model from their devices before transmission.
+
+## Figure 2: The semantic similarity for DeepSC
+**Type**: Plot
+**Priority**: MEDIUM
+**Key insight**: 3D surface plot showing how semantic similarity ($\xi_{n,m}$) depends on SNR (-10 to 20 dB) and the number of symbols per word ($k_n$, 0 to 20). High SNR and higher $k_n$ lead to semantic similarity approaching 1.0.
+
+## Figure 3: The S-SE of the semantic-aware network with different models
+**Type**: Plot
+**Priority**: HIGH
+**Key insight**: Line plot showing S-SE ($\Phi$) vs Number of channels ($M$). The proposed model achieves the highest S-SE (plateauing at 1.2), significantly outperforming conventional models with various fixed $k_n$ values.
+
+## Figure 4(a): The S-SE versus the number of channels
+**Type**: Plot
+**Priority**: HIGH
+**Key insight**: Compares Semantic, Ideal, 5G, and 4G systems. Semantic achieves the highest S-SE (1.2 at $M \ge 5$), followed by Ideal, 5G, and 4G.
+
+## Figure 4(b): The S-SE versus the transmit power
+**Type**: Plot
+**Priority**: HIGH
+**Key insight**: S-SE vs Transmit power (-40 to 23 dBm). The Semantic system quickly rises and plateaus around 10 dBm, outperforming 4G and 5G. The Ideal system grows continuously and overtakes the Semantic system at very high transmit power (around 18-20 dBm).
+
+## Figure 4(c): The S-SE versus the transforming factor
+**Type**: Plot
+**Priority**: HIGH
+**Key insight**: S-SE vs Transforming factor $\mu$ (bits/word) from 18 to 40. Semantic performance is constant (~1.18). Ideal, 5G, and 4G S-SE decrease as $\mu$ increases. Semantic outperforms 5G and 4G for $\mu > 19$, and outperforms Ideal for $\mu > 27$.
diff --git a/workspace/resource_allocation/analysis/paper_structure.md b/workspace/resource_allocation/analysis/paper_structure.md
new file mode 100644
index 0000000..42927d2
--- /dev/null
+++ b/workspace/resource_allocation/analysis/paper_structure.md
@@ -0,0 +1,91 @@
+# Paper Structure Analysis
+
+## Basic Information
+- **Title**: Resource Allocation for Text Semantic Communications
+- **Authors**: Lei Yan, Zhijin Qin, Rui Zhang, Yongzhao Li, Geoffrey Ye Li
+- **Year**: 2022
+- **Venue**: IEEE Wireless Communications Letters
+
+## Abstract Summary
+This paper introduces semantic spectral efficiency (S-SE) as a new metric to measure communication efficiency from a semantic perspective. Taking text semantic communication (using DeepSC) as an example, the authors formulate and solve a resource allocation problem to maximize overall S-SE via channel assignment and semantic symbol length optimization. A transform method is also proposed for fair comparison between bit-based and semantic-based communication systems.
+
+## Problem Statement
+Conventional communications use bit-based spectral efficiency, which is not applicable for semantic communications as bits are irrelevant to the meaning of the source. Resource allocation needs to be rethought from the semantic perspective to maximize communication efficiency while guaranteeing transmission reliability in semantic-aware networks.
+
+## Key Contributions
+1. Proposing a novel resource allocation model for semantic-aware networks by defining Semantic Spectral Efficiency (S-SE) for the first time.
+2. Formulating and solving an optimization problem to maximize overall S-SE in terms of channel assignment and the number of transmitted semantic symbols.
+3. Developing a transform method to convert bit-based SE to S-SE to make fair comparisons between semantic and conventional communication systems.
+
+## Method Overview
+
+### Architecture
+The system consists of a cellular network with a base station and multiple users. DeepSC is adopted as the semantic communication model for text transmission, utilizing Transformer architecture to map sentences to semantic symbols. The symbol vector length varies based on the sentence length and the average number of semantic symbols per word ($k_n$). The receiver decodes the symbols using a channel decoder and semantic decoder, evaluated by BERT-level semantic similarity.
+Reference to `Figure 1: The structure of semantic-aware networks` from `image_understanding.md`.
