We consider a distributed reconfigurable intelligent surfaces (RISs) enabled MIMO system with a transmitter and a receiver. In this work, we find the optimal precoding matrix at the transmitter and the optimal phase shifts at the multiple RISs, under a transmit power constraint. The problem under consideration is non-convex, and in the recent literature, deep reinforcement learning (DRL) based methods have shown promising results in such scenarios. Our solution is particularly based on the soft actor-critic (SAC) algorithm of DRL. Our innovative Markov Decision Process (MDP) formulation for the problem leads to better rate compared with the prevalent formulations. Our simulation results show that SAC can improve the rate considerably starting from a random value. For fair comparison, we consider deep deterministic policy gradient (DDPG) and alternating optimization (AO) as benchmarks. Our contribution also includes a new way of handling each episode which leads to stable training.

Couchbase Capella is a scalable document-oriented database service in the cloud. Its existing Capella Operational service is based on a shared-nothing architecture and supports high volumes of low-latency queries and updates for JSON documents. Its new Capella Columnar cloud service complements the Operational service. The Capella Columnar service supports complex analytical queries (e.g., ad hoc joins and aggregations) over large collections of JSON documents that can originate from a variety of Couchbase and non-Couchbase data sources and formats and can either be stored and managed by the Capella Columnar service or externally stored and accessed on demand at query time. This paper describes the new Capella Columnar service, looking both over and under the hood.

Application of Internet of Things (IoT) in health, defense, banking, and other confidential information transfer urges the need for secure IoT. As most of the IoT devices are resource-limited (antennas, bandwidth, energy), securing the information transfer has always been a challenge. Looking at a solution for enhancing the security of single antenna, single carrier, energy efficient devices, we propose a novel scheme, channel-based mapping diversity. This scheme uses the inherent randomness of the wireless channel and multiple mappings available for an M-ary phase shift keying constellation in confusing an eavesdropper. When the legitimate and the eavesdropper channels are independent of each other, it is shown that a symbol error rate (SER) of (M-1)/ M is induced at the eavesdropper. Whereas, when the channels are correlated, optimal and suboptimal strategies at source and eavesdropper are derived for their respective optimal performances. Further, a closed-form expression for a lower-bound on the SER at the eavesdropper is derived. Simulation results show that for the correlated case, as SNR at the eavesdropper increases, SER initially decreases, later saturates to a relatively high SER, hence making the job of the eavesdropper difficult in getting the legitimate data. Furthermore, the effect of the correlation is more pronounced on SER at higher levels of correlation. This indicates that for practical correlation scenarios, SER is high enough to confuse the eavesdropper.

In this paper, we consider a distributed MIMO system with a set of geographically distributed nodes forming a multi-antenna source S, and a set of geographically distributed full duplex (FD) nodes forming a multi-antenna destination D. S and D each forms a transmit and a receive cluster, respectively. D uses FD mode to jam the signal from S at eavesdropper E, incurring some residual self-interference. We assume that neither S nor D has knowledge of their instantaneous channel state information to E. Furthermore, to simplify the system design, especially for the mobile case, we assume as well that S has no knowledge of the ICSI of its channel to D. Hence, adopting ergodic secrecy rate (ESR) as the performance metric, we are interested in evaluating the ESR averaged over the nodes' locations, i.e., cluster averaged ESR. To accomplish this, we obtain a lower bound (LB) and an upper bound of the ESR. As part of our work, we also obtain bounds on the cluster averaged ergodic rate between the two clusters. We demonstrate by simulations that the LB can act as a good approximation of the ESR for practical purposes.

