This paper proposes a model-based deep learning framework for Reconfigurable Intelligent Surface (RIS)-aided Orthogonal Frequency-Division Multiplexing (OFDM) radar interference mitigation without any assumption on the transmitted waveform. First, a modified Multiple Signal Classification (MUSIC) algorithm generates coarse estimates of target and interferer angles. These estimates feed a lightweight MLP that jointly optimizes RIS amplitude–phase settings via a custom loss balancing SINR enhancement with angle-estimation fidelity. A convolutional notch filtering step then deepens the null at the interference direction, and a subcarrier-pooling strategy fuses per-frequency spectra to ensure uniform suppression across the band. Simulation results under automotive radar conditions demonstrate robust interference mitigation, high angular resolution, and precise angle recovery in dense environments.

This paper introduces an indoor localization method that utilizes fixed reflector objects within the environment, leveraging a base station (BS) or users equipped with Angle of Arrival (AoA) and Time of Arrival (ToA) measurement capabilities. The localization process consists of two phases. In the offline phase, using specific strategy effective reflector points within a specific region are identified. In the online phase, a maximization problem is solved to locate users based on BS measurements and information gathered during the offline phase. Through analysis and simulation results, we demonstrate that with the same number of training points, the performance of the proposed localization technique surpasses that of fingerprint-based techniques. Additionally, we show that localizing an unknown user does not require a large number of training points throughout the entire environment; it is sufficient to place these training points on the boundary of the environment. We introduce the reflectivity parameter ($n_{r}$), which quantifies the average number of first-order reflection paths from the transmitter to the receiver, and demonstrate its impact on localization accuracy. The log-scale accuracy ratio ($R_{a}$) is defined as the logarithmic function of the localization area divided by the localization ambiguity area, serving as an indicator of accuracy. We show that in scenarios where the Signal-to-Noise Ratio (SNR) approaches infinity, and without a line of sight (LoS) link,$R_{a}$is upper-bounded by$n_{r} \log _{2}\left ({{1 + \frac {\mathrm {Vol}({\mathcal {S}}_{A})}{\mathrm {Vol}({\mathcal {S}}_{\epsilon }({\mathcal {M}}_{s}))}}}\right)$, where$\mathrm {Vol}({\mathcal {S}}_{A})$and$\mathrm {Vol}({\mathcal {S}}_{\epsilon }({\mathcal {M}}_{s}))$represent the areas of the localization region and the area containing all reflector points with a probability of at least$1 - \epsilon $, respectively.

This paper proposes an indoor localization strategy that utilizes fixed reflector points. The strategy assumes the presence of a base station (BS) with a known location, equipped with Angle of Arrival (AoA) and Time of Arrival (ToA) measurement capabilities. The localization process comprises two stages. In the initial stage, a systematic environmental scan identifies reflector points, facilitating the creation of a detailed map of their locations. Next, to achieve precise localization, a maximization problem is formulated. For each candidate location, the method assesses the likelihood of a set of measurements to the reflectors map. This approach accelerates user localization compared to fingerprint search algorithms. Simulation results provide compelling evidence for the effectiveness of this strategy in speeding up data gathering in the localization procedure.

We present a new low-complexity interference management algorithm that exploits reconfigurable antennas, removes most of the interference signal power in a$K$-user interference channel, and achieves the promised but unrealized gain of interference alignment in low-to-medium signal-to-noise ratio (SNR) range. The reconfigurable antenna includes several elements, each controlled individually using a radio frequency (RF) switch. The antenna has only one RF chain, resulting in simple hardware design and enabling low-complexity algorithms. We exploit space-time diversity to find two separate combinations of activating or deactivating antenna elements to allow for interference neutralization/elimination. Finding such states is a cumbersome task for which we develop novel, efficient algorithms. We further devise simple encoding and decoding with short precoder length to exploit the benefits of our designs. Our implementation does not rely on channel state information at the transmitters; works at finite signal-to-noise ratios, unlike the typical degrees-of-freedom results; and works in slow-fading environments. We calculate the average achievable rates, the minimum number of elements to reach a specific level of interference suppression, and we provide outage analysis of our technique.

