Derya Malak

Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks

Machine-to-machine (M2M) communications severe power limitations challenge the interconnectivity, access management, and reliable communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy-efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the energy density of an M2M communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.

Optimal caching for device-to-device content distribution in 5G networks

Content distribution using direct device-to-device D2D communication is a promising approach for optimizing the utilization of air-interface resources in 5G network. Research in this area has indicated, perhaps unexpectedly, that the optimal caching distribution for D2D content does not match the distribution of user demands/requests. Therefore, in order to achieve the best utilization of network resources, the network should bias the distribution of cached content. The optimal caching distribution is dependent on, but not identical to, the content demand distribution. In this paper we model transmitting and receiving devices as member of a homogeneous Poisson Point Process (PPP), and use results from stochastic geometry to derive the probability of successful content delivery in the presence of D2D interference and noise. We formulate an optimization problem, and find the best caching distribution that maximizes the total probability of content delivery, assuming user demands/requests are modeled by a Zipf distribution with exponent γr. Our results show that the optimal caching distribution follows a similar trend as the demand distribution, and can also be modeled as a Zipf distribution. The exponent γc of the optimal caching distribution is related to γr through a simple expression involving the path loss exponent.

Optimizing Content Caching to Maximize the Density of Successful Receptions in Device-to-Device Networking

Device-to-device (D2D) communication is a promising approach to optimize the utilization of air interface resources in 5G networks, since it allows decentralized opportunistic short-range communication. For D2D to be useful, mobile nodes must possess content that other mobiles want. Thus, intelligent caching techniques are essential for D2D. In this paper, we use results from stochastic geometry to derive the probability of successful content delivery in the presence of interference and noise. We employ a general transmission strategy, where multiple files are cached at the users and different files can be transmitted simultaneously throughout the network. We then formulate an optimization problem, and find the caching distribution that maximizes the density of successful receptions (DSR) under a simple transmission strategy, where a single file is transmitted at a time throughout the network. We model file requests by a Zipf distribution with exponent γr, which results in an optimal caching distribution that is also a Zipf distribution with exponent γc, which is related to γr through a simple expression involving the path loss exponent. We solve the optimal content placement problem for more general demand profiles under Rayleigh, Ricean, and Nakagami small-scale fading distributions. Our results suggest that it is required to flatten the request distribution to optimize the caching performance. We also develop strategies to optimize content caching for the more general case with multiple files, and bound the DSR for that scenario.

Optimizing the spatial content caching distribution for device-to-device communications

We study the optimal geographic content placement problem for device-to-device (D2D) networks in which the content popularity follows the Zipf law. We consider a D2D caching model where the locations of the D2D users (caches) are modeled by a Poisson point process (PPP) and have limited communication range and finite storage. Unlike most related work which assumes independent placement of content, and does not capture the locations of the users, we model the spatial properties of the network including spatial correlation in terms of the cached content. We propose two novel spatial correlation models, the exchangeable content model and a Matérn (MHC) content placement model, and analyze and optimize the hit probability, which is the probability of a given D2D node finding a desired file at another node within its communication range. We contrast these results to the independent placement model, and show that exchangeable placement performs worse. On the other hand, MHC placement yields a higher cache hit probability than independent placement for small cache sizes.

Device-to-device content distribution: Optimal caching strategies and performance bounds

Content distribution using direct device-to-device (D2D) communication is a promising approach for optimizing the utilization of air-interface resources in 5G network. To achieve the best utilization of network resources, the network should bias the distribution of cached content. The optimal caching distribution depends on the content demand distribution. In this paper, we model the locations of transmitting and receiving devices by a homogeneous Poisson Point Process (PPP), and use results from stochastic geometry to derive the probability of successful content delivery in the presence of D2D interference and noise. We formulate an optimization problem to maximize the total probability of content delivery, assuming user demands are modeled by a Zipf distribution. We also develop strategies to optimize content caching, and bound the total coverage probability for multiple file caching scenarios.

Fundamental limits of random access communication with retransmissions

We consider a single cell wireless uplink in which randomly arriving devices transmit their payload to a receiver. Given SNR per user, payload size per device, a fixed latency constraint T, total available bandwidth W, i.e., total symbol resources is given by N = TW. The total bandwidth W is evenly partitioned into B bins. Each time slot of duration T is split into a maximum number of retransmission attempts M. Hence, the N resources are partitioned into N/MB resources each bin per retransmission. We characterize the maximum average rate or number of Poisson arrivals that can successfully complete the random access procedure such that the probability of outage is sufficiently small. We analyze the proposed setting for i) noise-limited regime and ii) interference-limited regime. We show that in the noise-limited regime the devices share the resources, and in the interference-limited regime, the resources split such that devices do not experience any interference. We then incorporate Rayleigh fading to model the channel power gain distribution. Although the variability of the channel causes a drop in the number of arrivals that can successfully complete the random access phase, similar scaling results extend to the Rayleigh fading case.

Modeling uplink coverage and rate with aggregation in machine-to-machine communication networks

Machine-to-machine (M2M) communications severe power limitations challenge the interconnectivity, access management, and reliable communication of data. In densely deployed M2M networks, coordinating and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network with truncated power control. We optimize the number of hierarchical stages and perform a coverage probability-based uplink analysis for M2M devices. Our analysis exposes the key tradeoffs between the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate performances of the transmission models, we observe that successive and half-duplex parallel modes have better coverage characteristics compared to full-duplex parallel scheme.