We consider the distributed resource selection problem in Vehicle-to-vehicle (V2V) communication in the absence of a base station. Each vehicle autonomously selects transmission resources from a pool of shared resources to disseminate Cooperative Awareness Messages (CAMs). This is a consensus problem where each vehicle has to select a unique resource. The problem becomes more challenging when---due to mobility---the number of vehicles in vicinity of each other is changing dynamically. In a congested scenario, allocation of unique resources for each vehicle becomes infeasible and a congested resource allocation strategy has to be developed. The standardized approach in 5G, namely semi-persistent scheduling (SPS) suffers from effects caused by spatial distribution of the vehicles. In our approach, we turn this into an advantage. We propose a novel Distributed Resource Allocation mechanism using multi-agent reinforcement Learning (DIRAL) which builds on a unique state representation. One challenging issue is to cope with the non-stationarity introduced by concurrently learning agents which causes convergence problems in multi-agent learning systems. We aimed to tackle non-stationarity with unique state representation. Specifically, we deploy view-based positional distribution as a state representation to tackle non-stationarity and perform complex joint behavior in a distributed fashion. Our results showed that DIRAL improves PRR by 20% compared to SPS in challenging congested scenarios.

Among the key 5G targets is to offer “ultrareliable” communication services with packet loss rates not exceeding 10-5. It is widely acknowledged that this objective can be achieved only in multi-cellular setups with a special use of multi-connectivity, known as data duplication. This paper examines the use of data duplication at layer 2, i.e., at the Packet Data Convergence Protocol (PDCP) layer of the protocol stack. Unlike many alternative schemes, it does not rely on detailed channel state information or coordination in layer 1, but relies on feedback and coordination at layer 2 only, including a new level of packet acknowledgments. Using system-level simulations the efficiency of the proposed scheme in a real-world environment is illustrated. Besides packet loss rate, the system performance against throughput and delay at the application layer is assessed.

This paper focuses on emulation of the link-level processing for efficient system-level simulations of MIMO-OFDM based mobile communication systems. While the downlink of 3GPP's long term evolution (LTE) and the various MIMO transmission modes used therein stand in the focus of our considerations, the proposed techniques can be applied to other MIMO-OFDM based communication systems such as WiMAX. In particular, we compute effective fading gains and interference levels that allow us to very accurately emulate the signal processing at link-level, i.e. channel en-/decoding, (de-)modulation, layer (de-)mapping, preceding and equalization, with minimal complexity during system-level simulations.

In this paper, the application of combinatorial geometry to noncoherent multiple-symbol differential detection (MSDD) is considered. The resulting algorithm is referred to as CG-MSDD. Analytical expressions for the complexity of CG-MSDD are derived and it is shown that it is polynomial in the length N of the MSDD observation window if the rank of the N times N channel autocorrelation matrix is fixed, but in fact exponential in N if standard fading models are considered. Compared to popular sphere-decoder based MSDD, CG-MSDD is superior (i) in low-signal-to-noise power ratio (SNR) slow-fading channels as its complexity is independent of the SNR, (ii) as its complexity is constant, i.e., independent of the particular channel and noise realization, and (iii) asymptotically, as its complexity exponent only scales linearly with the bandwidth of the fading process.

In this paper, a unified framework for the analysis of differential detection (DD) schemes in time-variant multiple-input multiple-output Rayleigh fading channels is provided. The present results are very general in that they apply to transmission with differential phase-shift keying, unitary differential space-time modulation (DSTM), and block DSTM and reception with conventional DD (CDD), multiple-symbol DD (MSDD), decision-feedback DD (DFDD), and (differentially) coherent detection (CD). New result for general quadratic forms of Gaussian random variables are derived which allows us to obtain elegant closed-form expressions for the pairwise error probabilities (PEPs) of the dominant error events of the considered detectors. Furthermore, it is shown that a unified treatment of all considered detectors is possible with a properly defined effective signal-to-noise ratio (ESNR) and a useful connection between MSDD and minimum-mean-squared-error (MMSE) interpolation is established. Interesting novel results obtained from this analysis include: (i) DSTM constellations designed for CDD and CD are also optimum for MSDD and DFDD; (ii) the error floor entailed by MSDD and DFDD in time-variant fading decreases exponentially with the observation window size N; and (iii) in time-variant fading with effective normalized fading bandwidth Bh,effT MSDD with N → ∞ suffers only from an SNR loss of (1 -2Bh,effT) compared to CD, whereas DFDD suffers from a diversity loss of (1 - 2Bh,effT).

