The increasing traffic demand in cellular networks has recently led to the investigation of new strategies to save precious resources like spectrum and energy. A possible solution employs direct device-to-device (D2D) communications, which is particularly promising when the two terminals involved in the communications are located in close proximity. The D2D communications should coexist with other transmissions, so they must be careful scheduled in order to avoid harmful interference impacts. In this paper, we analyze how a distributed context-awareness, obtained by observing few local channel and topology parameters, can be used to adaptively exploit D2D communications. We develop a rigorous theoretical analysis to quantify the balance between the gain offered by a D2D transmission, and its impact on the other network communications. Based on this analysis, we derive two theorems that define the optimal strategy to be employed, in terms of throughput maximization, when a single or multiple transmit power levels are available for the D2D communications. We compare this strategy to the state-of-the-art in the same network scenario, showing how context awareness can be exploited to achieve a higher sum throughput and an improved fairness.
Conversely, we create a distributed situational awareness by proper observations of some channel parameters, and exploit it through our analytical results to choose when and how a D2D connection can be established. We have already investigated the concept of situational awareness in a multi-cell scenario in @cite_19 . In that work, however, context awareness is based on statistical information, and decisions are taken based on the output of properly designed Bayesian Networks, without seeking to find the optimal solution.
@cite_19
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Device-to-device (D2D) communication is one of the most promising solutions to the dramatic increase of wireless networks traffic load. In D2D communications, mobile nodes can communicate in a semi-autonomous way, with minimal or no control by the base station (BS). In this context, we address the problem of the coexistence of cellular and D2D tiers in the uplink frequencies, where a D2D source is allowed to transmit without direct control of its scheduling by the base station (BS). In order to limit the interference, we add a punishment mechanism triggered by the BS to limit the activity of disturbing terminals. We propose a context- aware channel access mechanism for a D2D source, where the context-awareness is obtained by 1) observing the topology and the wireless transmissions in the proximity of the D2D source, and 2) exploiting the past knowledge learned thanks to a Bayesian network approach. To design the channel access mechanism, we study the tradeoff between maximizing the end-to-end throughput and minimizing the interference to the cellular tier. We then evaluate the performance improvement of the proposed solution, showing the effectiveness of the learning mechanism and the advantages of context awareness. We consider a dynamic multichannel access problem, where multiple correlated channels follow an unknown joint Markov model and users select the channel to transmit data. The objective is to find a policy that maximizes the expected long-term number of successful transmissions. The problem is formulated as a partially observable Markov decision process with unknown system dynamics. To overcome the challenges of unknown dynamics and prohibitive computation, we apply the concept of reinforcement learning and implement a deep Q-network (DQN). We first study the optimal policy for fixed-pattern channel switching with known system dynamics and show through simulations that DQN can achieve the same optimal performance without knowing the system statistics. We then compare the performance of DQN with a Myopic policy and a Whittle Index-based heuristic through both more general simulations as well as real data trace and show that DQN achieves near-optimal performance in more complex situations. Finally, we propose an adaptive DQN approach with the capability to adapt its learning in time-varying scenarios. This paper presents a first fully analytical approach to performance evaluation of D2D communication systems, which does not assume the system to be in saturated conditions. We adopt a Coupled Processors model to describe a cellular scenario with D2D users sharing radio resources with cellular users, i.e., adopting in-band underlay D2D schemes. We derive sufficient conditions for stability of such system, characterizing the effect of D2D transmissions on cellular user performance. Moreover, we present a computationally feasible method for the determination of a proportionally fair allocation of resources. We show that, in non-saturated networks, such an allocation sensibly improves the one derived under the assumption of saturation. Our results show the importance of accurately modelling the interdependence in users performance in the design and evaluation of a D2D cellular system. In device-to-device (D2D) communications, channel state information (CSI) is exploited to manage the interference between D2D users and regular cellular users (CUs) and improve system performance. However, obtaining the accurate CSI is usually difficult and causes high overhead, particularly when the links are not connected to the base station (BS), such as the links between regular CUs and D2D receivers (CU-D links). In this paper, we investigate the signaling overhead and performance tradeoff in D2D communications with channel uncertainty. To limit interference to regular CUs, we only allow the resource of a CU to be reused by, at most, one D2D pair. We also assume that only partial CSI of the CU-D links is available at the BS and develop two different strategies to deal with the channel uncertainty, namely, probabilistic and partial feedback schemes. We first derive a probability-based resource-allocation scheme by utilizing channel statistical characteristics to maximize the overall throughput of the CUs and admissible D2D pairs while guaranteeing their quality of service (QoS) in terms of signal-to-interference-plus-noise ratio (SINR) and outage probability, respectively. Then, we propose an efficient feedback scheme to reduce the overhead of CSI feedback while providing near-optimal performance. In addition, we propose a combined scheme to take advantages of both probabilistic and partial feedback schemes. It is shown by simulation that there exists an optimal threshold of the outage probability for probabilistic scheme while the partial feedback scheme is robust to the channel models. Furthermore, the combined scheme outperforms the probabilistic and the partial feedback schemes in terms of overall throughput.