In secure distributed matrix multiplication (SDMM) the multiplication$\boldsymbol A \boldsymbol B$from two private matrices$\boldsymbol A$and$\boldsymbol B$is outsourced to$N$distributed servers. In$\ell $-SDMM, the goal is to design a joint communication-computation procedure that optimally balances conflicting communication and computation metrics without leaking any information on both$\boldsymbol A$and$\boldsymbol B$to any set of$\ell {< }N$servers. To this end, the user applies coding with$\tilde { \boldsymbol A}_{i}$and$\tilde { \boldsymbol B}_{i}$representing encoded versions of$\boldsymbol A$and$\boldsymbol B$destined to the$i$-th server. Now, SDMM involves multiple tradeoffs. One such tradeoff is the tradeoff between upload (UL) and download (DL) costs. To find a good balance between these two metrics, we propose two schemes which we term USCSA and GSCSA that are based on secure cross subspace alignment (SCSA). We show that there are various scenarios where they outperform existing SDMM schemes from the literature with respect to UL-DL efficiency. Next, we implement schemes from the literature, including USCSA and GSCSA, and test their performance on Amazon EC2. Our numerical results show that USCSA and GSCSA establish a good balance between the time spent on the communication and computation in SDMMs. This is because they combine advantages of polynomial codes, namely low time for the upload of$\tilde { \boldsymbol A}_{i}$and$\tilde { \boldsymbol B}_{i}$,$i\in [1:N]$, and the computation of$\boldsymbol O_{i}=\tilde { \boldsymbol A}_{i}\tilde { \boldsymbol B}_{i}$, with those of SCSA, being a low timing overhead for the download of$\boldsymbol O_{i}$,$i\in [1:N]$, and the decoding of$\boldsymbol A \boldsymbol B$.

In secure distributed matrix multiplication (SDMM) the multiplication AB from two private matrices A and B is outsourced by a user to N distributed servers. In ℓ-SDMM, the goal is to design a joint communication-computation procedure that optimally balances conflicting communication and computation metrics without leaking any information on both A and B to any set of ℓ ≤ N servers. To this end, the user applies coding with Ãiand B̃irepresenting encoded versions of A and B destined to the i-th server. Now, SDMM involves multiple tradeoffs. One such tradeoff is the tradeoff between uplink (UL) and downlink (DL) costs. To find a good balance between these two metrics, we propose two schemes which we term USCSA and GSCSA that are based on secure cross subspace alignment (SCSA). We implement schemes from the literature, in addition to USCSA and GSCSA, and test their performance on Amazon EC2. Our numerical results show that USCSA and GSCSA establish a good balance between the time spend on the communication and computation in SDMMs. This is because they combine advantages of polynomial codes, namely low time for the upload of (Ãi, B̃i)i=1Nand the computation of Oi= Ãi, B̃i, with those of SCSA, being a low timing overhead for the download of (Oi)i=1Nand the decoding of AB.

Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of a large-scale matrix multiplication is typically outsourced to multiple servers. However, due to data misusage at the servers, security is typically of concern. In this paper, we study the two-sided secure matrix multiplication problem, where a user is interested in the matrix product AB of two finite field private matrices A and B. In this problem, the user exploits the computational resources of N servers to compute the matrix product, but simultaneously tries to conceal the private matrices from the servers. Our goal is to maximize the communication rate while preserving security, where we allow for up to ℓ ≤ N servers to collude. To this end, we propose a general aligned secret sharing and matrix partition scheme for which we optimize the partition of matrices A and B as a function of N and ℓ in order to maximize the achievable rate. A proposed inductive approach gives us an analytical close-to-optimal solution which significantly outperforms the existing scheme of Chang and Tandon in terms of (i) communication rate, (ii) maximum tolerable number of colluding servers and (iii) computational complexity.