Demand of minimal hardware requirement and low energy consumption for internet of things (IoT) has gained the importance of techniques enhancing the physical layer security. Looking at this, we propose a channel-based mapping diversity (CBMD) scheme for a source (S), destination (D) pair to increase the average bit error rate (ABER) at an eavesdropper (E). CBMD leads to an ABER of 0.5 at E when the channels between S - D and S - E are independent. In the case where S - D and S - E are correlated (due to the proximity of E at D), we derive an optimal strategy at E which leads to the best performance at E. We also derive an optimal strategy at S, assuming E uses its optimal strategy, thereby, considering the worst case scenario. A sub-optimal strategy is also proposed at S which does not require any statistical information about the link S - E and hence, has low implementation complexity. We analyze the variation of ABER at E with the signal to noise ratio (SNR) and the channel correlation coefficient. Our simulation results demonstrate that CBMD indeed leads to significant performance degradation at E even at moderate channel correlation for a properly chosen strategy.

In this paper, we consider the problem of resource allocation in the orthogonal frequency division multiple access system with single source and M untrusted users in presence of a friendly jammer. The jammer is used to improve either the weighted sum secure rate or the overall system fairness. The formulated optimization problem in both the cases is a mixed integer non-linear programming problem, belonging to the class of NP-hard. In the sum secure rate maximization scenario, we decouple the problem and first obtain the subcarrier allocation at source and the decision for jammer power utilization on a per-subcarrier basis. Then, we do joint source and jammer power allocation using primal decomposition and alternating optimization framework. Next, we consider fair resource allocation by introducing a novel concept of subcarrier snatching with the help of jammer. We propose two schemes for jammer power utilization, called proactively fair allocation (PFA) and on-demand allocation (ODA). PFA considers equitable distribution of jammer power among the subcarriers, while ODA distributes jammer power based on the user demand. In both cases of jammer usage, we also present suboptimal solutions that solve the power allocation at a highly reduced complexity. Asymptotically optimal solutions are derived to benchmark optimality of the proposed schemes. We compare the performance of our proposed schemes with equal power allocation at source and jammer. Our simulation results demonstrate that the jammer can indeed help in improving either the sum secure rate or the overall system fairness.

We consider the problem of resource allocation among M untrusted mobile users with a source and a friendly jammer in a secure OFDMA system. The jammer can be utilized to improve either the sum secure rate or the fairness of the system. Since the optimization problem is combinatorial and belongs to the class of NP hard, we propose two independent suboptimal schemes to solve the resource allocation problem. Our simulation results demonstrate that the use of jammer helps trade between secure rate and fairness performance.

User-perceived quality-of-experience (QoE) is critical in Internet video applications as it impacts revenues for content providers and delivery systems. Given that there is little support in the network for optimizing such measures, bottlenecks could occur anywhere in the delivery system. Consequently, a robust bitrate adaptation algorithm in client-side players is critical to ensure good user experience. Previous studies have shown key limitations of state-of-art commercial solutions and proposed a range of heuristic fixes. Despite the emergence of several proposals, there is still a distinct lack of consensus on: (1) How best to design this client-side bitrate adaptation logic (e.g., use rate estimates vs. buffer occupancy); (2) How well specific classes of approaches will perform under diverse operating regimes (e.g., high throughput variability); or (3) How do they actually balance different QoE objectives (e.g., startup delay vs. rebuffering). To this end, this paper makes three key technical contributions. First, to bring some rigor to this space, we develop a principled control-theoretic model to reason about a broad spectrum of strategies. Second, we propose a novel model predictive control algorithm that can optimally combine throughput and buffer occupancy information to outperform traditional approaches. Third, we present a practical implementation in a reference video player to validate our approach using realistic trace-driven emulations.

We evaluate the bounds on the secrecy outage probability of a dual hop Amplify-and-forward (AF) system with an eavesdropper tapping the second hop, when all the links undergo Rayleigh fading. An approximate expression of outage is also obtained when the signal-to-noise ratio of the first hop is high. Based on the developed outage expressions we evaluate the bounds and the approximate outage for optimal relay selection when full instantaneous channel state information (FCSI) i.e. of all the links is available. We also propose a novel relay selection scheme when only statistical channel state information (SCSI) of all the links is available. The proposed scheme can be used in networks where feedback of FCSI to the decision making node is power intensive and CSI of the eavesdropper cannot be obtained at all instants. In the results section we show that the approximate expression follows the simulations exactly and the outage for relay selection in FCSI decreases with an increase in the number of relays.