Massive multiple-input multiple-output (MIMO) is a key technology in 5G. It enables multiple users to be served in the same time-frequency block through precoding or beamforming techniques, thus increasing capacity, reliability and energy efficiency. A key issue in massive MIMO is the allocation of power to the individual antennas, in order to achieve a specific objective, e.g., the maximization of the minimum capacity guaranteed to each user. This is a nondeterministic polynomial (NP)-hard problem that needs to be solved in a timely manner since the state of the channels evolves in time and the power allocation should stay in tune with this state. Although several heuristics have been proposed to solve this problem, these entail a considerable time-complexity. As a result, with the present methods, it cannot be guaranteed that power allocation happens in time. To solve this problem, we propose a deep neural network (DNN). A DNN has a low time complexity, but requires an extensive, offline, training process before it becomes operational. The DNN we propose is the combination of two convolutional layers and four fully connected layers. It takes as input the long-term fading information and it outputs the power for each antenna element to each user. We limit ourselves to the case of time-division duplex (TDD) based sub-6GHz networks. Numerical results show that, our DNN-based method approximates very closely the results of a commonly used heuristic based on the bisection algorithm.INDEX TERMS Cell-free massive MIMO, deep learning, power allocation.

In this paper we present a graph-based resource allocation scheme for sidelink broadcast vehicleto-vehicle (V2V) communications. Harnessing available information on the geographical position of vehicles and spectrum resources utilization, eNodeBs are capable of allotting the same set of sidelink resources to several different vehicles in order for them to broadcast their signals. Hence, vehicles sharing the same resources would ideally be in different communications clusters for the interference level-generated due to resource repurposing-to be maintained under control. Within a communications cluster, it is crucial that vehicles transmit in orthogonal time resources to prevent conflicts as vehicleswith half-duplex radio interfaces-cannot transmit and receive simultaneously. In this research, we have envisaged a solution based on a bipartite graph, where vehicles and spectrum resources are represented by vertices whereas the edges represent the achievable rate in each resource based on the signal-to-interference-plus-noise ratio (SINR) that vehicles perceive. The aforementioned constraint on time orthogonality of allocated resources can be approached by aggregating conflicting vertices into macro-vertices which, in addition, narrows the search space yielding a solution with computational complexity equivalent to the conventional graph matching problem. We show mathematically and through simulations that the proposed approach yields an optimal solution. In addition, we provide simulations arXiv:1805.06550v1 [eess.SP]

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Conversely to mainstream cellular networks where uplink / downlink data traffic is centrally managed by eNodeBs, in vehicle-to-vehicle (V2V) broadcast communications mode-3 eNodeBs engage solely in subchannel assignment but ultimately do not intervene in data traffic control. Accordingly, vehicles communicate directly with their counterparts utilizing the allotted subchannels. Due to its loosely controlled one-to-all nature, V2V mode-3 is advantageous for time-critical applications. Nevertheless, it is imperative that the assignment of subchannels is accomplished without conflicts while at the same time satisfying quality of service (QoS) requirements. To the best of our knowledge, there exists no unified framework for V2V mode-3 that contemplates both prevention of allocation conflicts and fulfillment of QoS. Thus, four types of conditions that are of forceful character for attaining QoS-aware conflict-free allocations have been identified: (i) assure differentiated QoS per vehicle, (ii) preclude intra-cluster subframe conflicts, (iii) secure minimal time dispersion of allotted subchannels and (iv) forestall onehop inter-cluster subchannel conflicts. Such conditions have been systematized and merged in an holistic manner allowing non-complex manipulation to perform subchannel allocation optimization. In addition, we propose a surrogate relaxation of the problem that does not affect optimality provided that certain requisites are satisfied.

In Release 14, the 3rd Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) mode-4 as a novel disruptive technology to support sidelink vehicular communications in out-of-coverage scenarios. C-V2X mode-4 has been engineered to operate in a distributed manner, wherein vehicles autonomously monitor the received power across sidelink subchannels before selecting one for utilization. By means of such an strategy, vehicles attempt to (i) discover and (ii) reserve subchannels with low interference that may have the potential to maximize the reception likelihood of their own broadcasted safety messages. However, due to dynamicity of the vehicular environment, the subchannels optimality may fluctuate rapidly over time. As a consequence, vehicles are required to make a new selection every few hundreds of milliseconds. In consonance with 3GPP, the subchannel selection phase relies on the linear average of the perceived power intensities on each of the subchannels during a monitoring window. However, in this paper we propose a nonlinear power averaging phase, where the most up-to-date measurements are assigned higher priority via exponential weighting. We show through simulations that the overall system performance can be leveraged in both urban and freeway scenarios. Furthermore, the linear averaging can be considered as a special case of the exponentially-weighted moving average, ensuring backward compatibility with the standardized method. Finally, the 3GPP mode-4 scheduling approach is described in detail.