In this paper, we are concerned with indoor localization based on multiple-antenna channel measurements. Indoor localization is an active area of research due to its great importance in many applications. We propose a hybrid algorithm which combines the benefits of two techniques, namely signal processing and machine learning. We validate our algorithm based on real measurements acquired from two practical setups. Our approach shows a very promising performance in the IEEE CTW 2019 - Positioning Algorithm Competition where the algorithm achieves an accuracy within RMSE values below 10 cm. We further build a setup in another indoor environment, where the algorithm still proves a very good performance compared to state-of-the art techniques used in indoor localization tasks.

An emerging trend of next generation communication systems is to provide network edges with additional capabilities such as additional storage resources in the form of caches to reduce file delivery latency. To investigate this aspect, we study the fundamental limits of a cache-aided wireless network consisting of one central base station, M transceivers and K receivers from a latency-centric perspective. We use the normalized delivery time (NDT) to capture the per-bit latency for the worst-case file request pattern at high signal-to-noise ratios (SNR), normalized with respect to a reference interference-free system with unlimited transceiver cache capabilities. For various special cases that satisfy K+M≤4, we establish the optimal tradeoff between cache storage and latency. This is facilitated through establishing a novel converse (for arbitrary M and K) and an achievability scheme on the NDT. Our achievability scheme is a synergistic combination of multicasting, zero-forcing beamforming and interference alignment.

Multi-way and device-to-device (D2D) communications are currently considered for the design of future communication systems. Unmanned aerial vehicles (UAVs) can be effectively deployed to extend the communication range of D2D networks. To model the UAV-D2D interaction, we study a multi-antenna multi-way channel with two D2D users and an intermittently available UAV node. The performance in terms of sum-rate of various transmission schemes is compared. Numerical results show that for different ground environments, the scheme based on a combination of interference alignment, zero-forcing and erasure-channel treatment outperforms other schemes at low, medium and high SNRs and thus represents a viable transmission strategy for UAV-aided multi-way D2D networks.

Hybrid architectures are generally composed of a cyber cloud with additional support of edge caching. By utilizing the benefits associated with cloud computing and caching, powerful enhanced interference management techniques can be readily utilized - that among others - also results in low-latency transmission. In this paper, we study the impact of cloud and edge processing on the latency for a heterogenous network (HetNet) consisting of two users and two transmitters. We define an information-theoretic metric, the delivery time per bit (DTB), that captures the delivery latency. We establish bounds on the DTB as a function of cache size, backhaul capacity and wireless channel parameters. We show the optimality on the DTB for various channel regimes.

Stringent mobile usage characteristics force wireless networks to undergo a paradigm shift from conventional connection-centric to content-centric deployment. With respect to 5G, caching and heterogenous networks (HetNet) are key technologies that will facilitate the evolution of highly content-centric networks by facilitating unified quality of service in terms of low-latency communication. In this paper, we study the impact of transceiver caching on the latency for a HetNet consisting of a single user, a receiver and one cache-assisted transceiver. We define an information-theoretic metric, the delivery time per bit (DTB), that captures the delivery latency. We establish coinciding lower and upper bounds on the DTB as a function of cache size and wireless channel parameters; thus, enabling a complete characterization of the DTB optimality of the network under study. As a result, we identify cache beneficial and non-beneficial channel regimes.

The application domains of civilian unmanned aerial systems (UASs) include agriculture, exploration, transportation, and entertainment. The expected growth of the UAS industry brings along new challenges: Unmanned aerial vehicle (UAV) flight control signaling requires low throughput, but extremely high reliability, whereas the data rate for payload data can be significant. This paper develops UAV number projections and concludes that small and micro UAVs will dominate the US airspace with accelerated growth between 2028 and 2032. We analyze the orthogonal frequency division multiplexing (OFDM) waveform because it can provide the much needed flexibility, spectral efficiency, and, potentially, reliability and derive suitable OFDM waveform parameters as a function of UAV flight characteristics. OFDM also lends itself to agile spectrum access. Based on our UAV growth predictions, we conclude that dynamic spectrum access is needed and discuss the applicability of spectrum sharing techniques for future UAS communications.