The radiation pattern of transmit antennas varies and fluctuates as receivers change their location, other objects move around, and due to the antenna design itself. In this paper, we demonstrate how this observation can be exploited to align most of the interference signal power and significantly increase the average achievable communication rates. More precisely, in the context of K-user interference channels, we propose a blind interference alignment scheme that combines multi-layer coding at the transmitters and a post-processing methodology at the receivers to align a significant portion of the interference signal power. Our scheme does not rely on any channel state information (CSI), hence the term blind, and only relies on the statistics of the radiation pattern fluctuations. Our proposed communication methodology overcomes some of the barriers in practical implementation of the interference alignment concept. Due to the complexity of the expressions, in this work, we numerically evaluate the achievable rates in different scenarios, demonstrate the gains of our proposed strategy, and compare our results to the prior works with perfect CSI.

The observed coherence times associated with different wireless transmitters at any given user may vary at different rates. We demonstrate how these variations can be exploited for interference management. More precisely, we propose a new opportunistic blind interference alignment (BIA) strategy in the context of K -user interference channels that exploits these variations in coherence times and provides significant data rate gains. We first provide a proof-of-concept setup in which the information about coherence time variations is available non-causally to the transmitters, and we demonstrate how transmitters and receivers can perform pre-coding and post-processing, respectively, to align a considerable part of the interference signal power. We note that in this non-causal scenario, no channel state information is available to the transmitters, and we make no specific assumption on the channel distributions. We take the key ideas of this scenario and consider a K -user interference channel in which the direct links vary at a higher pace compared to the cross links. This assumption is motivated by considering mobile users, or by using our proposed transmit antenna for stationary or low-mobility users. We show how to eliminate the need for non-causal knowledge of coherence time variations, and still provide significant capacity gains.

In this paper, we consider the problem of data transmission for K-user interference channel (IC) with no channel state information (CSI) at transmitters. Each pair of transceivers with the aid of a particular precoder and a switching pattern structure tries to place its desired signal in a subspace which is linearly independent of interference signals. We show that if the channel values are known only to the receivers, the sum degrees-of-freedom (DoF) of the linear interference alignment (IA) with reconfigurable antenna at receivers is Kr/r2-r+K, where r = [√1+4K-1/2]. For previous methods when 3 ≤ K ≤ 104, the number of solvable problems to achieve maximum sum DoF is less than 2% of different cases while in our work there is not any limitation on the number of users. In comparison with the previous related works in our proposed method, there is no limit on the number of users and the reception modes. The result indicates that the maximum sum DoF for the K-user IC with asymptotically large values of K is √K/2. Thus, the sum DoF of such networks using the reconfigurable antenna at receivers grows sublinearly with the number of users, whereas it grows linearly in the case where transmitters have access to CSI.

In this paper, we consider a binary erasure channel (BEC) with an unknown erasure probability of δ at a transmitter. In addition, we consider that δ has a constant value in each transmission block and the transmitter knows the probability distribution of δ. For this problem with the infinite block length, based on the distribution of the random variable δ, a multi-layer encoding strategy at the transmitter and a successive decoding strategy at the receiver are proposed. In our proposed scheme, based on the value of δ, the receiver can decode a part of transmitted data from different transmission layers. In order to have a comparison with other erasure correction codes, we generalize the result of the infinite block length to the finite block-length transmission. We show that considering the average transmission rate, our scheme has a better performance than the other erasure correction codes such as the Raptor and the MDS codes. We also analyze both the decoding complexity and bit error rate versus the erasure probability of the channel. Besides, by using a numerical comparison, we try to show that our proposed strategy outperforms other transmission strategies.