In this paper differential space-frequency frequency division multiplexing (MIMO-OFDM) and multiple-symbol differential detection (MSDD) without channel state information (CSI) at the receiver is considered. Inspired by previous work presented in the literature, a novel DSFM scheme is devised, which makes use of spatial and/or spectral (multipath) diversity and is particularly suited for MIMO-OFDM and power-efficient, low-delay MSDD. Furthermore, the application of a two-dimensional (2D) observation window to MSDD (2D-MSDD) in order to exploit channel correlations in both time and frequency direction, is investigated and tree-search decoding is applied to solve the detection problem efficiently. An analytical approximation of the symbol-error rate of 2D-MSDD for MIMO-OFDM under spatially correlated fading is derived, which enables quick and accurate performance evaluations. Numerical and simulation results corroborate the efficacy of our approach and show that power efficiency close to that of coherent detection with perfect CSI is feasible in all standard fading scenarios at reasonable decoder complexity.

In this paper, the application of combinatorial geometry to noncoherent multiple-symbol differential detection (MSDD) is considered. The resulting algorithm is referred to as CG-MSDD. Analytical expressions for both the complexity and the error-rate performance of CG-MSDD are derived and it is shown that its complexity is polynomial in the length N of the MSDD observation window if the rank of the N times N channel autocorrelation matrix is fixed, but in fact exponential in N if standard fading models are considered. Compared to popular sphere-decoder based MSDD, CG-MSDD is superior (i) in low-signal-to-noise power ratio (SNR) slow-fading channels as its complexity is independent of the SNR, (ii) as its complexity is constant, i.e., independent of the particular channel and noise realization, and (iii) asymptotically, as its complexity exponent only scales linearly with the bandwidth of the fading process.

Differential space-time modulation (DSTM) using unitary-matrix signal constellations is an attractive solution for transmission over multiple-input multiple-output (MIMO) fading channels without requiring channel state information (CSI) at the receiver. To avoid a high error floor for DSTM in relatively fast MIMO fading channels, multiple-symbol differential detection (MSDD) has to be applied at the receiver. MSDD jointly processes blocks of several received matrix-symbols, and power efficiency improves as the blocksize increases. But since the search space of MSDD grows exponentially with the blocksize and also with the number of transmit antennas and the data rate, the complexity of MSDD quickly becomes prohibitive. In this paper, we investigate the application of tree-search algorithms to overcome the complexity limitation of MSDD. We devise a nested MSDD structure consisting of an outer and a number of inner tree-search decoders, which renders MSDD feasible for wide ranges of system parameters. Decoder designs tailored for diagonal and orthogonal DSTM codes are given, and a more power-efficient variant of MSDD, so-called subset MSDD, is proposed. Furthermore, we derive a tight symbol-error rate approximation for MSDD, which lends itself to efficient numerical evaluation. Numerical and simulation results for different DSTM constellations and fading channel scenarios show that the new tree-search MSDD achieves a significantly better performance-complexity tradeoff than benchmark decoders.

Multiple-symbol differential detection (MSDD) for multiple-input-multiple-output Rayleigh-fading channels is considered. MSDD, which jointly processes blocks of N received symbols to detect N-1 data symbols, allows for power-efficient transmission without requiring channel state information at the receiver. In previous work, the authors showed that computational efficient sphere decoding algorithms can be used to accomplish MSDD. In this correspondence, the computational complexity of this sphere-decoding based MSDD is analyzed. In particular, it is proven by means of a lower bound that the complexity of the Fincke-Pohst multiple-symbol differential sphere decoder (FP-MSDSD), while being very low over wide ranges of N and signal-to-noise ratios, is exponential in N in principle. Furthermore, both exact and simple approximate expressions for the complexity of FP-MSDSD are derived, which allow for quick assessment of ranges of useful window sizes N of FP-MSDSD and show that the exponential rate of growth of the complexity of FP-MSDSD is asymptotically equal to that of brute-force MSDD

We consider multiple-symbol differential detection (MSDD) for multiple-input multiple-output Rayleigh-fading channels. MSDD, which jointly processes blocks of N received symbols to detect N - 1 data symbols, allows for power-efficient transmission without requiring channel state information at the receiver. In previous work, we showed that computationally efficient sphere decoding algorithms can be used to accomplish MSDD. In this paper, we analyze the computational complexity of this sphere-decoding based MSDD. In particular, we prove by means of a lower bound that the complexity of the Fincke-Pohst multiple-symbol differential sphere decoder (FP-MSDSD), while being very low over wide ranges of N and signal-to-noise ratios, is exponential in N in principle. We further derive expressions for the exact complete of FP-MSDSD, which allow for quick assessment of ranges of useful window sizes N

