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International audience; This work aims to design protocol sequences through deep reinforcement learning (DRL). Protocol sequences are periodic binary sequences that define multiple access control among users, introduced for systems considering collision channel without feedback (CCw/oFB). In this paper, we leverage the recent advancement of DRL methods to design protocol sequences with desirable new properties, namely Throughput Maximizing User-Irrepressible (TMUI) sequences. TMUI has two specific properties: (i) user-irrepressibility (UI), and (ii) maximizing the minimum individual throughput among the users. We assumed that the transmission channel is divided into time slots and the starting time of each user in joining the system is arbitrary such that there exist random relative time offsets. We use a DRL approach to find TMUI sequences. We report the obtained TMUI protocol sequences and conduct numerical studies comparing TMUI against slotted ALOHA. Simulation results also show that the new medium access control (MAC) protocol does hold the UI property and can achieve substantially higher minimum individual user throughput, under the same system parameters.

Coded Slotted ALOHA (CSA) is a random access scheme based on the application of packet erasure correcting codes to transmitted packets and the use of successive interference cancellation at the receiver. CSA has been widely studied and a common assumption is that interference cancellation can always be applied perfectly. In this paper, we study the design of CSA protocol, accounting for a non-zero probability of error due to imperfect interference cancellation (IC). A classical method to evaluate the performance of such protocols is density evolution, originating from coding theory, and that we adapt to our assumptions. Analyzing the convergence of density evolution in asymptotic conditions, we derive the optimal parameters of CSA, i.e., the set of code selection probabilities of users that maximizes the channel load. A new parameter is introduced to model the packet loss rate of the system, which is non-zero due to potential IC errors. Multi-packet reception (MPR) and the performance of 2-MPR are also studied. We investigate the trade-off between optimal load and packet loss rate, which sheds light on new optimal distributions that outperform known ones. Finally, we show that our asymptotic analytical results are consistent with simulations obtained on a finite number of slots.

International audience; We develop a framework for maximizing the number of transmitted packets for devices in a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA) in the downlink. The base station (BS) chooses one of the multiple available physical resource blocks (PRBs) that are well separated in frequency for a device, giving them the advantage of exploiting frequency diversity. The scheduling strategy focuses on the twofold problem involving efficient device clustering and optimum power allocation. This problem is a mixed-integer non-convex problem. We propose a bipartite graph matching approach, termed minimum weight full matching with pruning (MWFMP), to address the problem over multiple PRBs and solve it under the quality-of-service (QoS), allowable PRB, power budget, and interference constraints. Additionally, we provide a comparison with a greedy heuristic, the multi-PRB stratified device allocation (MPSDA), where we extend our previous work for a single PRB connectivity problem. Furthermore, we compare our algorithms to orthogonal multiple access (OMA) scheduling, which is prevalent in legacy LTE networks. We show that our algorithms steadily outperform the connectivity performance offered by OMA.

International audience; We address the issue of maximizing the number of connected devices in a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA) in the downlink. We first propose an optimal joint sub-carrier and power allocation strategy assuming perfect channel state information (CSI) called Stratified Device Allocation (SDA), that maximizes the connectivity under data rate, power and bandwidth constraints. Then, we generalize the connectivity maximization problem to the case of partial CSI, where only the distancedependent path-loss component of the channel gain is available at the base station (BS). We introduce a novel framework called the Stochastic Connectivity Optimization (SCO) framework. In this framework, we propose a heuristic improvement to SDA namely SDA with Excess Power (SDA-EP) algorithm for operation under partial CSI. Furthermore, we derive a concave approximation (SCO-CA) algorithm of near-optimal performance to SCO given the same amount of CSI. Through computer simulations, we show that SDA-EP and SCO-CA outperform conventional NOMA and OMA schemes in the presence of partial CSI over a wide range of service scenarios.

Chromatin insulators are DNA-protein complexes that can prevent the spread of repressive chromatin and block communication between enhancers and promoters to regulate gene expression. In Drosophila, the gypsy chromatin insulator complex consists of three core proteins: CP190, Su(Hw), and Mod(mdg4)67.2. These factors concentrate at nuclear foci termed insulator bodies, and changes in insulator body localization have been observed in mutants defective for insulator function. Here, we identified NURF301/E(bx), a nucleosome remodeling factor, as a novel regulator of gypsy insulator body localization through a high-throughput RNAi imaging screen. NURF301 promotes gypsy-dependent insulator barrier activity and physically interacts with gypsy insulator proteins. Using ChIP-seq, we found that NURF301 co-localizes with insulator proteins genome-wide, and NURF301 promotes chromatin association of Su(Hw) and CP190 at gypsy insulator binding sites. These effects correlate with NURF301-dependent nucleosome repositioning. At the same time, CP190 and Su(Hw) both facilitate recruitment of NURF301 to chromatin. Finally, Oligopaint FISH combined with immunofluorescence revealed that NURF301 promotes 3D contact between insulator bodies and gypsy insulator DNA binding sites, and NURF301 is required for proper nuclear positioning of gypsy binding sites. Our data provide new insights into how a nucleosome remodeling factor and insulator proteins cooperatively contribute to nuclear organization.