+
+### Key Components
+| Component | Description | Implementation Priority |
+|-----------|-------------|------------------------|
+| Semantic Spectral Efficiency (S-SE) Metric | Defines the effectively transmitted semantic information over a unit of bandwidth. | High |
+| DeepSC Transmitter/Receiver | Transformer-based semantic encoder/decoder. | Low (Pre-trained look-up table used) |
+| Resource Allocation Optimizer | Solves for optimal $k_n$ and channel assignment $\alpha_{n,m}$ using exhaustive search and the Hungarian algorithm. | High |
+| Transform Method & Baselines | Converts conventional bit-based SE to S-SE based on a transforming factor $\mu$ (bits/word). Evaluates Ideal, 4G, and 5G baselines. | High |
+
+### Mathematical Formulation
+S-R (Semantic transmission rate) and S-SE (Semantic spectral efficiency):
+
+$$
+\Gamma_{n, m} = \frac{W I}{k_n L} \xi_{n, m}
+$$
+
+$$
+\Phi_{n, m} = \frac{\Gamma_{n, m}}{W} = \frac{I}{k_n L} \xi_{n, m}
+$$
+
+Optimization Objective (P0/P1) to maximize total S-SE:
+
+$$
+\max_{\boldsymbol{\alpha}_n, k_n} \widetilde{\Phi} = \sum_{n=1}^{N} \sum_{m=1}^{M} \alpha_{n, m} \frac{\xi_{n, m}}{k_n}
+$$
+
+### Training Details
+- **Optimizer**: DeepSC is pre-trained; the resource allocation uses Hungarian algorithm & exhaustive search.
+- **Hardware**: Assumed pre-trained at the BS or cloud platforms.
+- **Note**: The paper abstracts the DeepSC performance into a mapping between $\xi_{n,m}$ (semantic similarity), $k_n$, and SNR $\gamma_{n,m}$ over an AWGN channel.
+
+## Experiments
+
+### Datasets
+| Dataset | Size | Purpose |
+|---------|------|---------|
+| Synthetic Cellular Network | N=5, M=5 (default) | Resource allocation optimization simulation |
+| Text dataset (implicit) | N/A | To obtain DeepSC semantic similarity performance look-up table |
+
+### Metrics
+- **Semantic similarity ($\xi$)**: Evaluated using pre-trained Sentence-BERT model.
+- **Semantic Spectral Efficiency (S-SE)**: Measured in suts/s/Hz.
+- **Transforming factor ($\mu$)**: Measured in bits/word.
+
+### Key Results
+- The proposed resource allocation model maximizes S-SE, significantly outperforming conventional models with fixed $k_n$.
+- The semantic communication system achieves higher S-SE than 4G and 5G systems for text transmission when the transforming factor $\mu > 19$ bits/word.
+- It outperforms the ideal Shannon limit system when $\mu > 27$ bits/word.
+Reference to Figure 3, Figure 4a, Figure 4b, Figure 4c from `image_understanding.md`.
+
+## Appendix Notes
+No supplementary material findings explicitly stated, but baseline implementation details refer to 3GPP TS 36.213 (4G) and TS 38.214 (5G) tables for CQI to SE mapping.
+
+## Data Source Labeling
+
+### Figure 3: S-SE with different models
+| Data Point | Value | Source | Reliability |
+|------------|-------|--------|-------------|
+| S-SE Proposed Model Plateau | ~1.2 | Image extraction | REFERENCE ONLY |
+
+### Figure 4a, 4b, 4c: Comparison curves
+| Data Point | Value | Source | Reliability |
+|------------|-------|--------|-------------|
+| S-SE Semantic (M>=5) | ~1.2 | Image extraction | REFERENCE ONLY |
+| Semantic transmit power plateau | ~10 dBm | Image extraction | REFERENCE ONLY |
+| $\mu$ cross point (Semantic vs 5G/4G) | 19 | Paper text, Section IV.C | HIGH |
+| $\mu$ cross point (Semantic vs Ideal) | 27 | Paper text, Section IV.C | HIGH |
diff --git a/workspace/resource_allocation/analysis/reference_images/fig2.png b/workspace/resource_allocation/analysis/reference_images/fig2.png
new file mode 100644
index 0000000000000000000000000000000000000000..0d7626689c1a19ef5576f638a1ba9402d22e9581
GIT binary patch
literal 331846
zcmeEti$7EUAAdKWltL*gu~f)C2_g20Zpi(9uN1l84I4@>Q79~xTke-(ZZpiRD00tb
zZnKS$>r7*tnal6c_xt<(55LFP<58VFwzISM<@LP1-m!-I_l_SucXZ#reaE%$-!|U2
zk7In_zC#t9{{X+4^ndjS_(#S6j-|hemy