The random coding exponent for differential modulation with finite signal sets over block-fading channels and multiple-symbol differential detection with an observation window size of N symbols is considered. Different from and in extension to previous work, the channel coherence interval is independent of N and the case of spatial transmit diversity is included. In this context, we also devise bounds and approximations of the random coding exponent, which allow for efficient numerical evaluation. The presented numerical results provide useful information on the performance of coded differential transmission with short to moderate code lengths

Coded interleaved differential M-ary phase-shift keying (M-DPSK) with iterative decoding, the so-called "Turbo DPSK," is known as a power-efficient transmission format. Due to the rotational invariance of DPSK, it particularly enables detection without channel state information (CSI). However, the soft-input soft-output (SISO) component decoder for DPSK is the computational bottleneck if performance close to the ideal case of perfect CSI is desired. In this paper, we take a fresh look at SISO decoding without CSI and apply sphere decoding (SD) to reduce complexity. In particular, we devise a maximum a posteriori probability (MAP) multiple-symbol differential sphere decoder (MSDSD) which efficiently solves the high-dimensional search problem inherent to detection without CSI. Together with a soft-output generation device the MAP-MSDSD algorithm forms a new SISO-MSDSD module for iterative decoding. We analyze the extrinsic information transfer (EXIT) characteristic of the novel module, by means of which we are able to design powerful encoder and decoder structures. For, respectively, the additive white Gaussian noise (AWGN) and the continuously time-varying Rayleigh-fading channel without CSI these designs operate within 1.7-1.9 and 2.3-2.5 dB of channel capacity assuming perfect CSI. These figures compare favorably with results available in the literature, especially for reasonably high data rates of 1-2 bit/channel use. Simulation studies of the average and the maximum complexity required by SISO-MSDSD demonstrate the advantageous performance versus complexity tradeoff of our approach.

We consider multiple-symbol differential detection (MSDD) for multiple-input multiple-output (MIMO) Rayleigh-fading channels. MSDD, which jointly processes blocks of N received symbols to detect N - 1 data symbols, allows for power-efficient transmission over rapid-fading channels. However, the complexity of the straightforward approach to find the maximum-likelihood (ML) MSDD solution is exponential in N, the number of transmit antennas N/sub T/ and the rate R. In this paper, we introduce an MSDD algorithm based on sphere decoding whose average complexity is not exponential in N for interesting ranges of signal-to-noise ratio (SNR) and arbitrary unitary signal constellations. For the interesting special cases of diagonal and orthogonal constellations we achieve a similar complexity reduction in N/sub T/ and R. Based on an error-rate analysis for MSDD we also propose a variant of MSDD that considerably improves power efficiency in relatively fast fading at a very moderate increase in complexity.

In multiple-symbol differential detection (MSDD) for power-efficient transmission over Rayleigh fading channels without channel state information, blocks of N received symbols are jointly processed to decide on N-1 data symbols. The search space for the maximum-likelihood (ML) estimate is therefore (complex) (N-1)-dimensional, and maximum-likelihood MSDD (ML-MSDD) quickly becomes computationally intractable as N grows. Mackenthun's low-complexity MSDD algorithm finds the ML estimate only for Rayleigh fading channels that are time-invariant over an N symbol period. For the general time-varying fading case, however, low-complexity ML-MSDD is an unsolved problem. In this letter, we solve this problem by applying sphere decoding (SD) to ML-MSDD for time-varying Rayleigh fading channels. The resulting technique is referred to as multiple-symbol differential sphere decoding (MSDSD).

In multiple-symbol differential detection (MSDD) for power-efficient transmission over fading channels without channel state information, blocks of N received symbols are jointly processed to decide on N - 1 data symbols. The search space for the maximum-likelihood (ML) estimate is therefore (complex) (N - 1)-dimensional, and MSDD quickly becomes computationally intractable as N grows. Sphere decoding (SD) is a widely used approach to find the ML estimate in such high-dimensional spaces. In this paper, we devise the application of SD to accomplish MSDD, and we refer to the resulting technique as multiple-symbol differential sphere decoding (MSDSD). We present an efficient algorithm for MSDSD, whose excellent performance versus complexity trade-off is verified by various simulation results and by comparisons with other, suboptimum approaches known from the literature.