946 publications from this institution
This paper presents an unusual view of interference wireless networks based on complex system thinking. To proceed with this analysis, a literature review of the different applications of complex systems is firstly presented to illustrate how such an approach can be used in a wide range of research topics, from economics to linguistics. Then the problem of quantifying the fundamental limits of wireless systems where the co-channel interference is the main limiting factor is described and hence contextualized in the perspective of complex systems. Specifically some possible internal and external pressures that the network elements may suffer are identified as, for example, queue stability, maximum packet loss rate and transmit power constraint. Besides, other important external factors such as mobility and incoming traffic are also pointed out. As a study case, a decentralized point-to-point interference network is described and several claims about the optimal design setting for different network states and under two mobility conditions, namely quasi-static and highly mobile, are stated based on results found in the literature. Using these claims as a background, the design of a robust adaptive algorithm that each network element should run is investigated.
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Non-orthogonal multiple access (NOMA) has been identified as a promising technology for future wireless systems due to its performance gains in spectral efficiency when compared to conventional orthogonal schemes (OMA). This gain can be easily translated to an increasing number of served users, but imposes a challenge in the system reliability which is of vital importance for new services and applications of coming cellular systems. To cope with these issues we propose a NOMA rate control strategy that makes use only of topological characteristics of the scenario and the reliability constraint. We attain the necessary conditions so that NOMA overcomes the OMA alternative, while we discuss the optimum allocation strategies for the 2-user NOMA setup when operating with equal rate or maximum sum-rate goals. In such scenario we show that the user with the largest target error probability times the ratio between the average receive signal power and the average interference power, should be scheduled to be decoded first for optimum performance. We compare numerically the performance of our allocation scheme with its ideal counterpart requiring full CSI at the BSs and infinitely long blocklength, and show how the gap increases as the reliability constraint becomes more stringent. Results also evidence the benefits of NOMA when the co-interference can be efficiently canceled, specially when the goal is to maximize the sum-rate.
Decentralized base station (BS) assignment methods with limited backhaul information exchange are proposed to be combined with decentralized optimal multi-cell minimum power beamforming and Zero-Forcing (ZF) beamforming techniques. The aim of the combined BS assignment and downlink beamforming is to minimize sum power while guaranteeing a minimum SINR per user. A major finding is that a simple ZF beamforming with the optimal decentralized BS assignment, and with limited backhaul information exchange obtains near optimal performance near the cell edge when the user-specific SINR targets are high. Thus, only marginal gains are available from more complex BS assignment and beamforming approaches. However, in the case of low user-specific SINR targets, there is a significant gain available at the cell edge from the optimal beamforming and BS assignment. For example, a simple maximum channel norm selection based BS assignment method with the optimal minimum power beamforming can obtain much of this gain.
A graph neural network (GNN) based access point (AP) selection algorithm for cell-free mas-sive multiple-input multiple-output (MIMO) systems is proposed. Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network. A GNN based on the inductive graph learning framework GraphSAGE is used to obtain the embed-dings which are then used to predict the links between the nodes. The numerical results show that compared to the proximity-based AP selection algorithms, the proposed GNN based algorithm predicts the potential APs with more accuracy. Compared to the large scale fading coefficient based AP selection algorithms, the proposed algorithm does not require measured and sorted signal strengths of all the neighbouring APs. Furthermore, the proposed algorithm is scalable in terms of the number of users in the cell-free system.
Virtualized Radio Access Network (vRAN) is one of the key enablers of future wireless networks as it brings the agility to the radio access network (RAN) architecture and offers degrees of design freedom. Yet, it also creates a challenging problem on how to design the functional split configuration. In this paper, a deep reinforcement learning approach is proposed to optimize function splitting in vRAN. A learning paradigm is developed that optimizes the location of functions in the RAN. These functions can be placed either at a central/cloud unit (CU) or a distributed unit (DU). This problem is formulated as constrained neural combinatorial reinforcement learning to minimize the total network cost. In this solution, a policy gradient method with Lagrangian relaxation is applied that uses a stacked long short-term memory (LSTM) neural network architecture to approximate the policy. Then, a sampling technique with a temperature hyperparameter is applied for the inference process. The results show that our proposed solution can learn the optimal function split decision and solve the problem with a 0.4% optimality gap. Moreover, our method can reduce the cost by up to 320% compared to a distributed-RAN (D-RAN). We also conclude that altering the traffic load and routing cost does not significantly degrade the optimality performance.
5G systems research is getting close to the stage that practical implementations need to be tested in real scenarios. However, the technical requirements for 5G as very stringent and no general purpose equipment exists for testing 5G radio performance in reality yet. One future direction is, however, quite obvious: small cell deployments will play a major role in 5G systems. This paper discusses an initiative started in Oulu, Finland early 2015 to investigate this matter in practice. A special emphasis is put to test novel small cell enabled operator business models.
This paper investigates the effect of feedback delay on the performance of amplify-and-forward (AF) relay selection over a Nakagami- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading environment. Spatial diversity can be improved by employing multiple relays with selection; however, this diversity gain cannot be fully realized when the feedback delay is present in the decision metric. Hence, in this paper, we quantify this detrimental effect by deriving the exact closed-form solution to outage probability, average symbol error rate (SER), and generalized higher moments of signal-to-noise ratio (SNR) for both channel state information (CSI)-assisted and fixed-gain relay. Further, an exact closed form of the ergodic capacity for CSI-assisted relay is derived, and a tight approximation is provided for the fixed-gain relay case. To have a clear picture of the diversity gain variation, we analyze the system in high SNR and obtain simple asymptotic results. Monte Carlo simulation is used to verify the analytical work.
Multicarrier code-division multiple-access (MC-CDMA) is a promising candidate for future wireless communication systems. After the development of a unified signal model that applies to different multirate MC-CDMA schemes (multicode and variable spreading factor (VSF)), a novel space-frequency minimum mean-squared error based hybrid serial/parallel interference cancellation receiver (SF-MMSE/HIC) is developed. The signal processing of this new detector is jointly carried out in space and frequency domain which leads to the advantage of combating the interference resulting from different sources simultaneously. Furthermore, the impact of subcarrier interleaving is investigated for VSF MC-CDMA systems. Based on the characteristic function (CF) of a complex Gaussian random vector (CGRV), a bit error rate (BER) performance analysis method is developed. Several representative simulation examples of the detector are provided to demonstrate the performance of the proposed receiver. Moreover, results indicate that taking into account the subcarrier interleaving gain, VSF MC-CDMA systems can offer similar performance as that of multicode systems.
