A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks

Massive MIMO is a promising technology to connect very large numbers of energy constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes in groups that can communicate simultaneously such that the mutual interference is minimized. We here propose node partitioning strategies that do not require full channel state information, but rather are based on nodes' respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency

Fairness considerations with algorithms for elastic traffic routing, Journal of Telecommunications and Information Technology, 2004, nr 2

The bit rate of modern applications typically varies in time. We consider the traffic elastic if the rate of the sources can be controlled as a function of free resources along the route of that traffic. The objective is to route the demands optimally in sense of increasing the total network throughput while setting the rates of sources in a fair way. We propose a new fairness definition the relative fairness that handles lower and upper bounds on the traffic rate of each source and we compare it with two other known fairness definitions, namely, the max-min and the proportional rate fairness. We propose and compare different routing algorithms, all with three types of fairness definitions. The algorithms are all a tradeoff between network throughput, fairness and computational time

Telecommunications network design and max-min optimization problem, Journal of Telecommunications and Information Technology, 2005, nr 3

Telecommunications networks are facing increasing demand for Internet services. Therefore, the problem of telecommunications network design with the objective to maximize service data flows and provide fair treatment of all services is very up-to-date. In this application, the so-called maxmin fair (MMF) solution concept is widely used to formulate the resource allocation scheme. It assumes that the worst service performance is maximized and the solution is additionally regularized with the lexicographic maximization of the second worst performance, the third one, etc. In this paper we discuss solution algorithms for MMF problems related to telecommunications network design. Due to lexicographic maximization of ordered quantities, theMMF solution concept cannot be tackled by the standard optimization model (mathematical programme). However, one can formulate a sequential lexicographic optimization procedure. The basic procedure is applicable only for convex models, thus it allows to deal with basic design problems but fails if practical discrete restrictions commonly arriving in telecommunications network design are to be taken into account. Then, however, alternative sequential approaches allowing to solve non-convex MMF problems can be used

Path Diversity Protection in Two-Layer Networks, Journal of Telecommunications and Information Technology, 2009, nr 3

The paper addresses an optimization problem related to dimensioning links in a resilient two-layer network. A particular version of the problem which assumes that links of the upper layer are supported by unique paths in the lower layer is considered. Two mixed-integer programming formulations of this problem are presented and discussed. Direct resolving of these formulations requires preselection of “good” candidate paths in the upper layer of the network. Thus, the paper presents an alternative approach which is based on decomposing the resolution process into two phases, resolved iteratively. The first phase subproblem is related to designing lower layer path flows that provide the capacities for thelogical links of the upper layer. The second phase is relatedto designing the flow patterns in the upper layer with protection assured through diversity of paths. In this phase we take into account the failures of the logical links that result from the failures of the lower layer links (so called shared risk link groups)

Min–max optimization of node‐targeted attacks in service networks

peer reviewedThis article considers resilience of service networks that are composed of service and control nodes to node‐targeted attacks. Two complementary problems of selecting attacked nodes and placing control nodes reflect the interaction between the network operator and the network attacker. This interaction can be analyzed within the framework of game theory. Considering the limited performance of the previously introduced iterative solution algorithms based on non‐compact problem models, new compact integer programming formulations of the node attack optimization problem are proposed, which are based on the notion of pseudo‐components and on a bilevel model. The efficiency of the new formulations is illustrated by the numerical study that uses two reference networks (medium‐size and large‐size), and a wide range of the sizes of attacks and controllers placements

Mixture of Tokens: Efficient LLMs through Cross-Example Aggregation

Despite the promise of Mixture of Experts (MoE) models in increasing parameter counts of Transformer models while maintaining training and inference costs, their application carries notable drawbacks. The key strategy of these models is to, for each processed token, activate at most a few experts - subsets of an extensive feed-forward layer. But this approach is not without its challenges. The operation of matching experts and tokens is discrete, which makes MoE models prone to issues like training instability and uneven expert utilization. Existing techniques designed to address these concerns, such as auxiliary losses or balance-aware matching, result either in lower model performance or are more difficult to train. In response to these issues, we propose Mixture of Tokens, a fully-differentiable model that retains the benefits of MoE architectures while avoiding the aforementioned difficulties. Rather than routing tokens to experts, this approach mixes tokens from different examples prior to feeding them to experts, enabling the model to learn from all token-expert combinations. Importantly, this mixing can be disabled to avoid mixing of different sequences during inference. Crucially, this method is fully compatible with both masked and causal Large Language Model training and inference