Allocation de puissance pour les réseaux radio cognitifs à relais

International audienceThis paper addresses a power allocation problem in a relay-aided cognitive network that is composed by a primary and secondary source/destination pair and where a relay helps the communication of the secondary network. The optimal power allocation is derived under a minimum quality of service (QoS) constraint when the relay performs Decode-and-Forward (DF) or Compress-and-Forward (CF). Numerical simulations show that DF outperforms CF when the relay is close to the opportunistic user.Ce papier porte sur un problème d'allocation de puissance pour un réseau radio cognitif composé d'une paire utilisateur/destination primaire et secondaire ainsi que d'un relais aidant la communication entre l'utilisateur opportuniste et sa destination. Le système secondaire ne peut transmettre que si une contrainte de qualité de service est satisfaite. L'allocation de puissance optimale est présentée dans le cas où le relais emploie Decoder-et-Transférer (DF) ou Quantifier-et-Transférer (CF). Des résultats numériques montrent que DF est plus performant que CF lorsque le relais est proche de l'utilisateur opportuniste

On implementation aspects of decode and forward and compress and forward relay protocols

In this work, the common relay protocols Decode-and-Forward and Compress-and-Forward (CF) are investigated from a practical point of view: This involves on the one hand the impact of imperfections like channel and carrier phase stimation errors and on the other hand, the question of how to implement relay protocol specific signal processing like quantization for CF which is modeled in information theory simply by additive quantizer noise. To evaluate the performance, achievable rates are determined either numerically with the help of the Max-Flow Min-Cut theorem or by link level simulations.Diese Arbeit untersucht die Relay-Protokolle Decode-and-Forward und Compress-and-Forward (CF) mit dem Fokus auf einer praktischen Umsetzung. Es werden sowohl Störeinflüsse wie Kanal- und Phasenschätzfehler betrachtet als auch spezielle Kompressionsverfahren für das CF Protokoll implementiert. Von großer Bedeutung ist hier die Kompression in Form der Quantisierung, weil diese in der Informationstheorie lediglich durch Quantisierungsrauschen modelliert wird. Zur Auswertung der Leistungsfähigkeit der Protokolle werden die erzielbaren Raten entweder numerisch oder durch Simulation bestimmt

Adapting Language Models to Compress Contexts

Transformer-based language models (LMs) are powerful and widely-applicable tools, but their usefulness is constrained by a finite context window and the expensive computational cost of processing long text documents. We propose to adapt pre-trained LMs into AutoCompressors. These models are capable of compressing long contexts into compact summary vectors, which are then accessible to the model as soft prompts. Summary vectors are trained with an unsupervised objective, whereby long documents are processed in segments and summary vectors from all previous segments are used in language modeling. We fine-tune OPT models on sequences of up to 30,720 tokens and show that AutoCompressors can utilize long contexts to improve perplexity. We evaluate AutoCompressors on in-context learning by compressing task demonstrations. We find that summary vectors are good substitutes for plain-text demonstrations, increasing accuracy while reducing inference cost. Finally, we explore the benefits of pre-computing summary vectors for large corpora by applying summary vectors to retrieval-augmented language modeling. Overall, AutoCompressors emerge as a simple and inexpensive solution for extending the context window of LMs while speeding up inference over long contexts

Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning

“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv