Information Theory (cs.IT)

Abstract: This paper investigates the ultra reliable and low latency communication (URLLC) performance of the IRS-aided MIMO system. The upper and lower bounds of the optimal average error probability (OAEP) for the coding rate within 1/sqrt(Mn) of the capacity are derived, where n and M represent the blocklength and the number of transmit antennas, respectively. To achieve this goal, a new central limit theorem (CLT) for the mutual information density over the IRS-aided MIMO system is derived in the asymptotic regime where the block-length, the IRS size, and number of the antennas go to infinity with the same pace. The CLT is then utilized to derive the closed-form upper and lower bounds for the OAEP. Based on the analysis result, a gradient-based algorithm is proposed to minimize the lower bound of the OAEP by optimizing the phase shift of the IRS. Simulation results validate the fitness of the CLT and the effectiveness of the proposed algorithm in optimizing the theoretical bound, as well as the performance of practical LDPC code.

Abstract: The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks (NTN). This paper studies the problem of running a federated learning (FL) algorithm within a low Earth orbit (LEO) constellation of satellites connected with intra-orbit inter-satellite links (ISL). Satellites apply on-board machine learning and transmit the local parameters to the parameter server (PS). The main contribution is a novel approach to enhance FL in satellite constellations using intra-orbit ISLs. The key idea is to rely on the predictability of satellite visits to create a system design in which ISLs mitigate the impact of intermittent connectivity and transmit the aggregated parameters to the PS. We first devise a synchronous FL, which is then extended towards an asynchronous FL for the case of sparse satellite visits to the PS. An efficient use of the satellite resources is attained by sparsification-based compression the aggregated parameters of each orbit before forwarding to the PS. Performance is evaluated in terms of accuracy and the required size of data to be transmitted. The numerical results indicate a faster convergence rate of the presented approach compared with the state-of-the-art FL on satellite constellations.

Abstract: Intelligent reflecting surface (IRS) can be densely deployed in complex environments to create cascaded line-of-sight (LoS) links between base stations (BSs) and users, which significantly enhance the signal coverage. In this paper, we consider the wireless power transfer (WPT) from a multi-antenna BS to multiple energy users (EUs) by exploiting the signal beam routing via multi-IRS reflections. First, we present a baseline beam routing scheme with each IRS serving at most one EU, where the BS transmits wireless power to all EUs simultaneously while the signals to different EUs undergo disjoint sets of multi-IRS reflection paths. Under this setup, we aim to tackle the joint beam routing and resource allocation optimization problem by jointly optimizing the reflection paths for all EUs, the active/passive beamforming at the BS/each involved IRS, as well as the BS's power allocation for different EUs to maximize the minimum received signal power among all EUs. Next, to further improve the WPT performance, we propose two new beam routing schemes, namely dynamic beam routing and subsurface-based beam routing, where each IRS can serve multiple EUs via different time slots and different subsurfaces, respectively. In particular, we prove that dynamic beam routing outperforms subsurface-based beam routing in terms of minimum harvested power among all EUs. In addition, we show that the optimal performance of dynamic beam routing is achieved by assigning all EUs with orthogonal time slots for WPT. A clique-based optimization approach is also proposed to solve the joint beam routing and resource allocation problems for the baseline beam routing and proposed dynamic beam routing schemes. Numerical results are finally presented, which demonstrate the superior performance of the proposed dynamic beam routing scheme to the baseline scheme.

Abstract: Aiming at providing wireless communication systems with environment-perceptive capacity, emerging integrated sensing and communication (ISAC) technologies face multiple difficulties, especially in balancing the performance trade-off between the communication and radar functions. In this paper, we introduce a reconfigurable intelligent surface (RIS) to assist both data transmission and target detection in a dual-functional ISAC system. To formulate a general optimization framework, diverse communication performance metrics have been taken into account including famous capacity maximization and mean-squared error (MSE) minimization. Whereas the target detection process is modeled as a general likelihood ratio test (GLRT) due to the practical limitations, and the monotonicity of the corresponding detection probability is proved. For the single-user and single-target (SUST) scenario, the minimum transmit power of the ISAC transceiver has been revealed. By exploiting the optimal conditions of the BS design, we validate that the BS is able to realize the maximum power allocation scheme and derive the optimal BS precoder in a semi-closed form. Moreover, an alternating direction method of multipliers (ADMM) based RIS design is proposed to address the optimization of unit-modulus RIS phase shifts. For the sake of further enhancing computational efficiency, we also develop a low-complexity RIS design based on Riemannian gradient descent. Furthermore, the ISAC transceiver design for the multiple-users and multiple-targets (MUMT) scenario is also investigated, where a zero-forcing (ZF) radar receiver is adopted to cancel the interferences. Then optimal BS precoder is derived under the maximum power allocation scheme, and the RIS phase shifts can be optimized by extending the proposed ADMM-based RIS design. Numerical simulation results verify the performance of our proposed transceiver designs.

Abstract: We consider a gossip network consisting of a source generating updates and $n$ nodes connected in a two-dimensional square grid. The source keeps updates of a process, that might be generated or observed, and shares them with the grid network. The nodes in the grid network communicate with their neighbors and disseminate these version updates using a push-style gossip strategy. We use the version age metric to quantify the timeliness of information at the nodes. We find an upper bound for the average version age for a set of nodes in a general network. Using this, we show that the average version age at a node scales as $O(n^{\frac{1}{3}})$ in a grid network. Prior to our work, it has been known that when $n$ nodes are connected on a ring the version age scales as $O(n^{\frac{1}{2}})$, and when they are connected on a fully-connected graph the version age scales as $O(\log n)$. Ours is the first work to show an age scaling result for a connectivity structure other than the ring and fully-connected networks that represent two extremes of network connectivity. Our work shows that higher connectivity on a grid compared to a ring lowers the age experience of each node from $O(n^{\frac{1}{2}})$ to $O(n^{\frac{1}{3}})$.

Abstract: Storage codes on graphs are an instance of \emph{codes with locality}, which are used in distributed storage schemes to provide local repairability. Specifically, the nodes of the graph correspond to storage servers, and the neighbourhood of each server constitute the set of servers it can query to repair its stored data in the event of a failure. A storage code on a graph with $n$-vertices is a set of $n$-length codewords over $\field_q$ where the $i$th codeword symbol is stored in server $i$, and it can be recovered by querying the neighbours of server $i$ according to the underlying graph. In this work, we look at binary storage codes whose repair function is the parity check, and characterise the tradeoff between the locality of the code and its rate. Specifically, we show that the maximum rate of a code on $n$ vertices with locality $r$ is bounded between $1-1/n\lceil n/(r+1)\rceil$ and $1-1/n\lceil n/(r+1)\rceil$. The lower bound on the rate is derived by constructing an explicit family of graphs with locality $r$, while the upper bound is obtained via a lower bound on the binary-field rank of a class of symmetric binary matrices. Our upper bound on maximal rate of a storage code matches the upper bound on the larger class of codes with locality derived by Tamo and Barg. As a corollary to our result, we obtain the following asymptotic separation result: given a sequence $r(n), n\geq 1$, there exists a sequence of graphs on $n$-vertices with storage codes of rate $1-o(1)$ if and only if $r(n)=\omega(1)$.